2.5D effects, such as occlusion and perspective foreshortening, enhance visual dynamics and realism by introducing 3D depth cues into 2D designs. However, creating these effects remains challenging, as designers must manually infer and author depth relationships—such as relative ordering, occlusion boundaries, and perspective scaling—within 2D representations. We introduce DepthScape, a human–AI collaborative system that facilitates 2.5D effect creation by placing design elements directly into 3D reconstructions. Using monocular depth reconstruction, DepthScape transforms images into 3D scenes, enabling depth-based blending that produces realistic occlusion and perspective foreshortening. To simplify 3D placement, DepthScape leverages a vision-language model to analyze source images and extract key visual components as parametric anchors, which support direct manipulation editing. The system design was iteratively refined through a formative user study with an early prototype. We evaluate DepthScape through a technical study on 100 professional stock images to assess robustness, alongside an expert evaluation confirming design quality, usefulness, and broad application potential, further illustrated through five example scenarios.

Professional creative software has steep learning curves for novices due to complex interfaces, limited guidance, and unfamiliar terminology. To support educators and tool creators in addressing learner challenges, we introduce TaskLens, an LLM-based method that automatically generates task-conditioned scaffolded UIs from natural language task descriptions. Our method uses LLMs to identify workflow stages and domain concepts, select task-relevant tools, generate implementation code, and execute the code to produce scaffolded interfaces. The interfaces surface relevant tools, organize them by workflow stage, link them to domain concepts, and progressively disclose advanced features. We evaluate TaskLens by deploying two LLM-generated scaffolded interfaces in Blender, a professional 3D modeling software. A user study with beginners (n=32) showed that our scaffolded interfaces significantly reduced perceived task load, improved task performance through embedded workflow guidance, and increased domain concept learning in Blender during task execution. A second study with experts (n=8) showed improved task efficiency and potential to create personalized UIs for productivity and creativity.

Mechanical assembly instructions are commonly delivered through static manuals or fixed-sequence animations, which limit users’ ability to seek clarification, request partial explanations, or adapt guidance to their moment-to-moment needs during physical assembly. We present AssembleIt, an interactive system that generates on-demand 3D assembly animations and verbal explanations directly from natural language user queries, without relying on pre-authored instructional content. AssembleIt automatically derives a part dependency graph from CAD geometry using an Assembly-by-Disassembly strategy and uses this representation to generate query-driven, context-aware animations at runtime, rather than following a single predefined sequence. We evaluate AssembleIt through a controlled user study with 12 participants performing physical assembly tasks using real parts, comparing on-demand, query-driven animations against a static 3D animation baseline. Results indicate that on-demand animation generation supports flexible exploration and targeted clarification during assembly, highlighting the design potential of generative, dependency-driven instructional interfaces for hands-on mechanical tasks.

3D environments offer powerful capabilities for simulating and monitoring complex systems. However, they remain inaccessible to non-expert users who lack specialised training. We present the Layered Customisation System (LCS), a hybrid interface that combines GUI-based direct manipulation with LLM-powered natural language interaction for customising 3D scenes. Through a controlled study with 12 participants, each completing three sessions with different interfaces (GUI-only, LLM-only and hybrid), we investigated how interaction types affect performance, errors and user preferences. Key findings include a reduced learning effect across sessions for users starting with the LLM interface and a trend where participants who first experienced GUI-based interaction showed higher rates of recognising LLM errors later. We contribute empirical evidence of GUI-LLM modality trade-offs in 3D interaction, the selector-layer architecture for hybrid interfaces, and design recommendations, including GUI-first onboarding, enabling prompts as discovery tools and supporting prompt-then-refine workflows.

This research presents MAVE, a framework that combines a multi-agent large language model (LLM) system with an augmented reality (AR) interface to support interactive design and robotic fabrication. While LLM agents are highly flexible, grounding user input in task-relevant digital and physical context remains a key challenge for effective human-machine collaboration. To address this, we introduce five grounding strategies – model, object, spatial, tool, and goal references – that structure how agents interpret and act on user input. We conducted ablation studies to evaluate these strategies, showing that they improve agent execution efficiency and accuracy while reducing the number of interaction turns. We further demonstrate the framework’s adaptability through a design workshop, in which student teams extended a truss design-and-fabrication scenario involving a human and a cobot to prototype diverse interactive workflows. The workshop outcomes highlight the relevance of augmented multi-agent LLM systems for design and fabrication and suggest the framework’s potential to support rapid prototyping of interactive multi-actor workflows.

The cube is an object often used for tangible interaction across many fields of application, notably by manipulating cubes or performing gestures with or on them. Although some system- or designer-defined gestures exist for tangible cubes, user-defined gestures for them remain unexplored. To bridge this gap, we conducted three gesture elicitation studies, each using an identical experimental protocol on a different sample of 30 participants proposing a gesture for 13, 16, and 20 referents controlling a smart home appliance, respectively. Based on the total of 1,440 gesture proposals, we established a classification of user-defined gestures for designing tangible interaction with cubes in smart home control structured into seven major categories, which are further broken down into 55 subcategories. This classification is abstracted from properties such as the number of fingers and hands used to manipulate a cube, the repetition of gestures, their combination, and the time of articulation. A Poisson law estimates the number of participants to be recruited to obtain a desired number of gesture categories.

Amid sociopolitical precarity and denials of social and institutional care, trans people find ways to support each other – named trans care. Alongside nascent HCI work characterising trans care, we conduct focus groups and supplementary participatory speculative design with 17 trans adults in the UK to investigate how trans people use social media to support trans care, and the sociotechnical conditions shaping such care. Based on reflexive thematic analysis and member reflections, we describe how social media support essential community building, identity work, responses to healthcare and legal barriers, everyday care, and resistance work, highlighting sociotechnical tensions animating this care. Simultaneously, we find that algorithmic curation and moderation systematically undermines trans care and exposes trans people to pervasive, yet unpredictable harm. We argue that social media are essential technologies for trans care, yet commercial platforms simultaneously act as precarisation machines, and discuss design orientations for supporting trans people and/alongside trans care.

Sexual and Reproductive Health (SRH) education remains largely limited to awareness-based interventions, particularly in stigmatized socio-cultural contexts. Researchers are extensively focusing on pedagogical theories, reshaping teaching and learning practices to enhance educational outcomes. This paper investigates the application of Bloom’s taxonomy as a design framework for scaffolding SRH learning from foundational knowledge to higher-order cognitive engagement. We conducted a mixed-methods study with 45 participants who engaged in learning activities across different cognitive levels, followed by semi-structured interviews. Our analysis shows that integrating higher-order learning objectives—such as application, analysis, and reflection—supports deeper engagement and understanding of SRH topics beyond surface-level awareness. We also highlight how technologies such as VR and LLMs enable flexible, exploratory learning and create safe, judgment-free spaces for engaging with sensitive topics, supporting users’ comfort. Based on these findings, we derive design implications for developing structured, inclusive, and stigma-sensitive SRH education technologies. This work contributes to HCI by empirically demonstrating how established learning theories can advance learning in sensitive educational domains.

We invite a visual exploration and critique of imagery that can be (re)produced through open-source models available in Civitai — a repository where creators share and monetize custom text-to-image models. The proliferation of these models contributes to the creation and distribution of Non-Consensual Synthetic Intimate Imagery (NCSII), a digitally-mediated form of image-based sexual abuse that primarily affects women and girls, commonly referred to as “deepfake porn.” We examine the practices surrounding the creation and sharing of custom open-source models designed to produce photorealistic content. Through a qualitative content analysis of 510 models and 3800 images, we illustrate the imaginaries about sex, bodies, and gender expressions that models embody and perpetuate, as well as how model creators understand their practices in relation to potential harms and misuse. We discuss pathways for feminist intervention and the care practices we embedded into our research process, and the design of this pictorial.

Data donation has received attention as a more consensual means of collecting personal data for scientific inquiry and AI technology. Yet the nature of data often donated–such as harmful online messages and menstrual tracking logs–carries risk of retraumatization (the forced reliving of traumatic experience). While the well-being of data donors is considered in prior work, approaches to retraumatization remain ad hoc. We present Trauma-Informed Data Donation (TIDD): a context-specific, exploratory design framework for adapting the Trauma-Informed Approach (TIA) from the Public Health domain to data donation. TIDD was the product of a 2-year research through design process with experts on sexual violence and trauma, and observational interviews of data donors. We use a case study applying TIDD to our custom data donation platform, Ube, as an invitation for designers to consider how TIDD could be used as a malleable foundation for donation of data associated with other forms of trauma.

CONTENT WARNING: This paper discusses image-based sexual abuse. Technological advances in Artificial Intelligence (AI) have made it easy to generate and distribute Non-Consensual Synthetic Intimate Imagery (NCSII); images and videos that depict people’s voices, faces, or bodies in intimate or sexually explicit scenarios. The creation and distribution of NCSII is a form of image-based sexual abuse that primarily affects marginalized people. We present Photo BOO-th, an interactive installation that invites attendees to encounter NCSII of themselves, resulting in a creepy, visceral, and affective experience. We designed Photo BOO-th to critique, raise awareness, and foster societal discussions around consent, image-based sexual abuse, and the role of technology in enabling harm. Following a Research through Design approach, we unpack design events that reveal some of the tensions and considerations in designing Photo BOO-th. We conclude with a discussion around designing creepy interactions in sensitive contexts, designing with and against the uncertainties in generative AI, and a reflection on vulnerability when designing creepy interactions.

Commercial Virtual Reality (VR) transforms people’s virtual experiences but introduces deceptive design opportunities that threaten user privacy. Although privacy deceptive patterns on 2D platforms are well-documented, their impacts in VR remain understudied. We surveyed 481 users’ experiences and responses to privacy deceptive patterns across eight commercial VR scenarios. We found that VR deceptive design can exploit both cognitive vulnerabilities and bodily strain, a phenomenon we define as Ergonomic Susceptibility, and that VR’s sensory-rich experiences can make users more likely to accept invasive data disclosure framed as immersion-preserving. Users recognized manipulation but their prior non-VR exposure can foster privacy resignation. Our study shows ergonomics is a critical factor in future privacy-preserving VR design, and urges VR researchers, designers, and policymakers to develop ethical design and privacy management solutions that account for VR’s unique multimodal, immersive, and ergonomic properties, building immersive experiences that respect user privacy and mitigate manipulative data practices.

Blind and Low-vision (BLV) people face challenges in their daily lives, especially regarding independent navigation in unfamiliar settings. Assistive robots have recently emerged as assistive tools for people with special needs. Drones, in particular, have unique advantages over ground robots. Our work aims at designing a drone assistant in addition to primary mobility aids (i.e., white cane) to improve spatial awareness and navigation in unfamiliar settings. Using a Research-through-Design method with 21 BLV users, we identified challenging scenarios and the need for two distinct operating modes (Explorer and Team). We then deployed a low-fidelity prototype in a Wizard-of-Oz study. Participants were enthusiastic about the assistive drone. The outcomes were reviewed by low vision specialists and drone experts to critically assess the strengths and limitations of this assistive tool. Based on these results, we provide recommendations for future design of assistive drones tailored to BLV users.

People with visual impairments (PVI) increasingly adopt conversational AI (CAI) in their daily practices. While much existing HCI research has focused on the technical capabilities of CAI, less has examined the societal contexts in which PVI use CAI, particularly from non-Western perspectives. We conducted a study with 14 participants with visual impairments in South Korea using an audio-based probe featuring imagined dialogues between a blind user and a future CAI. Our findings situate CAI use alongside persistent social barriers such as prejudice and restricted employment opportunities that contribute to a lack of social visibility for PVI. These societal conditions shape not only how CAI is used, but also how the potential benefits and limitations of CAI are experienced. We discuss the need for CAI design within the socio-technical realities of PVI, and conclude by discussing the importance of emphasizing social awareness and empowerment in the development of future CAI systems.

Art has long been a powerful means by which disabled people and other minority group have established counter-narratives, pushing back against damaging stereotypes. These efforts become particularly meaningful when bringing together the work and stories of multiple disabled people, helping to move away from individual, while striving to preserve the uniqueness of everyone’s contribution. In this paper, we document a collective digital art project led by an internationally acclaimed disabled artist involving 16 local disabled people in Japan, featuring a series of workshops where participants created self-portraits using iPads subsequently included in a larger inflatable sculpture. The generated art piece was exhibited to the local community during a series of events organised for the International Day of People with Disability. We reflect on our experiences to suggest key methodological learnings around peer-led collaborative art for counter-narrative creation and examine the role of technology in the context of curation.

Extended reality (XR) technologies offer significant potential to create immersive virtual companionship experiences that support social connection, emotional engagement, and everyday practical needs. However, little is known about how people envision day-to-day interactions with virtual companions in XR environments, raising questions about how they should be designed. To address this gap, we conducted speculative design workshops with 16 participants experienced in AI and XR, generating 16 diverse design concepts spanning varied contexts, use cases and XR-specific affordances. Our analysis reveals key opportunities and emerging challenges in designing virtual companions for immersive environments, demonstrating how they foster meaningful companionship while supporting everyday activities. We further uncover a social tension around companions’ embodied presence and visibility in shared spaces: users often prefer private, controllable interactions (engaging with a companion only they can see), which can conflict with social norms, create ambiguity for bystanders, or result in missing context during co-present interactions.

Humanoid robots are increasingly expected to be designed to interact in diverse social contexts. Yet, recent commercial prototypes often omit expressive communication features, such as faces and gestures. This disconnect raises questions about how communication modalities are actually selected for real-world deployment. In this study, we interviewed 12 industry decision-makers involved in humanoid robot development to examine the factors that guide their design adoption choices. Our findings show that technical feasibility, cost, safety, reliability, and organizational priorities frequently outweigh the benefits of communication modalities. By highlighting where practitioner considerations align with or diverge from established design research, this study offers a grounded, industry-facing perspective on the design of humanoid communication. Interpreting our findings, we present a conceptual framework to guide design researchers in understanding decision-making for product development in industry contexts.

Learning from Demonstration (LfD) allows novice users to teach robots through demonstrations without coding; however, such demonstrations are often suboptimal and can limit robot performance. To better support novices, we investigate the design of a feedback system that enables effective human-robot communication during demonstrations. We first conducted a focus group study (N = 9) to identify effective ways of visualizing key robot information, including joint limits, self-collisions, and manipulability. Guided by these insights, we designed an AR-based real-time feedback system and evaluated it in a between-subjects user study (N = 36) on a 7-DoF collaborative robot. Participants performed two tasks—insertion and pouring—with the second task enabling assessment of participants’ learning across tasks. Results show that real-time feedback reduced demonstration time, increased task completion rate, lowered perceived mental workload, and improved adherence to robot kinematic constraints. These findings demonstrate the effectiveness of the real-time feedback system for intuitive and effective robot teaching.

Population ageing and the growing burden of neurodegenerative diseases call for brain-health interventions that are locally meaningful and socially engaging. Evidence highlights social engagement and cognitive stimulation as preventive practices. Advancing a multidisciplinary approach to this challenge, this pictorial documents the design and pilot deployment of an interactive installation that playfully transforms community storytelling into a tangible, multimodal artefact to (re)activate place attachment and stimulate cognitive health. Focus-group storytelling with older adults and subsequent co-creation activities translated narratives about urban sites into blend drawn reinterpretations. These visual compositions were integrated into a tangible cube puzzle, which returns the collected stories through audio retelling, creating an interactive mnemonic loop. We contribute a replicable workflow of embodied cognitive interventions for translating collective narratives into annotated visualizations and tangible interaction; design lessons on curating borrowed memories; and machine vision and audio rewarding integration for inclusive engagement and cognitive stimulation in public settings.

The ways people remember and recall places reveal an invisible aspect of cultural heritage (CH), reflecting how individuals and communities relate to these places. Heritage is communal, emerging through collaboratively constructed narratives rather than individual records. To probe how people may share collective memories, we designed an immersive two-person workflow for collaboratively co-designing 3D artifacts and environments in virtual heritage locations, using Generative AI (GenAI) to instantiate these intangible memories. Observations of the co-creation process revealed that participants merged prompts and model placements when negotiating different perspectives. They used spatial operations to compose scenes, and also to express personal and embodied experiences of CH. When GenAI failed to meet their needs, participants engaged in creative appropriation, re-purposing unsatisfactory generated objects as sources of design inspiration to further shared narratives. While GenAI may have a homogenizing effect on CH expression, this work shows how people may overcome limitations in immersive collaborative workflows.

Many spatial interaction systems privilege visual representation, leaving other sensory modalities underexplored as design materials. This paper presents an interactive research prototype and its evaluation, examining how sound can support memory evocation, place attachment, and social connection within a virtual representation of a shared space. The prototype combines a navigable three-dimensional environment with location-specific ambient sounds and spoken narratives through a two-stage process. First, participatory workshops were conducted with members of a lab community to collect meaningful sonic memories and associate sounds with the corresponding locations in the virtual environment. Second, the resulting audiovisual prototype was evaluated through a controlled study with two conditions: visual-only interaction and audiovisual interaction. The evaluation combined quantitative measures of the autobiographical memory experience, place attachment, and social connection with qualitative accounts of the user experience. The results show that audiovisual interaction was associated with stronger emotional engagement and perceived connection. Qualitative findings further illustrate how everyday sounds supported a sense of shared presence and continuity across non-overlapping occupants of the same space.

Since the concept of soundscape emerged, researchers have primarily viewed sound as a result of relationships between the surrounding environment and human listeners. However, this relational view tends to frame sound only in relation to human perception, leaving its independent existence underexplored. In this context, this research proposes a new perspective that understands sound as an independent entity existing regardless of sound–human relationships. To explore this notion, we first conducted participatory workshops to collect people’s imaginations of a sonic entity. Based on these insights, we developed a system through which individuals could directly experience an embodiment of such an auditory entity—named Sonic Being. We then employed this system in a 7-day deployment study to investigate how people perceive, understand, and interact with Sonic Being. Our findings reveal that people attribute agency to independent sound, forming an evolving sense of companionship that persists even during periods of silence.

We report on an ultra-long-term deployment of Olo Radio, a slow technology research product designed to support reflective, memory-oriented music listening. Following a single participant across multiple moves and life-stage transitions, we trace how Olo Radio was periodically foregrounded and backgrounded—cared for, reconfigured, and sometimes ignored—as it became woven into domestic life. Over the same period, researcher–participant relations shifted from a conventional study dynamic toward more reciprocal co-inquiry, culminating in the participant independently designing and building a successor device inspired by Olo Radio’s core design qualities. Drawing on this “sample of one,” we show how temporal depth makes visible rhythms, frictions, and forms of continuation that shorter deployments rarely capture. We also articulate methodological and ethical implications of research products that outlive their projects, including stewardship, attachment, and participant-led continuation beyond project time.

This pictorial examines how long-term, thing-centered documentation can function as a practice of noticing in everyday life, where meaning emerges not from individual events but through sustained attention to mundane arrangements, absences, and repetitions, as well as through processes of human–thing co-configuration. Drawing on a seven-year collection of daily breakfast photographs, the work captures presence and absence through simple table settings—plates, cups, and their shifting arrangements. Each image follows a strict rule: one side marks the author’s breakfast, while the other records others, an empty setting, or complete absence. Through repetition, the images form a visual archive in which relationships and temporal rhythms emerge primarily through the images themselves. This pictorial invites designers and researchers to consider how mundane assemblages can be engaged as a method for noticing how memory, routine, and identity are shaped over time.

Early-stage concept envisioning is a critical juncture in AI design, shaping how designers frame problems and the decisions that follow. Yet values and potential harms are often too abstract or addressed too late to meaningfully shape design. Using a Research-through-Design (RtD) approach, we developed the AI Concept Envisioning Toolkit, comprising an AI Capability Library, 24 Value–Harm Cards, and a Value–Tension Map, to support reasoning by juxtaposing values and harms within AI technical capabilities. Through a survey with 30 designers and in-depth interviews with 12 designers, we find that the toolkit is clear and perceived as valuable, and that it encourages value reflection, helps anticipate potential harms, and makes ethical considerations more transparent in early-stage design. We reflect on our design process and discuss design approaches for tools that promote reflection on values and potential harms, surface and navigate value tensions, and introduce productive friction throughout design workflows.

Designing cycling infrastructure requires balancing the competing needs of diverse user groups, yet designers often struggle to anticipate how different cyclists experience the same street environment. We investigate how persona-based evaluation can support cycling infrastructure design by making experiential conflicts explicit during the design process. Informed by a formative study with 12 domain experts and crowdsourced bikeability assessments from 427 cyclists, we present StreetDesignAI, an interactive system that enables designers to (1) ground evaluation in real street context through imagery and map data, (2) receive parallel feedback from simulated cyclist personas spanning confident to cautious users, and (3) iteratively modify designs while the system surfaces conflicts across perspectives. A within-subjects study with 26 transportation professionals comparing StreetDesignAI against a general-purpose AI chatbot demonstrates that structured multi-perspective feedback significantly Broaden designers’ understanding of various cyclists’ perspectives, ability to identify diverse persona needs, and confidence in translating those needs into design decisions. Participants also reported significantly higher overall satisfaction and stronger intention to use the system in professional practice. Qualitative findings further illuminate how explicit conflict surfacing transforms design exploration from single-perspective optimization toward deliberate trade-off reasoning. We discuss implications for AI-assisted tools that scaffold persona-aware design through disagreement as an interaction primitive.

Artificial intelligence (AI) technologies offer tremendous potential for product and service innovation, yet finding good use cases remains challenging. Currently, AI projects largely fail due to breakdowns in early stage ideation and problem formulation. Drawing on HCI research that used AI capabilities and examples to facilitate AI concept ideation, this paper investigates how these approaches might be operationalized in industry settings. We collaborated with cross-functional industry teams in insurance, accounting, and consultancy. We conducted a series of AI Design Sprints, where innovators simultaneously consider AI capabilities and human needs. All teams perceived the ideation method highly valuable both for rapidly exploring use cases and building AI literacy within teams. We detail our process, the challenges, and artifacts that proved effective. We share insights on how AI projects get initiated, and how innovation teams identify use cases. Reflecting on these case studies, we discuss opportunities for improving early stage AI innovation.

Generative AI is rapidly becoming embedded in design tools and workflows, accompanied by promises of increased productivity, reduced cognitive burden, and improved decision-making. These promises shape how AI systems are adopted and evaluated in design practice. However, research on design, automation, and sociotechnical work suggests that labor-saving technologies often reorganize work in unintended ways rather than simply reducing effort. In this paper, I examine AI-assisted design by focusing on how AI reshapes the cognitive, coordinative, and organizational conditions under which design work unfolds. The analysis is organized around myths and ironies of AI-assisted design, treating myths as early abstractions of design work that become institutionalized as technologies are embedded in practice. It identifies six widely articulated myths—including higher-level thinking, explainability, reduced cognitive load, simplified workflows, democratization, and objectivity—and the patterned ironies that follow as omitted cognitive and coordinative work reasserts itself in practice. The paper concludes with implications for AI-assisted design tools, professional practice, and design education.

As AI integrates into design practice, designers increasingly use generative AI tools to envision AI-enabled solutions, positioning AI as both design tool and design material. This dual role creates recursive value tensions distinct from traditional design work. We engaged 18 designers in a concept envisioning activity and interviews to understand how they navigate values and recognize potential harms in this context. Our analysis reveals that (i) designers engage in reciprocal reflection-in-action with AI; (ii) this process surfaces multi-level value tensions across tool, designer, and concept; (iii) designers demonstrate greater attunement to harm recognition as a primary design signal than to articulating positive value fulfillment; and (iv) designers exercise anticipatory judgment through meta-design reasoning about how tool assumptions risk propagating into designed concepts and future use contexts. We extend Schön’s reflection-in-action framework and discuss implications for redesigning AI-mediated design tools, supporting harm-centered reasoning, and positioning design as foundational to AI development.

UX professionals routinely conduct design reviews, yet privacy concerns are often overlooked, not only due to limited tools, but more fundamentally from low intrinsic motivation, driven by limited privacy knowledge, weak empathy for unexpectedly affected users, and low autonomy in identifying harms. We present PrivacyMotiv, an LLM-powered system that generates vulnerability-centered personas, persona journey stories, and traceable design diagnoses grounded in lo-fi user flows to support privacy-oriented UX design review. In a within-subjects study with professional UX practitioners (N=16), PrivacyMotiv significantly improved empathy, intrinsic motivation, and perceived usefulness, with participants identifying 59% more privacy issues and proposing 70% more redesign solutions compared to self-proposed methods. This work contributes empirical insight into motivational barriers in privacy-aware UX and a structured, narrative-driven approach for integrating privacy review into early-stage UX practice.

Although HCI research papers offer valuable design insights, designers often struggle to apply them in design workflows due to difficulties in finding relevant literature, understanding technical jargon, the lack of contextualization, and limited actionability. To address these challenges, we present ReFinE, a Figma plugin that supports real-time design iteration by surfacing contextualized insights from research papers. ReFinE identifies and synthesizes design implications from HCI literature relevant to the mockup’s design context, and tailors this research evidence to a specific design mockup by providing actionable visual guidance on how to update the mockup. To assess the system’s effectiveness, we conducted a technical evaluation and a user study. Results show that ReFinE effectively synthesizes and contextualizes design implications, reducing cognitive load and improving designers’ ability to integrate research evidence into UI mockups. This work contributes to bridging the gap between research and design practice by presenting a tool for embedding scholarly insights into the UI design process.

Sketches are widely used in conceptual design to externalize early ideas and communicate intent. With the rise of generative AI, sketch-to-design workflows have advanced rapidly. However, sketches are limited for organizing component-level structure and intent: parts, functions, and relations are often implicit, making systematic design space exploration difficult. We present SketchConcept, a sketch-to-design system that enables multimodal exploration through sketching and language. It allows designers to sketch out the form, then use voice or text to articulate and refine component functions and structural organization. This enables designers to explore not only satisfying appearances, but also functional and structural alternatives that are essential for design. To support this workflow, SketchConcept introduces a function-to-visual mapping mechanism that connects visual components to functional properties for component-wise iteration. We demonstrate the system through a set of representative use cases and evaluate its efficacy and usability in a two-session user study.

While designers increasingly leverage Generative AI for divergent exploration, current interaction is optimized for convergent refinement, forcing users to specify fixed targets rather than open-ended search spaces. Based on a formative study (N=7), we define the anatomy of Divergent Intent, comprising property, direction, and range, and identified two critical barriers: the lack of mechanisms to explicitly shape the parametric boundaries of exploration and the difficulty of reusing successful search strategies. We present IdeaBlocks, where users can modularize divergent intents into Exploration Blocks. Users can reuse prior intents at multiple levels (block, path, and project) with options for literal or context-adaptive reuse. In our comparative study (N=12), participants using IdeaBlocks explored 2.13 times more images with 12.5% greater visual diversity than the baseline, demonstrating how structured intent expression and reuse support divergent exploration. A three-day longitudinal study (N=6) further revealed how different reuse mechanisms allowed distinct creative strategies, offering design implications for future intent-aware design support tools.

Generative AI often produces results misaligned with user intentions, for example, resolving ambiguous prompts in unexpected ways. Despite existing approaches to clarify intent, a major challenge remains: understanding and influencing AI’s interpretation of user intent through simple, direct inputs requiring no expertise or rigid procedures. We present ToMigo, representing intent as design concept graphs: nodes represent choices of purpose, content, or style, while edges link them with interpretable explanations. Applied to graphic design, ToMigo infers intent from reference images and text. We derived a schema of node types and edges from pre-study data, informing a multimodal large language model to generate graphs aligning nodes externally with user intent and internally toward a unified design goal. This structure enables users to explore AI reasoning and directly manipulate the design concept. In our user studies, ToMigo’s design concept graphs received high alignment ratings and captured most user intentions well. Users reported greater control and found interactive features—editable graphs, reflective chats, concept-design realignment—useful for evolving and realizing their design ideas.

While Large Language Models (LLMs) have significantly advanced design ideation and problem-solving, a fundamental tension persists between the discrete nature of current LLM interactions and the iterative, non-linear essence of design thinking. This “human-LLM misalignment” often forces designers to choose between accepting opaque “black-box” outputs or restarting the generation process entirely, thereby hindering the evolution of design intent and diminishing critical reflection. Based on a formative study with eight designers, we identify three core challenges in achieving reasoning-level alignment and propose a “Curated Reasoning” interaction approach. To instantiate this approach, we developed DesignerlyLoop, a prototype that introduces a nested two-layer diagram structure. This system allows users to externalize their evolving design intent while simultaneously inspecting, reorganizing, and selectively regenerating the underlying LLM reasoning chains. A within-subject user study with 20 designers demonstrated that curated reasoning significantly enhances design intent formulation, and output quality. By shifting the user’s role from a passive recipient of generated content to an active curator of AI reasoning, DesignerlyLoop fosters a more reflective and iterative human-AI collaborative process. Our contributions include: identifying key alignment challenges in creative design; the implementation of a dual-layer structure for reasoning curation; and empirical evidence validating how explicit curated reasoning supports more effective human-LLM alignment in creative tasks.

Catala is a domain-specific programming language for statutory law, featuring prioritized default logic and programming constructs that precisely mirror legal reasoning structures. Extensive study of block-based programming has not clarified how such representations function in semantics-constrained domain-specific languages that require users to directly encode statutory logic. We introduce CatalaBlocks, a block-based representation of Catala that renders its core semantic constructs into a constrained visual form, enabling empirical study of how representation shapes interaction with computationally formalized statutory logic. We conducted a comparative study with legal professionals, examining how participants implemented statutory rules using either textual Catala or CatalaBlocks. Participants using CatalaBlocks completed tasks more quickly, produced more accurate code, reported lower difficulty, and expressed greater confidence in the solutions’ alignment with intended statutory logic. These findings provide insight into how representation shapes domain experts’ interaction with a semantics-first language for statutory law.

We report on a long-term participatory design research project in three social housing apartments that explores how design can extend the catalogue of domestic (energy) technologies beyond dominant industrial premises of scalability and efficiency. This pictorial walks through dioramas of the homes and their idiosyncrasies – constructed from visits, sketches, probes, participant-taken photographs, floor plans, local histories and theories, reflections, and speculations. We argue that homes and households, particularly in contexts that require resourcefulness, act as repositories of alternative potentials. Through a situated spatial analysis, we explore how residents navigate the gaps between standardized architecture and diverse, lived spaces using low and high-tech solutions that work. Four resulting designs collectively sensitize and expand an alternative approach to designing domestic technologies, challenging dominant notions of what counts as novel in the context of the home.

Seemingly simple smart home sensor data, such as temperature or humidity, can lead to conflicts between involved stakeholders, such as users, their partners, or landlords. To understand potential mitigation strategies in product design, we conducted seven scenario-based workshops with 38 tech practitioners and academics. We asked them to prototype smart home device packages that showcase how conflicts caused by simple sensor data could be minimized in product design. The strategies they employed are: 1) reducing data granularity on the visual layer; 2) minimizing data storage; 3) offering more sensor data explainability; 4) increasing trust through strict data regulations. We contribute applicable design strategies for mitigating tensions for simple sensor systems developed by tech experts illustrated with examples.

Rural areas play a critical yet under-examined role in the transition toward more sustainable food environments. As design settings, they are shaped by interconnected barriers of mobility, infrastructure, and social coordination that can be difficult to make sense of and address for HCI research. Building on participatory design, we conducted participatory mapping with members of communities of practice from a rural area in Germany. We used participatory mapping within a future workshop to make food environments and the barriers and enablers shaping them visible and negotiable as design material. Based on our findings, we contribute insights into participatory mapping as a method for documenting local food environments at multiple, intertwined layers, and analyze its potential for creating a shared, situational understanding of how sustainable food practices are enabled and constrained.

This paper positions Black senses of place and the labor of placemaking as central to speculative design practice. We foreground the social, cultural, and historical specificities of place as experienced by Black communities long subjected to racial violence. Drawing from a participatory speculative design project with seniors and youth in Detroit, we show how visions of sociotechnical alternatives emerged from community members’ situated experiences of their neighborhoods and place-based cultural practices. We argue that speculative design must engage placemaking to confront three interrelated forms of nowhereness, namely the rendering of Black life as placeless within racialized spatial orders, the erasure of Black futures through dominant technocratic futurity, and speculation itself operating from and for nowhere, detached from place and accountability. We conceptualize and advocate for a shift from nowhere to now-here within speculative design, offering practical implications for grounding futurity in lived places, relational labor, and Black livingness. This reorients speculative design toward a place-based practice that centers the agency of impacted communities in imagining and enacting sociotechnical worlds otherwise.

Digitalization is for small business growth and resilience and adoption of ICT is limited to digital payments, and uneven and fragmented across business functions (e.g., accounting, inventory, administration). This is true in contexts with resource constraints, limited infrastructure, informal work practices and retrospective digitalization. This raises questions for HCI and ICTD research: how do small businesses expand digitalization beyond initial adoption, what challenges emerge as they sustain and adapt these technologies over time, and how should technologies be designed for small businesses across diverse contexts. To enable designers reason about digitalization solutions, we conducted a field study of small businesses in Kanpur, India. Using the existing LIAISE framework for small business digitalization as an scaffold, we synthesize our findings to propose LIAISE-CAM, an analytical and sensitizing lens for designing digitalization solutions. LIAISE-CAM extends LIAISE to account for retrospective adoption, sustained maintenance, and ongoing use—dimensions often underexplored in existing systems.

Agriculture, the backbone of the Indian economy, provides livelihoods to over 40% of the population. Yet, estimates of national agricultural production and macro-level decisions on crop planning, exports, and government schemes often also rely on data created at desks, alongside scientifically collected data and limited on-ground evidence. We observe that end-user stakeholders often struggle to participate in decision-making. Thus, a divide persists between data collection methods, policymakers, and on-ground lived experiences. To facilitate communication of ground-level information, we propose Participatory Insight, a design research method for grounded, nuanced and potentially scalable stakeholder engagement in policymaking. We instantiate this method through Avani, an AI-supported, audio-based data collection and analysis system designed for the agricultural domain. Avani aims to deliver actionable insights to decision-makers to reduce information gaps. Our main contributions include a generalisable method for participatory policymaking, and a pilot testing its implementation in the context of Indian agriculture.

Ikebana, the Japanese art of flower arrangement, offers practitioners significant mental health benefits, yet daily practice is often limited by a lack of guidance and resources. This work explores how augmented reality (AR) in combination with AI can address these accessibility challenges to support ikebana self-practice. Based on a formative study interviewing 14 students and instructors on their ikebana experiences and challenges, we designed HanaARrange, an AR system providing visual and verbal guidance as users arrange real-life ikebana. In a user study with 9 participants, we found that HanaARrange outperformed traditional self-practice in achieving correct basic ikebana forms, while offering comparable mental well-being benefits. We then conducted iterative refinements with 6 more experienced learners, identifying recursive use patterns and future opportunities. We contribute insights into a design framework for supporting intangible cultural heritage practices, expanding the ikebana ecosystem beyond constraints of physical lessons while supporting mental well-being and life qualities.

As the population of older adults continues to grow, there is increasing need to explore how technology-enhanced recreation can support well-being. While Generative AI (GenAI) shows promise in this area, its role in recreational activities for older adults remains underexplored. We report on a two-year engagement with a suburban Japanese aging community aimed at identifying design considerations for image-based GenAI-supported recreational activities. Through four workshops conducted using a Research-through-Design approach—individual prompting, collective prompting, prompt guessing, and curating prompts—we examined how different forms of participation shaped engagement with GenAI. Our findings suggest that although older adults show clear interest in image-based GenAI, it becomes more meaningful when embedded in culturally recognizable practices and socially mediated infrastructures, rather than framed primarily as an individual creativity or literacy tool. Based on these insights, we discuss implications for integrating GenAI into meaningful community-based recreational activities for older adults.

Playing video games can enhance older adults’ well-being and social connections. However, most mainstream games rely on button-based controls that require fine motor skills, limiting accessibility. We present ElderPlay, a real-time game input translation system that enables older adults to play unmodified commercial games using intuitive, motion-based interaction. We first conducted a gesture elicitation study to derive user-defined gestures grounded in everyday experiences, which informed the design of a proof-of-concept system translating gestures into controller inputs. We then evaluated ElderPlay with two commercial Nintendo Switch games. Results show that gesture-based interaction improves enjoyment, perceived physical engagement, and performance. Rather than replacing controllers, our findings highlight the effectiveness of hybrid interaction, where gesture and controller inputs support different gameplay actions. We discuss implications for context-dependent and inclusive game interaction design.

People commonly manage everyday tasks by registering schedules in digital calendars and setting alarms for important events. In this study, we designed and implemented Prepy, a physical reminder that provides vertical movement–based alerts for upcoming events retrieved from Google Calendar. Prepy allows users to configure the movement interval and supports task initiation by stopping the movement. We conducted a two-week field study with eight participants. The results show that participants tended to adjust and reorganize their schedules in ways that aligned with their perception of the device’s vertical movement and user-configured intervals, adapting them to everyday contexts. Furthermore, participants interpreted Prepy not merely as an alerting device but as a temporal companion that signaled the start of an event and enabled the delegation of everyday tasks. This study presents design opportunities for temporal companion typology that bridge physical reminders and digital calendars to support schedule management in everyday life.

Diminished Reality (DR) modifies or removes elements of the perceived environment. We explored DR for attention support through diary studies, interviews, and speculative co-design with 15 university students with ADHD. Participants described a persistent gap between intention and attention, and the use of externalization strategies that encounter fundamental limits in physical reality. In co-design, rather than reasoning from visual operations, participants articulated which properties of stimuli they needed to perceive versus filter, revealing cases where visual diminishment does not reliably map to attentional diminishment. We contribute rich descriptive data on the attentional challenges and DR preferences of students with ADHD, and propose Attentional Diminishment, a sensitizing concept that reframes DR for attention support from visual outcomes to attentional effects.

Autistic livestreamers use platforms like Twitch for social connection, self-expression, and community, but these spaces also impose ongoing social and emotional demands. Prior work has documented these experiences, but less is known about what autistic creators themselves envision for the tools and platforms they use. We address this gap through a Research through Design (RtD) co-design study with three autistic Twitch streamers, using speculative artefacts as discussion prompts to explore how participants reasoned about potential livestreaming technologies. Across three co-design activities, we identify three overarching tensions shaping autistic streaming practice: Expression versus Misinterpretation and Harm; Public Participation versus Control and Moderation; and Sustainable Practice versus Finite Energy. Our co-designers surfaced constraints, trade-offs, boundaries, and hesitations toward automation, and concerns about managing viewer expectations. This work contributes an account of co-design as a reflective practice with autistic livestreamers, showing how speculative artefacts can surface values, limits, and design reasoning around livestreaming technology.

Contemporary interactive systems often center design around discrete user actions and interface responses. While effective for tasks, this focus can overlook how experience is shaped through perceptual conditions such as light, sound, airflow, temperature, and material presence. This pictorial presents Reduced Perceptual Cues (RPC) as a research-through-design method that shifts attention from interaction events toward the deliberate selection and composition of perceptual cues.

We introduce a compositional approach supported by a card-based design process, computational workflows for perceptual distillation, and simple modular sensory elements. Through a series of workshops, we show how this approach supports diverse experiential configurations across contexts. By foregrounding perceptual cues as design material, this work contributes a transferable method for interaction design beyond event- and interface-centric paradigms.

Smell and temperature are tightly intertwined in everyday experience, yet interactive systems often treat them as independent sensory channels. While thermohaptics and olfactory interfaces have been studied separately, it remains unclear how localized nasal thermal stimulation can modulate olfactory perception without changing chemical concentration. We introduce a nose-mounted thermohaptic interface that delivers precise, closed-loop heating and cooling stimuli to the nasal skin area (nasal sidewalls) to modulate olfactory perception. We evaluate our system in two user studies. In the first psychophysical study, we estimate perceptual thresholds (just-noticeable differences, JNDs) for thermally induced changes in perceived odor intensity across three odors, revealing intensity biasing and asymmetric sensitivity between cooling and heating (mean JND: 4.33°C for cooling; 6.15°C for heating). In the second study, we profile odor quality in a structured multidimensional descriptor space, revealing odor-dependent, dimension-specific semantic shifts rather than uniform changes. Together, these findings suggest that nasal thermal modulation can serve as a rapid, reversible, and non-chemical approach for shaping olfactory experience, expanding the design space for perceptually driven olfactory interfaces in multisensory Human-Computer Interaction (HCI).

Different scent delivery methods may influence how scents are perceived in interactive systems. This study presents PlayScent, a custom-built research prototype comprising our in-house multi-module driver (UniODriver) and an integrated 3D-printed apparatus. Using a single-outlet design, PlayScent enables controlled comparison of atomizer, air pump, heater, and fan-based delivery methods under shared spatial conditions. Through a mixed-methods study, we develop a preliminary perceptual mapping and an initial perception vocabulary for scent delivery methods, relating user descriptions to measured physical parameters and identifying distinct perceptual tendencies across methods. Exploratory expert feedback further reflects on the possible relevance of these findings for design practice. Building on the empirical results, we outline illustrative design scenarios for future exploration. Together, this work offers an early design-oriented account of how delivery methods may be considered alongside scent selection in olfactory interaction design.

Electronic textiles are an expanding field, yet their sustainability challenges demand urgent attention. Recent research highlights the broader inclusion of biomaterials to reduce their environmental impact. This has opened up new functionalities by combining biomaterial properties, textile techniques, and electronics. Nevertheless, an underexplored aspect is their aesthetic dimension, which plays a critical role in the user’s experience with them. To address this gap, we created a collection of five bio-based eTextiles artefacts and introduced them to participants in an aesthetics-focused user study.

Our contribution lies in empirically expanding initial observations by surfacing their aesthetic particularities and proposing design recommendations. We argue that the potential of using biomaterials in eTextiles extends beyond their circular benefits, as they can create multisensory experiences aligned with care, trust, and beauty, while supporting tangible imagination. These qualities could foster meaningful aesthetic interactions with eTextiles, thereby enhancing the use and appreciation of more sustainable eTextile systems.

As interactive technologies shape indoor environments, dominant design approaches prioritise optimisation, efficiency, and instrumental forms of socialisation. In contrast, architectural traditions of biophilic design show how interaction with nature supports emotions, attentional restoration, and physical health. Yet these insights are often overlooked in interactive indoor environments. In this paper, we advance biophilic design as an interaction design lens for technologically mediated indoor environments. Drawing on design sessions with architects, we examine how qualities of natural phenomena, beyond visual greenery, are translated into biophilic interaction indoors. We contribute a biophilic interaction design framework for indoor environments structured through: (1) seven biophilic dimensions that characterise the design space; (2) seven design values that articulate the experiential priorities shaping this space; and (3) six interaction forms that illustrate novel ways occupants interact with indoor environments. Our framework positions biophilic interaction as a multisensory process shaped through relations between people, technologies, and built environments.

The science and technology world is stuck in an unresolved debate: can technologies ever be conscious? While this question dominates discussions in neuropsychology and cognitive science, it is ultimately unproductive for Human-Computer Interaction (HCI). As users perceive a level of consciousness in commercially available systems, there is a need to understand the factors that lead to a perception of consciousness so that we account for consciousness in designing interactive systems. To that end, we systematically develop the Precursors of Perceived Consciousness in Interactive Systems Scale—a validated instrument for quantifying the design qualities that contribute to perceived consciousness in a technology. Through reporting on a structured scale development process, we show how the PreCoS enables studies of technologies potentially perceived as conscious. PreCoS is the first step towards the systematic study and design of systems which responsibly integrate perceived consciousness: fostering positive, engaging experiences while recognising and mitigating ethical risks such as overtrust, bias, or psychological discomfort.

Computational thinking (CT) is increasingly promoted as a core literacy, yet learners and teachers face challenges in connecting abstract program logic to meaningful outcomes. We design and evaluate RoboBlockly Studio, an integrated interactive system that combines block-based programming, a conversational AI teaching agent, and embodied robot execution. RoboBlockly Studio creates a tight iterative loop of authoring, running, observing, and revising. Informed by interviews with five programming teachers, the system was designed to support four goals: (1) preserving learner agency in computational thinking, (2) making program behavior transparent and interpretable, (3) grounding programming in embodied, classroom-aligned tasks, and (4) scaffolding reflection through pedagogically grounded AI dialogue. We deployed RoboBlockly Studio with 32 high school students, observing how robot and AI feedback influenced students’ interactions with code, reflections on problem-solving strategies, and understanding of CT concepts. We discuss design insights and implications for creating interactive, embodied learning environments that integrate AI and robotics to support CT learning in computing education.

Edularp (educational live-action role play) has been used as an educational method in several subjects but not in formal computer science education (CSE) and formal digital literacy education (DLE). This paper answers the question “How can edularp be used in CSE and DLE?” on the basis of a unique mixed-methods approach. We report on, first, our online survey and, second, on our approach of research through co-design, which we carried out together with eleven in-service and pre-service teachers of CSE and DLE. We identified several potential topics and skills that could be worked on through edularps in CSE or DLE. With this we deliver insights for future edularp designers, as well as for education specialists. Furthermore, we describe six edularp prototypes which were one of the results of our research through the co-design process. We also reflect on our research through co-design in regard of empowerment, its benefits and its challenges.

AI literacy is an emerging research area that increasingly incorporates new forms of computational intelligence, creating opportunities to enrich human learning and interactive experiences. Our scoping review examines design interventions and discourses within AI literacy and games to identify and characterize the learning experiences that prior research has sought to support. Drawing from 45 papers, we identified and analyzed 48 unique design artifacts, including game‑based learning prototypes and gamified systems. We constructed a comprehensive matrix charting each artifact’s game or gamification approach, platform, AI literacy focus, game design elements, player requirements, modality, and open‑source availability, providing a detailed view of how AI literacy is represented across these systems. Building on this matrix, we identify nine design suggestions that illustrate how specific design choices shape learners’ knowledge, skills, and experiences with AI. Our work clarifies game‑based AI literacy interventions and offers actionable suggestions for designing future systems that effectively leverage games and gamification to support AI learning.

Art educators are on the front lines of designing a rising generation’s relationship with creativity and media. While public access to generative AI tools has sparked new discussions and practices in creative communities, industries, and higher academia, secondary education art teachers often lack the digital literacy and resources to align emergent technology trends with classroom goals. We interviewed 19 art educators across the United States, surfacing 1) constraints around technological access, guidance, and agency; 2) five core values for art education; and 3) the social-emotional labour educators perform to create classroom communities. Lensed by educator needs and the sociotechnical contexts they teach within, we call for researchers to critically consider how art education technology can motivate creative thinking, enable peer teaching among students, complement analogue art-making, and prioritise joy over results to better attend to classroom needs.

Learning sciences research shows that intuitive physics knowledge productively supports disciplinary learning through the Knowledge in Pieces (KiP) framework, which treats learners’ everyday intuitions as productive resources. We argue that similar parallels exist in HCI education, where students’ intuitive design knowledge can ground the development of interaction design expertise. Students live in a world saturated with professionally designed artifacts and routinely engage in “everyday design”, cultivating tacit knowledge about interfaces long before formal HCI instruction. This mirrors how intuitive physics knowledge develops through daily physical experience. To support this argument, we analyze first-week assignment submissions from an introductory HCI course in which students critiqued interactive systems. Our findings show that students enter HCI instruction with rich, nuanced intuitions about interface evaluation. We therefore suggest that (1) students’ prior intuitive knowledge is a valuable pedagogical resource for HCI educators, and (2) operationalizing a KiP approach can advance future HCI education research.

A solid understanding of VR optics is highly valuable for designing usable and innovative immersive experiences, yet the underlying principles remain challenging for students due to their abstract, interdisciplinary nature. Conventional lecture materials often struggle to convey how parameters such as focal length and lens placement influence image perception. We built an accessible web-based HMD Simulator that lets users manipulate parameters and observe real-time effects on stereo image formation and frustum geometry. Using the simulator as a design probe, we conducted an exploratory study (n = 16) with staff and students to compare experiences against conventional learning materials. Thematic analysis revealed that participants perceived interactivity as stimulating curiosity to tinker, while real-time feedback was reported to reduce perceived cognitive effort, facilitating deeper inquiry. Participants further emphasized how the simulator complemented rather than replaced conventional instruction, highlighting guided integration features as priority. These findings inform initial design considerations for interactive tools in VR optics and other domains with complex hardware-software interactions.

Knowledge workers increasingly rely on multi-agent systems to solve complex problems. While these systems offer valuable support, they often obscure which agents contributed to a response, leading to a lack of transparency that may result in errors and reduced trust. To address this, we propose avatars that make agents’ expertise and contributions transparent. We iteratively co-designed avatars representing distinct expertise areas and validated them in an experiment (N=100). Building on this, we developed four multi-agent prototypes varying in explanation modality (text vs. avatars) and resolution (low vs. high). We then conducted a mixed-methods evaluation with an online experiment (N=124) and follow-up interviews (N=20). Qualitative results suggest that avatars foster clearer mental models, improve perceived explainability, and support users’ trust calibration without increasing cognitive load, although no significant quantitative differences were found. Our research contributes validated avatar designs, insights into explanation strategies, and design implications for explainable multi-agent systems.

Explanations are often expected to support decision-making in human–AI interaction, yet most research evaluates their effectiveness through performance-oriented outcomes such as accuracy and reliance. These evaluations implicitly assume that users will engage with explanations once they are available. However, little is known about when users actually want explanations. To address this, we investigate explanation demand through a mixed-methods study in which 280 participants completed an AI-supported decision-making task under varying explanation availability and incentive conditions.

Results show that explanation demand increases with higher perceived task difficulty and lower decision confidence, while monetary incentives reduce users’ tendency to request explanations. Qualitative findings reveal diverse motivations for wanting explanations, including learning, uncertainty, and curiosity. Across conditions, explanation availability did not meaningfully affect decision accuracy or reliance. These findings indicate that explanation demand is selective and goal-driven, with direct implications for the design and evaluation of explanations in human–AI interaction.

What would it mean for a digital assistant to stay with our cursor, rather than as a chatbot in a separate window? By staying near the user’s actions, such assistance promises lightweight, continuous, in-the-flow help, but also introduces unique risks of intrusion. However, little is known about the tasks it is suited for, and the design considerations it entails. This study offers an initial investigation of this space through a multi-stage design inquiry. First, a retrospective think-aloud study with nine participants reveals common inefficiencies in everyday information work where cursor-centric support may be valuable. Building on these observations, we proposed the Fairy Cursor as a design probe and developed a proof-of-concept environment to examine users’ interpretations, preferences and concerns around it. Our findings surfaced key challenges and opportunities in designing cursor-centric assistants, including how users tolerate presence, interpret initiatives, and balance assistance with ongoing engagement. We conclude with design implications for in-the-flow human–AI collaboration and outline directions for future systems.

Exploring databases remains cognitively demanding for non-experts. While natural language interfaces offer flexibility, they place the full burden of articulation and refinement on the user, hindering exploratory discovery. We identify a core interaction design problem: how to dynamically support users’ evolving understanding during query formulation. We propose and implement a paradigm of adaptive mixed-initiative interaction, where the system interprets user behavioral cues (e.g., tentativeness, focus shifts) to infer intent and dynamically adapts its support strategies. This involves switching between responsive and proactive modes, and integrating textual responses with graphical previews to scaffold the query-building process. We instantiate this paradigm in a functional prototype. A controlled user study demonstrates that this adaptive approach not only improves task efficiency and usability but, more importantly, reduces cognitive load and fosters a more exploratory, less formulaic query-building process compared to traditional reactive interfaces.

As agentic AI systems grow increasingly capable of operating for hours or days at a time, users’ prompts are transforming into highly elaborate specifications for the AI to autonomously work on. While prompting for bounded, single-turn tasks has been extensively studied, less is known about how people communicate specifications for long-horizon tasks. In this work, we conducted a qualitative study in which 16 professionals drafted specifications for both a human colleague and an AI, revealing a core divergence: participants treated human delegation as a “compass,” offering high-level intent to encourage flexible exploration. In contrast, communication with AI resembled painstakingly laying down “railway tracks”: rigid, exhaustive instructions to minimize ambiguity and deviation. This reflected a perception that current AI struggles to infer intent, prioritize, and make judgments on its own. When envisioning an ideal AI collaborator, users expressed a desire for a hybrid : a collaborator blending AI’s efficiency and large context window with the critical thinking and agency of a human colleague. We discuss design implications for future AI systems, proposing that they align on outcomes through generated rough drafts, verify feasibility via end-to-end “test runs,” and monitor execution through intelligent check-ins—ultimately transforming AI from a passive instruction-follower into a reliable collaborator for ambiguous, long-horizon tasks.

Generative AI systems optimized for efficient artifact production can inadvertently bypass the cognitive labor that gives knowledge work its value. Through a Research through Design study in corporate goal-setting—a reflective practice increasingly hollowed by bureaucratic pressures—we propose and evaluate a design principle: cognitive outcomes first, material artifacts second. We operationalize this through a prototype using cognitive scaffolds that require users to articulate contextual nuances, constraints, and strategic realities before generating artifacts. Evaluation with 16 employees revealed that 75% recognized persistent questioning as productive rigor yielding “cognitive delta” (new insight). A five-month follow-up confirmed durability of cognitive outcomes beyond the study context. We contribute: (1) Cognitive outcomes first, a principle establishing cognitive labor as the primary product and artifacts as proof of work; (2) Instrumental drift, an analytic lens revealing how efficiency-optimized AI decouples instruments from purposes; and (3) empirical demonstration that persistent inquiry succeeds when designed for cognitive engagement, with users recognizing epistemic value despite interaction costs.

Volunteered geographic information (VGI) platforms often suffer from redundant submissions, as contributors tend to create new reports rather than update existing ones—partly due to the effort required to locate and examine relevant prior entries. This study introduces Tell2Find, a large language model (LLM)-powered agent designed to support report retrieval and reuse during mobile crowdsourcing tasks. We conducted a study comparing Tell2Find with a traditional filter-based tool and a hybrid version combining both. Users with access to Tell2Find were more likely to examine existing reports before acting, resulting in more updates to prior reports and lower redundancy. Qualitatively, participants highlighted Tell2Find’s advantages in reducing the effort of matching reports, improving convenience in mobile contexts, and encouraging engagement with prior data due to more relevant suggestions. However, we also observed friction points: user trust around the system’s opaque matching logic and uncertainty about input phrasing occasionally led to disengagement with Tell2Find.

News events often generate fragmented coverage across a flood of articles, making it difficult for readers to discern what happened, what issues are at stake, and how different stakeholders are responding. While large language models (LLMs) are widely used to summarize articles, their use in synthesizing coherent digests from multiple sources about the same event remains underexplored. We present a study examining how different LLM prompting strategies—core issue, standpoints, and sub-events—shape readers’ perceptions of credibility, readability, and completeness. Based on a user evaluation (n=80) and follow-up interviews, we find that while standpoint digests improve perceived credibility, trust extends beyond structure. Sub-event digests support understanding of event development, and core-issue digests are often seen as vague. Readers also preferred timeline-based framing for ongoing events and concise, outcome-focused summaries for concluded ones. Consequently, we propose design principles emphasizing source transparency, verifiable evidence, visual scaffolding, and epistemic humility to foster trust.

Benchmarks play a significant role in how technology companies communicate about model capabilities and how researchers and the public understand generative AI systems. However, existing benchmarks have been criticized for their failure to adequately capture real-world usages (i.e. ecological validity) or to measure underlying concepts (i.e. construct validity). Building on approaches in HCI, we adopt a human-centered design process to address such critiques. Working within the journalism domain we engaged 23 professionals in a workshop which informed the design of a domain-oriented evaluation “cookbook”. Our workshop findings surface domain-specific challenges and tensions faced by designers in translating specific tasks to evaluation constructs, aligning metrics with domain-specific values, and balancing needs among different stakeholders when constructing evaluations. Through an instantiation of design-based approaches for benchmark creation in the journalism domain, this work not only produces an evaluation structure for journalism practitioners to experiment with, but also lays out design requirements for AI evaluations that are contextualized, value-aligned, and cultivate evaluative literacy for domain end-users.

Local journalism plays a vital role in community life by connecting citizens to local news and supporting public discussion. However, the global spread of Local News Desertification—the decline of reliable local media—has weakened civic deliberation, shared knowledge, and community attachment. Citizen-Participatory Journalism enables citizens to discover and share local news. Still, their participatory agency in reporting local news that matters to their lives is controlled by the journalists’ normative authority. This pictorial frames this tension as a Participatory Design (PD) problem. Through a multi-stage PD process, we propose a citizen–AI participatory journalism platform that enables citizens to actively report local news. The platform combines AI-assisted news article creation with a multi-actor review structure to support responsible reporting. Based on this platform and its underlying model, this pictorial contributes a framework for Community News Resilience, which aims to democratize local information ecosystems through human–AI collaboration in future participatory journalism.

People in informal e-markets often try to deal with fraud and financial harm by sharing posts, screenshots, and warnings in social media groups. However, buyers and sellers frequently face further problems because these reports are scattered, hard to verify, and rarely lead to resolution. We studied these issues through a survey with 124 participants and interviews with 36 buyers, sellers, and related stakeholders from Bangladesh and designed Bonik Somiti, a socio-technical system that supports structured reporting, admin-led mediation, and accountability in informal e-markets. Our evaluation with 32 participants revealed several challenges in managing fraud, resolving disputes, and building trust within existing informal practices and the assumptions behind them. Based on these findings, we further discuss how community-centered technologies can be designed to support safer and more accountable informal e-markets in the Global South.

Formal regulation designed to protect workers is often opaque, with narrow definitions of injury. This means that even when workers report harm, regulators fail to intervene on cumulative, chronic, and collective experiences. Through interviews and co-design workshops with unionized cleaning workers, we explore alternative forms of documentation that support collective action rather than institutional proof. Our study illuminates four interlocking worker-led documentation practices: (1) Surfacing experiences of working conditions, (2) Collectively making sense of existing reporting avenues, (3) Negotiating with management and publics, and (4) Building trust and solidarity within the union. For each of these practices, we offer concrete design concepts developed in collaboration with workers. Finally, we contribute conceptual knowledge on how engaging in the process of documentation functions as an engine of solidarity-building, a prerequisite for addressing workplace harms beyond disparate data points.

Although embodied AI systems are increasingly developed and tested in public environments, their influence on the design of products for older adults remains underexplored. We examine how different types of embodiment, or form factor, shape initial encounters between humans and AI when conversational abilities are comparable. Building on prior work, we conducted a two-day field study at a senior citizens’ fair in Germany (n = 25, age 24–90), where participants interacted with the screen-based avatar Ann-Sophie and the humanoid robot Ameca. Both used LLM-based dialogue systems, enabling a focus on embodied interaction rather than linguistic performance. Qualitative results show contrasting affective and social responses: Ameca’s physical presence and gaze dynamics were described as both fascinating and unsettling, while Ann-Sophie was perceived as warm but socially limited. These findings highlight the role of embodiment in shaping comfort, expectations, and ethical awareness in public human–AI interaction.

Explanations play a central role in shaping users’ trust and acceptance of autonomous vehicles (AVs). While existing AV explanation research has emphasized cognitive elements such as content, timing, and presentation fidelity, it offers limited guidance on how explanations might incorporate affective elements or adjust to varying driving contexts. To address this gap, we introduce a stance-strategy-tone framework for designing affective explanations, supported by scenario-specific guidelines and illustrative example utterances. Through interviews with seven domain experts and six co-design workshops involving 27 prospective AV users, we identified the components that influence how affective explanations are constructed and mapped them onto key driving scenarios. Our findings reveal design opportunities such as tailoring emotional framing to situational demands, combining empathy with informational clarity, and calibrating tone to balance warmth with directive precision. The study provides practical guidance for creating emotionally responsive explanation systems for AVs.

Autonomous vehicles (AVs) can enhance older adults’ mobility and independence, but also present significant safety and usability challenges. In a two-phase formative study, we examine older adults’ (aged 60+) unique design requirements in AVs and the potential of a conversational AI agent inside AVs to support as digital chauffeurs. Phase 1 focus group (N=10) findings present physical AV design with accessible interfaces. We conducted an interview study (N=18) in phase 2 using a virtual reality (VR) simulator and a large-language model (LLM)-powered chatbot. Findings reveal that older adults value conversational agents that could assist during emergencies, initiate social conversations and provide a sense of control and agency. Informed by the outcome, we discuss design implications, emphasizing the need for transparency, clear communication, and chauffeur-like interaction norms in AI agents with appropriate anthropomorphic characteristics (i.e., pacing, tone, and frequency) to ensure a comfortable riding experience for older adults.

Caring for a loved one with dementia is emotionally and physically demanding. As such, extensive HCI scholarship has focused on designing technologies to support caregivers directly. Far less attention, however, has been given to the human infrastructure that helps support dementia caregivers as they navigate ongoing changes associated with the disease. This study reveals the work of dementia support group facilitators, who position themselves as “caregivers of caregivers”. Through interviews and focus groups with 15 facilitators, we identify four roles facilitators embody and thus constitute critical human infrastructure within the dementia care ecosystem. Our analysis opens up the design space for dementia caregiving by taking a broader view of how technology could better support those who care for caregivers. We highlight design opportunities and boundaries for AI technologies, that may alleviate administrative and informational burdens while preserving the relational care central to facilitation. We argue that designing for facilitators can support safety, continuity, and emotional support throughout the caregiving journey.

Older adults have increasingly turned to conversational AI as a source of emotional support. However, little is known about how emotionally supportive interactions are experienced in everyday use, particularly when AI systems limit, redirect, or intervene during these interactions. We interviewed 18 older adults about their experiences using conversational AI for emotional support, examining when they turn to AI, how they engage during emotionally vulnerable moments, and how they respond when support feels disrupted. Our findings show that older adults often rely on AI when other forms of social support feel inaccessible. However, current safety-related interventions can redirect interactions in ways that participants experience as interruptions to emotional engagement or as shifts in control away from them. Such disruptions can undermine older adults’ ability to remain emotionally engaged and, in some cases, contribute to emotional distress. We discussed design implications for emotionally supportive conversational AI, emphasizing the need for safety interventions that are enacted within older adults’ social contexts, align with users’ emotional pacing, and preserve their sense of agency.

Photorealistic self-avatars with facial tracking bring VR communication closer to face-to-face interaction, but also expose a fundamental limitation: people often lack accurate awareness and control of their own facial expressions. As a result, directly mirroring facial movements may not reliably convey users’ intended expressions in social VR. We propose modulating rendered expressions on avatars to improve social interaction in VR (e.g., exaggerating positive ones). To design effective modulation strategies and understand how modulation impacts user behavior and perception, we invited 18 participants to perform emotional expressions under varying modulation levels. We collected their self-reported ratings on accuracy and social appropriateness, as well as naturalness ratings and emotion recognition results from 18 additional observers who viewed the expressions. Results show that suppressing rendered expressions amplified participants’ actual facial movements, with effects differing by expression type. Based on our findings, we calculated the modulation ranges for different expressions and developed a real-time modulation system that exaggerates users’ facial expressions with a mild amplitude. We evaluated the system through a VR speech task where 12 participants delivered speeches with and without modulation. Results showed that the applied modulation improved emotional expressiveness and performance. Finally, we developed three application prototypes to illustrate the broader practical implications of our findings.

We devote little to designing that resonates with our capacity for sensing in all the manners known and yet to be known – a capacity that allows us to perceive beyond the self. In this research, we approach tactility as liminality in which we notice our entanglement with the landscape. To this end, we conducted field research with rocks across Lake Ohrid and Galichica Mountain in North Macedonia. The gathered experience was assessed in a post-field autoethnography, yielding insights that inform the prospective design of an interactive book on the tactile knowledge of entangled existences. Across tactile interactions, drawings, notes, and reflexivity, we traced situated and local ways of knowing-with, which led us to recall belonging with the landscape as kinship, as accountability for anthropocentrism, and as co-crafting with rocks, water, lichen, moss, fish, shell, algae, and the matter of all possible others.

In recent years, interest in outdoor recreation activities has ballooned in popularity. In parallel, online discourse around how to “prepare” to enter the outdoors—e.g., buying the “correct” gear, finding scenic trails to hike, etiquette on the trail itself—has also proliferated. Dominant in this discourse is a view of nature as something wholly separate from humans. Building on interest in “disorientation” as a methodological strategy, we examine, through retrospective autoethnography, how hiking and backpacking trigger extreme oscillations between the human-centered and more-than-human. We found that while many aspects of hiking have been molded into a human-centered framing—including not only technology and information, but also human-built waymarks and the trail itself—the vastness of landscapes, intense forces of weather, and fragility of our bodies force us to notice the more-than-human. We reflect on how consecutive hikes can represent a practice of iterative physical disorientation, continually revealing new ways to embrace the more-than-human.

We propose Awareness-Attention-Connection (AAC) as a design framework for turning micro-mobility guidance into placemaking-in-motion, balancing narrative engagement with rider safety. Rather than treating e-bike routes as neutral corridors between landmarks, AAC choreographs when riders are gently alerted to a place, when their attention is drawn in, and when deeper connection is invited. This pictorial presents two deployments using audio-first, visually lightweight guidance to surface hidden histories, ecological transformations, and everyday atmospheres along urban and waterfront paths. Key contributions include: (1) a three-stage framework (AAC) with corresponding design strategies for structuring in-motion encounters with place; (2) empirical insights on how riders experience AAC in practice; and (3) a Perceptual Rhythm Model describing how distance, timing, and narrative depth can be co-designed as an experiential rhythm to reframe place impressions for mobility-first interaction design.

Human traces scattered across urban landscapes can signify our everyday lives and societal vibrancy in subtle and poetic forms. In this paper, we explore how designed technology can engage biodata as evocative traces. To this end, we present the design, implementation, and evaluation of HeartSway , an interactive hammock that captures a user’s heart rate and micro-movements as traces and replays them as an embodied experience for the next visitor. Through a qualitative field study (N=10), we find that HeartSway evokes feelings of connection, curiosity about prior users, and appreciation for shared human vitality. Our work contributes to understanding anonymous archival biodata as a design material for experiential urban traces. We offer design considerations for intimate asynchronous encounters between strangers in public spaces and for reimagining public amenities.

We examine portrayals of active and bicycle mobility in speculative fiction—highlighting the diverse ways in which authors have envisioned the intersection of interactive technologies and mobility tools, as well as how they diverge from contemporary bikeHCI research. The adoption of active mobility has deep health and sustainability implications, however the integration of novel interactive systems with both new and existing transportation modalities raises complex social and technical concerns. Speculative fiction provides rich and contrasting visions of the future of active mobility, while also capturing the zeitgeist around the collision of digital technologies with traditionally-analog forms of transport. We analyzed 106 text-based fictions, coding their key mobility-related technologies, data, interactions, and themes. We also examine how speculative futures in our corpus (which tend to center feminist and queer perspectives) relate to current trends in bikeHCI research and discuss potential new directions for active mobility tech.

Generative AI (GAI) is increasingly applied in playful placemaking to connect people with urban spaces. However, lacking a situated understanding of places, GAI could act as a tourist who introduces bias, misinformation, and cultural insensitivity. Addressing this challenge, we introduce flânerie as a metaphor that inspires creative yet critical design practice. We distill flânerie features, including embracing curiosity and serendipity, tracing, merging fragments, plunging and detaching, and (de)familiarizing, and translate them into design course activities implemented in the Netherlands and Taiwan, involving in-situ exploration with AI and design proposal development. Findings show how the flânerie approach supports students in recognizing GAI’s tourist-like playfulness and offensiveness situated in city spaces, alongside fostering more responsible designs and attitudes. This work contributes a metaphorical approach to harmonizing human-AI interaction for placemaking that balances creativity and reflectivity. We also offer new perspectives on human-AI-place relationships through the lens of flâneur and turtle-on-a-leash.

In schools, students are expected to perform obedience through routines which many find arbitrary and oppressive. Some students have sought an escape from these demands via virtual and hybrid schools. Drawing on ethnographic research, this work contributes understanding of how design can minimize control of diverse embodied experiences through emphasis on standardized outputs. Such a strategy lessens demands for assimilation, increasing a sense of freedom experienced by those who fall outside normative social categories. Yet, it also fails to fundamentally challenge underlying oppressive dynamics. This article weighs the harms and benefits of this translational approach for those seeking to navigate hostile institutions, arguing for designs that preserve autonomy but also actively offer support.

Digital platforms frequently reproduce heteronormative norms and structural biases, limiting inclusive communication between LGBTQ+ and cisgender individuals. The Metaverse, with its affordances for identity fluidity, presence, and community governance, offers a promising site for reimagining such interactions. To investigate this potential, we conducted participatory design workshops involving LGBTQ+ and cisgender participants, situating them in speculative Metaverse contexts to surface barriers and co-create alternative futures. The workshops followed a three-phase process—identifying challenges, speculative problem-solving, and visualizing futures—yielding socio-spatial-technical solutions across four layers: embodied interaction, negotiated visibility, community formation, and reconfigured norms. These findings highlight the importance of spatial cues and power dynamics in shaping digital encounters. We contribute by (1) articulating challenges of cross-group communication in virtual environments, (2) proposing inclusive design opportunities for the Metaverse, and (3) advancing principles for addressing power geometry in digital space. This work demonstrates futuring as a critical strategy for designing equitable, transformative communication infrastructures.

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As AI and data systems often falter when encountering queer identities and knowledge, reinforcing existing oppressions, queer people have resisted such systems and their normalizing tendencies. This pictorial explores tactics of queering AI through a collaborative zine-making project (i.e. zineography) that challenges generative AI and data systems. We share how we workshopped and materialized queering tactics in zine spreads; analyzed these spreads according to materials, content, and tone; and visualized our analysis as thematic collages. We contribute: (1) tangible characteristics of queering AI and data systems (i.e. materials, tones, and aesthetics); and (2) design opportunities for using zineographies as a radical method for building and collectively sharing knowledge about a marginalized community, including recommendations for enacting queer zineographies. By materializing queering tactics through zine-making, we invite embodied, action-oriented critiques that question dominant techno-solutionist movements and trace queer possibilities outside of their normalizing narratives.

This pictorial examines Beyoncé’s Renaissance World Tour and Janelle Monáe’s Dirty Computer as sites of sonic speculation—designed futures centering Blackness, technology, joy, and resistance through multisensory orchestration. We introduce sonic speculation as a holistic approach where sound, visual design, embodied performance, and technological intervention constitute experiential futures. Through close analysis, we trace how these performances integrate five design dimensions: sonic architectures, visual metaphors of transformation, interactivity and collective embodiment, fashion as technology, and futurist aesthetics. These dimensions address three Afrofuturist concerns: political resistance, Black embodiment, and technology’s relationship to Black existence. For interaction designers and HCI scholars, these performances offer insights into multisensory, culturally-grounded speculative design. We demonstrate how large-scale performance design can model interactive systems honoring marginalized communities’ histories while imagining liberatory futures, challenging the field to learn from Black femme artists as design theorists whose practices reveal possibilities current scholarship has yet to theorize.

Algorithmic systems embedded in everyday collaboration platforms increasingly shape the way workers form impressions about each other. As they infer, evaluate, and act upon workers’ traces, professional visibility becomes entangled with surveillance, recognition, and new forms of agency and reappropriation. This pictorial examines how such entanglements are materialized and negotiated when algorithmic systems and AI agents enter workplace visibility practices. We present a set of speculative artifacts situated within a shared regime of algorithmic workplace visibility, including organizational performance dashboards, predictive interfaces, Agentic AI, third-party optimization tools, and critical reactions around such socio-technical constellations. The pictorial pairs these artifacts with marginal annotations that operate as exegesis—critical, analytical, or elaborative commentary—positioning and interpreting the situated material work of design in relation to critical HCI scholarship. Our contribution lies in the insights about algorithmic visibility made possible through the indexical ties between speculative artifacts and exegetic annotation.

Techno-positivist narratives of efficiency, productivity, and “democratised” creativity risk obscuring the material, situated realities of creative work; narrowing how HCI can imagine and enable alternative futures. This pictorial presents four futures workshops with seventy creative professionals that surfaced nuanced visions of creative practice with AI in 2030 through structured exploration of three temporal forces: the weight of history, the push of the present, and the pull of the future. We contribute to HCI and DIS by offering a visually-documented framework for futures workshops with creative communities, extending futures-oriented design methods with a structured, dialogic approach that makes ambivalent attitudes toward technology visible rather than reducing them to simple polarities. By translating these discussions into visual vignettes, created in collaboration with an illustrator, we show how pictorials can surface and compare nuances across cohorts, making the work accessible to researchers, educators, and the creative communities that this research is intended to support.

Large Language Models (LLMs) are increasingly used to generate clinical documentation, yet most systems focus on easing individual users’ clerical burdens rather than supporting multi-stakeholder work. We explore how LLMs expand the design space of clinical multi-stakeholder systems. Through a research-through-design study with 10 dermatologists using a teledermatology prototype, we explore how designing for secondary stakeholders opens new design opportunities and reshapes primary users’ work. Applying the distributed cognition lens to our findings, we conceptualised representational design, i.e., the process of encoding secondary stakeholder perspectives (representations) in LLMs through, among others, prompts, templates, and constraints, while attending to LLMs’ latent knowledge. We envision representational design as supplemental to data- and interaction-level approaches to designing multi-stakeholder artificial intelligence (AI) systems. We demonstrate how LLM-mediated representations cause misalignments between generated outputs and primary users’ notions of acceptability, and how attending to these misalignments sets requirements for human-AI interaction techniques.

Effective patient-provider communication is often hindered by disparities in medical knowledge and the use of technical jargon. Although analogies and metaphors can help bridge this gap, physicians struggle to generate them under clinical time pressure, highlighting a need for supportive design. Through formative interviews with patients and physicians, we identified requirements for explanatory tools that are clear, accurate, and context-sensitive. In response, we designed MediMate, an interactive system that allows physicians to rehearse and iteratively refine patient-friendly explanations using LLM-generated analogies in a low-stakes setting. The interface of MediMate is designed to scaffold the creative process, helping physicians balance clarity with medical accuracy. In a user study involving both physicians and patients, we found that explanations developed with MediMate enhanced communication efficiency by providing such scaffolding. Physicians reported increased self-efficacy and perceived value in using the system as a practice tool for developing their communication skills. Our work demonstrates how interactive AI-powered tools can support clinical communication rehearsal and offers insights for the design of future clinical decision-support and educational tools.

AI development for healthcare aims to enhance medical decision-making through risk evaluation. Scholars have focused on improving the accuracy of Breast Cancer AI Risk Assessment Tools (BC-AIRAT), yet these tools remain underutilized in clinical practices. This provides an opportunity to explore how these tools are used and how they may support risk communications. We conducted a three-phase study, with clinicians and patients, in the context of the United States healthcare system, including formative interviews that surface the challenges of BC-AIRAT practices, design probe re-purposing BC-AIRAT as supporting risk communications, and design probe-driven interviews with diverse stakeholders. Our findings surface the gap and opportunity for designing AI-mediated risk communication tool, highlighting the participants’ reflections on AI for managing risk assessment workflows, mediating fragmented breast health guidelines, and delivering information to patients for proactive decision making. We conclude with design implications for using AI as a mediator in breast cancer risk communication.

This paper reports on the co-design of data comics for participants in clinical research trials and reflects on the specific challenges encountered when working in this design space. The data comics aimed to aid the communication of medical test results collected during the trial to individual study participants in a simple and understandable way. However, navigating the complexities, uncertainties and constraints associated with the clinical trial environment required adaptation to the co-design process. Over 14 months we co-designed a set of comics, overcoming a unique combination of challenges associated with designing in the context of a clinical research trial, such as strict ethical and legal frameworks, complex data and ongoing uncertainty. Reflecting upon the process, we identify several recurring challenges. Based on these challenges we give guidance on negotiating the constrained and uncertain design space of a clinical trial, with design method considerations that apply beyond this domain.

For trans people seeking gender-affirming surgery, there are barriers to accessing trustworthy information about procedures, surgeons, and the postoperative process. Trans-led online information sharing communities are essential tools for trans people seeking surgery, however, they rely on community knowledge that may not always be accessible, complete, or accurate. To understand how an online communication platform might support both surgeons and trans people seeking surgery, we conducted asynchronous online focus groups with 14 participants (ten trans community members and four gender-affirming surgeons). We found that gender-affirming surgeons and trans community members hold values in tension values in alignment such as shared desires for information accuracy, transparency, and communication efficiency to support the surgical process. We contribute design implications for future online information sharing platforms that balance the needs of both trans patients and gender-affirming surgeons, including surgeon-verified information, surgeon anonymity, credential verification, searchable FAQs, and integrated community knowledge.

Women with endometriosis and Polycystic Ovary Syndrome (PCOS) require individualized, long-term, holistic care, yet systemic healthcare barriers leave many managing independently. Increasingly, women are turning to Large Language Models (LLMs), such as ChatGPT, for support in self-management. However, little is understood about how people use and experience LLMs in this context. We employed a feminist participatory approach to collective knowledge production and situated expertise to explore participants’ motivations and expectations in using LLMs for self-management. We engaged in co-annotation of personal ChatGPT conversations and collage-making sessions with 8 women living with endometriosis and/or PCOS, centering their lived experiences. We surfaced the layered experiences of self-managing endometriosis and/or PCOS with LLMs and how these compare and contrast with their ideal visions of care. We contribute empirical insights and design considerations for LLM systems providing support in this context.

More-than-human design asks us to attend to non-human lifeworlds, yet human perception is overwhelmingly visual, making it difficult to engage with sensory realities unlike our own. Inspired by the star-nosed mole—a creature that navigates entirely through touch—we designed FeltSight, a mixed-reality wearable system that shifts the user’s perceptual priority from vision to touch. The system pairs a visually-reduced MR headset with custom vibrotactile haptic gloves, suppressing visual dominance while extending tactile sensitivity beyond the skin, so that users encounter their surroundings through active, pre-contact probing rather than passive observation. This sensory inversion induces somatic defamiliarization, drawing on Haraway’s “tentacular thinking” to invite users to approximate a tactile-first umwelt. We conceptualize this novel form of interaction as Touch Beyond Reach. Previously exhibited as an interactive artwork, this pictorial details the design process and implementation of the system.

Music education for Deaf and hard-of-hearing (DHH) learners remains challenging because access to auditory cues is constrained and instruction is often shaped by hearing-centered pedagogical and communication structures. Prior work has explored haptics for music, yet offers limited situated knowledge of how haptic cues should be designed and negotiated within authentic instruction. We address this gap through a co-design method centered on reconfigurable haptic prototyping. Using modular tactors that can be rapidly repositioned and retuned, we conducted four co-design workshops with educator–learner dyads in South Korea. The workshops involved hands-on trials, bodystorming, and mock teaching enactment to explore music-to-haptic cues within realistic instructional interaction. We show that haptics can support pedagogical fit, make musical concepts tangible as formative in-the-moment support, and function as a shared reference in instruction. We contribute a co-design method, empirical insights into haptics in music instruction, and design implications for DHH music education.

Research and design in Human-Computer Interaction (HCI) have increasingly engaged with embodied, situated, and material-mediated forms of interaction. Across many such systems, sensing is not performed solely by embedded, off-the-shelf components, but arises through material properties and transformations. Yet HCI still lacks a coherent framework for articulating how materials themselves participate in sensing beyond serving as passive substrates. This paper introduces Ambient Material-Embodied Sensing (AMES), a generative theoretical framework that reframes sensing by foregrounding materials as the sensing medium in their own right. AMES conceptualizes it as an emergent property of material transduction, material-body coupling, and ambient computational inference. We articulate three core mechanisms and demonstrate how AMES can reinterpret existing systems while informing future design and research on material-mediated sensing in interaction design.

In the physical world, the force required to interact with an object provides rich information about its material properties and intended use: some objects deform easily when force is applied (e.g. a pillow) while others resist our attempts (e.g. a spring). Recent advances in haptic technology support integrating force modalities into interactive surfaces, presenting novel ways to convey material properties and create engaging experiences. However, little is known about how people perceive and interpret surface-based force interactions. To address this, we designed the Transforcer, a device generating normal, shear, and rotational forces, and studied user perceptions in different scenarios. Our findings show that force is experienced as a multi-layered interaction: bodily perception grounds interpretation, material qualities convey intent and control, and meaningful interactions emerge when body, task, and system align. We contribute (i) a design space of surface-based force interactions; (ii) the design and implementation of the Transforcer; (iii) findings from a semi-structured interview study; and (iv) design recommendations.

Head-mounted displays increasingly require text entry that goes beyond character input to support real-world writing and editing. Existing HMD text entry research and systems focus primarily on entry speed and accuracy, while offering limited support for cursor control, navigation, and command execution. Midair and voice-based techniques address some needs but suffer from fatigue, limited affordances, and poor discoverability of editing functions. This work investigates one-handed thumb-to-finger text composition for HMDs, combining on-body input with hand-proximate visual interfaces that provide tactile feedback and spatial guidance. We map the design space of such systems across input, editing, command invocation, and visualization. Through participatory design workshops with 18 designers, we elicit concrete concepts for one-handed text composition on HMDs. From these artifacts, we derive five implementable design guidelines focused on discoverability and complex text composition.

Touch is a powerful way of connecting with others, yet remains elusive in mediated interaction. Embodied Telepresent Connection (ETC), a VR artwork, offers a unique experience of virtual touch through pseudo-haptics, ethereal aesthetics and subtle biosignals. Unlike approaches that strive to mimic physical tactility, ETC explores touch as an ephemeral and socially meaningful phenomenon. Through soma design-inspired workshops with 14 HCI experts and public exhibitions with 300+ participants, we observed a palette of experiences ranging from play to intimacy, shaped by illusions of pseudo-haptic touch and their embedded social meaning. We contribute: (1) the design material of ethereal virtual touch, expanding the design space of mediated touch, and (2) design insights for cultivating embodied social connection in VR through attention to bodies, aesthetics, and emergent ethics. ETC demonstrates how even ethereal forms of social touch in virtual reality can evoke experiences reminiscent of the real-world analogue, yet form a distinct felt phenomenon offering novel opportunities for supporting genuine human connection.

Design fiction uses narratives to make possible futures tangible and open to reflection, while large language models (LLMs) introduce new opportunities for human-AI co-creation in speculative design. This paper examines how different forms of human–AI interaction shape narrative qualities and thematic patterns of design fictions in the domain of education. We co-created eighteen design fiction narratives about AI in education with GPT-4, guided by six speculative prompting approaches, and analyzed them through a heuristic-based evaluation complemented by an inductive thematic analysis. Our findings suggest that the qualities of these narratives do not emerge from the model alone, but from the interplay between human framing, prompting strategies and model behavior. Different forms of interaction foreground different concerns, narrative perspectives, and speculative tensions, shaping how educational futures are imagined and contested. Building on these insights, we present the Speculative Design Fictionist, a prompt-based design instantiation that externalizes selected strategies and evaluates considerations as a scaffold for reflective human–AI co-creation. Rather than automating speculative design, the instantiation illustrates how generative AI can support collaborative inquiry into educational futures through structured yet open-ended interaction.

Journaling is a well-established practice for reflecting on life events and constructing meaning; yet, finding concrete meaning from them remains challenging. Recent advances in generative AI demonstrate the potential to generate materials that support reflection, suggesting opportunities to assist meaning-making in journaling. However, prior approaches often center on ambiguous outputs and reflections of a single experience, leaving unexplored how concrete interpretations from AI can help self-reflection in journaling. In this study, we designed Augmentiary, an LLM-based journaling system that suggests candidate interpretations of experiences with concrete meaning while preserving users’ agency and voice, based on insights from a formative study with eight journal writers. We then conducted a four-week deployment study with 25 participants. Our findings show that AI’s interpretive feedback helped connect fragmented experiences and supported self-understanding through comparison with their own thoughts. Moreover, tensions between user agency and constructive reflection were revealed. We conclude by discussing design implications for AI-supported systems for fostering meaning-making without replacing users’ thinking.

Large language models (LLMs) are increasingly embedded in students’ academic work, yet the increasing reliance can undermine learning depth and raise integrity concerns. While reflection has long been studied in HCI to foster awareness and behavior change, little is known about how to support students in reflecting on everyday LLM use. We present PromptMirror, a student-facing dashboard that processes LLM conversation logs and visualizes four perspectives, temporal, sentiment, intent, and thematic, to encourage reflection. We informed the design of PromptMirror with two focus groups (one expert and one student with four participants each) and subsequently conducted an online think-aloud with 20 university students who uploaded their own LLM use data. Findings provide preliminary evidence that PromptMirror may support students in recognizing their LLM use estimation gap and engaging in deeper reflection on LLM reliance. Our contributions are twofold: (1) a student-centric reflection system; (2) empirical insights into reflective analytics for everyday LLM tools.

Personalization is crucial for effective learning, yet online learning, designed for widespread availability and open access, lacks personalized guidance. Recent advancements in large language models (LLMs) offer opportunities to bridge this gap. We explore how LLM-driven tools may be designed to support personalized and adaptive learning and examine how they shape user experience and learning outcomes. We iteratively designed LearnMate² to support online learning by providing personalized study plans, real-time contextual assistance, and adaptive learning activities. A preliminary study (n = 24) assessed the effectiveness and usability of LearnMate² and informed refinements in our system, which we then evaluated (n = 16) against a combination of a state-of-the-art online learning platform and an LLM for learning support. Results indicate that LearnMate² advances AI pedagogy by improving both learning outcomes and user experience compared to existing online learning and support tools. This work advances our understanding of the design space of personalized, AI-driven educational tools and their potential impact on user experience.

How can AI systems support high-stakes self-advocacy without automating away a user’s voice? As people turn to chatbots for consequential communication, risks include misalignment, over-coaching, and loss of narrative authority. We present Sage, a task-oriented hybrid conversational system that operationalizes practices from narrative mediation and conflict resolution through structured interaction flows and persistent, user-visible state to support strategic storytelling. In a formative evaluation as a research probe (N = 21 domain experts across negotiation, communications, and HCI), we observed a recurring interaction pattern: participants frequently attempted to revise how the system understood their situation after it had already stabilized and reused earlier narrative interpretations to shape subsequent prompts, notes, and drafts. To account for this pattern, we introduce Narrative Plasticity (the user-facing capacity to revise a system’s standing narrative interpretation once it begins shaping interaction) and State Stickiness (the interactional resistance that arises when persistent interpretations are operationalized across multiple system surfaces). We identify four mechanisms of State Stickiness (consolidation, replication, operationalization, accumulation) and synthesize design considerations for governing interpretive state in AI systems mediating consequential communication.

Remote collaboration software mediates modern knowledge work, yet existing inspection methods often overlook inequities in remote teamwork. We introduce RemoteCollabEval, a new HCI inspection method for systematically surfacing inclusivity issues in synchronous remote collaboration software. RemoteCollabEval consists of: (1) six novel research-based facets illustrated by two personas grounded in social identity theory to capture the interdependent nature of teamwork for dominant and under-served users, and (2) a specialized walkthrough to help practitioners identify inclusivity issues and actionable interface design fixes. We evaluated RemoteCollabEval against the standard Groupware Walkthrough in a controlled study with 29 HCI students across 10 teams. Participants inspected Zoom and Replit, followed by a survey and semi-structured group discussion. RemoteCollabEval surfaced about six times more inclusivity issues than the standard approach and was perceived as comprehensive and well-designed. Our contributions include: (1) a validated method for identifying inclusivity issues in collaboration software and (2) teamwork-specific facets that provide a foundation for more equitable design practices in distributed teams.

Desktop 3D printers are celebrated as “maker technology,” yet their proliferation is often bound up with a sociotechnical imaginary that privileges linear automation over material engagement. As designer-makers, we found conventional systems limiting our interaction with the materials at hand. In this work, we explore reframing 3D printing through untaming: a critical framework that treats the machine as a situated tool involving makers and materials in its operations. Specifically, we investigated how printers work with textiles, developing a new controller that enables the rhythmic coupling of maker, machine, and material movements. We detail our journey through autoethnography, documenting our design decisions and situated practices, alongside a swatchbook that captures these “untamed” actions and resulting artefacts. This pictorial offers untaming as a provocation for Making-HCI scholarship, and demonstrates an instance of how we might question our agencies with materials in digital fabrication.

Computational weaving is an old form of HCI, where hand Jacquard machine weaving embodies rich interaction and digital computation. Low-cost Jacquard looms are becoming common in HCI, but they come with limitations due to technology standardization. Jacquard weaving looms are often customized to create different textile structures; in our case, 3D weaves. This research uses speculative itineration to investigate the iterative re-engineering of a low-cost computational Jacquard handloom. We rebuild and enhance an existing designed loom by co-constructing stories and conducting semi-structured interviews with novices and experts, allowing imaginaries to change the loom. Our findings reveal innovation potential across the spectrum of looms. Both novice and expert weavers seek looms that allow modification, driven by the potential for new weaving designs and design spaces of looms within digital craftsmanship. Itinerative co-constructive speculation allows for customized looms across skill levels, challenging prevailing paradigms where expertise level often dictates technology choice.

In this pictorial, we present our development of a simulator for resist slip-casting: a technique for crafting textured ceramics by masking regions of a water-absorbent mold. While actual slip-casting hides material transformations under opaque liquid clay, our simulator uses 3D cellular automata to visualize the deposition process in real-time. We detail our iterative algorithmic development, from simple proximity-based accumulation to a more complex model of diffusion and slumping. Through a research-through-design lens, we further reflect on the fractured flows between virtual and real materials. We annotate the distinct materialities and temporalities the simulator embodies, considering how its departures from the “ground truth” actually heighten our sensitivity to the physical craft. We propose that such making-simulators serve as a form of intermediate-level knowledge, acting as both a design tool, and a mirror for critical reflection on the agency of makers and materials.

Chinese ceramic-making involves complex and interdependent steps, making it technically demanding. Digital fabrication methods attempt to make the process more accessible, but for craft-creators, technical challenges such as CAD and CAM skills remain major obstacles. To address this, we designed a hybrid workflow that integrates Generative AI with clay 3D printing to support new creative possibilities. We evaluated the workflow through ClayScape, a design tool that operationalizes this approach, with four ceramic creators. Our findings show that the workflow supports accessible ceramic creation while revealing both expanded opportunities for creative exploration and challenges in balancing agency and control. This work demonstrates how hybrid workflows can lower barriers to digital fabrication while supporting creative possibilities in culturally grounded ceramic practices.

Translating Computer-Aided Design (CAD) models into physical objects requires expertise and adjustments to navigate fabrication constraints. Makers develop this tacit knowledge by understanding materials, techniques, and practical requirements. Adjustments are typically shared with designer collaborators through sketches and text. However, this documentation lacks situated knowledge gained during fabrication and remains disconnected from the model.

To explore how computational tools could address these limitations, we developed MakeAloud, a design probe leveraging AI to capture makers’ in-situ knowledge with hand-tracking hardware and think-aloud computing and then generate design insights within collaborators’ CAD tools. Through a study with woodworkers and designers, we identify three design considerations for designer-maker collaboration tools: surfacing fabrication constraints in CAD to preserve designer intent, supporting asymmetrical domain expertise through AI-mediated communication, and building collective fabrication knowledge archives. This work contributes empirical insights into how AI can bridge design and fabrication workflows, offering pathways for cross-disciplinary collaboration.

Creativity support tools (CSTs) aim to elevate the quality of artists’ creative processes and artifacts. Yet most current CST evaluations overlook temporal and social aspects of tool use. To address this gap, we present a longitudinal, group-based CST evaluation through a three-week deployment of ArtKrit, a computational drawing tool that supports disciplined drawing. Nine digital artists, organized into three communities of practice, completed weekly “master studies” alongside a researcher-artist. Our results show users’ evolving relationships with ArtKrit over time—from early experimentation to selective incorporation or misuse—alongside changes in their ways of artistic seeing. These changes unfolded within artist support networks that fostered confidence and creative safety, and validated individual expression. Overall, our findings suggest that CST evaluations can—and should—be designed as opportunities for meaningful artistic engagement rather than purely extractive measurement exercises. We contribute this longitudinal, group-based approach as one CST evaluation method.

Translating physical games to digital tabletops is a promising way to engage museum visitors in open-ended museum experiences, but the process is poorly understood. How can the mechanics and social dynamics of physical games be translated into this new material form? This paper presents an empirical account of translating a physical, history-themed card game to a tangible tabletop for museum play. Through a year-long Research through Design study, we developed and deployed three comparative prototypes, each investigating a key dimension of translation: (1) the shift from turn-based to real-time event placement, (2) the introduction of time pressure, and (3) the adaptation of game difficulty. Drawing on in-situ playtests with families and workshops with expert designers, our findings reveal a central tension between fidelity to the source material’s social strengths and the transformative affordances of the digital medium. We contribute actionable design implications for translation and an open-source toolkit to support future work.

There is growing interest in making museums more participatory, creating opportunities for HCI to support audience involvement. We approach them from the perspective of play and playfulness, looking at how playful technology might promote audience participation in museums. To explore this design space, we conducted a case study at Museu del Cinema, engaging diverse stakeholders to co-design playful technologies. From a reflexive analysis of our design process and its results, we contribute: (1) a catalog featuring three design ideas showcasing different ways of active audience participation; (2) a set of design insights for creating such technologies. Examining these contributions through the lens of literature on participatory museums allows us to position their scope and potential impact, and to envision exciting avenues for future research. Overall, our work offers an exploratory case study that contributes to research on playful technology and museum participation.

In this pictorial, we introduce the conceptual framework of “plasmatic visualization,” an interactive form of data visualization that incorporates the image of the observer to provoke critical reflection on how data, bodies, and environments are mutually constituted. Drawing on the concept of plasmaticness from cel animation theory, we extend environmental data visualization beyond stable, objective representations toward affective, mutable experiences. We present “Plasmatic Mirror,” a design prototype using a real-time video camera co-located with an architectural-scale display to illustrate the principles of plasmatic visualization and engage participants in a reality-bending encounter with data on microplastics in the news. Through this work, we articulate three defining aesthetic qualities of plasmatic visualization—shifting from representational to visceral experience; from separate to entangled forms of visualization; and from exploring to attuning to environmental data—to support HCI designers in creating interactive data visualizations that prompt audiences to reflect on their relationship to environmental issues.

While feminist and critical data theories have long critiqued the use of data to uphold a positivist-informed view about science, few examples offer alternative methods to display scientific constructs. In response, we present Data & Me: an exhibit informed by feminist and critical data theories, which we designed and launched at a local science museum. Data & Me introduces museum visitors to data using a @ vibe – a vibe that signals that data can be #slow, #handmade, and #personal. We designed this vibe to be noticeably different than the @Science vibe in the rest of the museum. Throughout our design process, we adapted visualization vibes as an analytic and generative tool in the context of a science museum. We present four design choices that enable the design of a vibe: visual, topical, material, and crediting. We discuss how our exhibit aligns with ongoing discussions about alternative research outcomes and calls for plurality in HCI.

This pictorial examines how Research through Design (RtD) can engage uncertainty, omission, and silence in historical datasets. Using an incomplete archive of shipwrecks in the Great Lakes region of North America, the project addresses gaps in archival records as well as forms of knowledge that are structurally excluded or held outside Western archival systems, particularly Indigenous watercraft used long before colonialism. We adopt a two-part RtD process that uses speculative data practices and temporal design: first, we construct a dataset that combines archival records with speculative and AI-generated images and text—not to recover missing histories or reconstruct data on behalf of communities, but to critically surface how such absences are produced, mediated, and what is at stake when they persist; second, we translate this hybrid dataset into two map-based artifacts that are designed to explore multiple, non-linear temporalities. These maps deliberately blur distinctions between archival and generated data, encouraging viewers to question what they are looking at. In doing so, they produce uncertainty and doubt as experiential qualities of artifacts often assumed to be factual and authoritative. We contribute to HCI by demonstrating how speculative data practices—treating archival silences as generative rather than corrective—can function as a form of temporal design by showing how multi-temporal maps can explore contingency, relationality, and contested historical knowledge.

Data-driven policing often prioritizes incident records over residents’ lived experiences. In the Baltic city of Riga, with a history of distrust and limited community-police engagement, this can further alienate the public. To bridge this gap, we propose a Research through Design (RtD) inquiry into the development of Par drošu Rīgu, a civic tool for community-data-integrated policing. With municipal police, NGOs, and city staff, we ask how RtD enables stakeholder negotiation and which interaction qualities support trust and the use of combined community and incident data. The co-design process included workshops that surfaced divergent notions of safety; material probes designed as boundary objects to negotiate among stakeholders; and a pilot deployment showing how combining quantitative and qualitative data reshapes engagement and trust. Mixed-methods evaluation suggests increased officer-citizen interaction, but frictions in sustaining stakeholder collaboration. We contribute (i) an empirical RtD inquiry with public institutions, (ii) an artifact combining physical and dashboard interactions, and (iii) reflections on interaction design as a boundary-spanning practice for trust and infrastructuring.

In menstrual cycle tracking apps (MCTAs), AI-based predictions and insights have become increasingly popular. These features enable users to receive personalized information about their bodies and mental states. However, there is currently little research on how these predictive AI features and explanations affect users’ lived experiences. This paper examines human-AI entanglement in MCTAs through 14 semi-structured user interviews and a group autoethnography. These methods uncover the processes leading to this phenomenon. Our results reveal that: (1) users understand their lived experiences in light of AI predictions, although these predictions can be faulty due to imperfect logging practices, (2) the user interface features and AI explanations do not support awareness or critical engagement with this entanglement and meaning-making, and (3) non-normative MCTA users report a sense of isolation in this entangled interaction. Based on our findings, we propose design implications for predictive AI features and explanations.

Fitness tracking platforms increasingly integrate generative AI to interpret activity data, such as Strava’s Athlete Intelligence. These integrations raise questions about how athletes engage with AI-supported fitness self-tracking. We analyzed 297 Reddit threads and 5,692 comments from r/Strava following the company’s launch of AI features to examine user reactions to AI-generated fitness feedback. Our findings revealed four recurring tensions: (1) numerical evaluation versus contextual understanding; (2) isolated session summaries versus ongoing training narratives; (3) a fixed AI tone versus diverse emotional states; and (4) a single AI voice versus different athletic types. Across these tensions, users resisted AI feedback that constrained interpretations of their own lived experiences. These findings shed light on the implicit challenges of integrating AI into self-tracking platforms. We conclude with implications for the design of AI-supported self-tracking systems that preserve interpretive openness and user agency.

Personal informatics research has predominantly explored the setting and tracking of quantitative goals, with less focus on qualitative goals. To address this gap, we report staged participatory design sessions with 26 stakeholders, including goal-oriented work practitioners, self-trackers, and designers. Leveraging practitioners’ expertise, we co-designed a novel multi-session, 4-week intervention for goal identifying and goal setting, consisting of sequentially scheduled tools from goal-oriented work. We also explored the intervention’s pre-use acceptability, the perception of user interaction, and usability of the digital format. Our findings informed four design implications to support identification of ambitious yet realistic goals, structured and customizable support for goal identification and setting, iterative goal revision, and reduced reliance on quantitative tools. We also reflect on the value of engaging multiple stakeholders in participatory design.

Although HCI research indicates that lived experiences should be considered in technologies for type 1 diabetes (T1D) management, technologies and prevailing concepts for “successful” management continue to focus narrowly on metrics for improving blood glucose, rather than a holistic understanding of life with T1D. T1D shapes every aspect of life, including personal priorities, values, and needs, making it highly personal and manifesting in different practices. We conducted semi-structured interviews, a 5-day diary study, and a workshop with 10 individuals with T1D to elicit and reflect on different ways of living with T1D. Based on these accounts, we conceptualize self-management styles as a multidimensional construct spanning seven dimensions: values and prioritization, planning, risk-taking, rhythm of care, openness to experiment and learn, engagement in tracking, and success definitions. We contribute an account of these dimensions that designers can use as a lens for developing T1D technologies that support personal ways of living with T1D, foregrounding lived experience and outlining implications for HCI research.

While the Quantified Self movement emphasizes the informed self through self-tracking and data representation, limited research has explored how to foster reflective engagement with personal data to cultivate a higher qualified self. To address this gap, we extend the focus onto fidgeting behavior, which is often considered subconscious with its qualitative meaning relatively less explored. We design MindSeed, an interactive system that enables users to track, co-create and reflect on their fidgeting data. We deployed MindSeed in a seven-day field study with eight participants who carried and interacted with it across different daily contexts. Analysis of the logged data and the interviews provides preliminary insights into how people adopt MindSeed into their daily lives, and how it may support meaningful reflection on daily subjective experiences that might otherwise go unnoticed. Building on these exploratory findings, we discuss how our works might inform future research agenda and design practices related to the qualified self.

With stress-tracking technologies becoming pervasive, a range of feedback displays has been explored to support engagement with stress data. However, most displays are studied in isolation, leaving limited understanding of how different forms of feedback shape engagement differently. This study compares three real-time stress feedback displays: screen-based quantitative, screen-based expressive, and physical expressive. Twenty-one participants took part in stress induction and recovery tasks to gain direct experience with each display, with data collected through surveys and semi-structured interviews. We found that different displays became associated with different modes of engagement: quantitative displays supported mobile, analytical use; screen-based expressive displays encouraged active monitoring; and physical expressive displays enabled peripheral awareness. We highlight the importance of balancing awareness with emotional experience, shaped by data representation and materiality. We challenge the assumption in personal informatics that more information or a more intuitive understanding is always beneficial, and offer a design framework for experiencing sensitive bio-signal feedback.

Youth are increasingly turning to social virtual reality for connection. Yet, while the embodied and social nature of these spaces may offer new opportunities for emotional expression and regulation, little is known about how youth leverage these capabilities in practice. To design VR interventions that better support youth, we collected and analyzed 64 surveys and 21 go-along interviews with youth in VRChat. Our findings suggest that youth engage with their emotions in social VR using multiple strategies in both adaptive and maladaptive ways, primarily distraction and venting. Youth noticed how practicing emotion regulation in VRChat translated to their daily lives and were curious to learn more about how to emotionally regulate themselves and help others. We generated four design opportunities: realistic practice of underdeveloped emotion regulation strategies, increased access to community support and resources, motivation through identity exploration and acceptance, and IRL transferability and integration.

Dining is a social activity, yet human–computer interaction (HCI) research has rarely examined how extended reality (XR) headsets shape shared restaurant experiences. Although XR can augment taste perception, headsets also obscure facial expressions – particularly pertinent when only some diners wear them. We identify an opportunity to make a wearer’s biodata socially visible via an outward-facing display on the headset to facilitate novel social dining experiences with XR. To explore this, we designed E.A.T. (Expressive Augmented Togetherness), a novel XR dining system that combines food augmentation with external displays depicting physiological arousal and pupil movement to facilitate social interactions. Through interviews following a restaurant field study with five groups of three diners, we examined how the system influenced taste perception and social interactions. We identify six design considerations for socially legible XR systems for dining, ultimately, aiming to inform how HCI can better support social dining experiences.

The central challenge for in-public companion devices is not only what to perceive, but when and how to become socially present. We study this question through the ACG (anime, comics, and games) community, where people already carry and display character goods in public as materially mediated forms of companionship. We present Goobo, a character-bound in-public companion embedded in an ita-bag that combines periodic visual sensing with in-character speech. We first conducted a formative online workshop with 17 ACG participants to scope desired capabilities and interaction boundaries, which led us to focus the probe on visual perception and character-consistent expression. We then carried out in-situ city walks with 20 companionship-oriented ACG participants using Goobo in everyday public settings. Through think-aloud data and post-walk interviews, we identify four user needs, three interaction preferences, and a set of forward-looking expectations for future companions. We synthesize these findings into a design framing of in-public companionship shaped by context factors, user factors, and device factors. Beyond the ACG case, the paper contributes interactional insights for designing publicly visible companions whose value depends on timing, selectivity, and socially legible presence.

Comic layouts are increasingly shaped by the interaction affordances of reading devices, such as scrollable smartphone comics. eXtended Reality (XR) headsets are emerging media consumption devices, offering spatial affordances for new comic layouts. We investigated how XR could enhance comic experiences through participatory design with comic creators, readers, and HCI experts. Participants used ComiXR, our novel XR comic platform, to adapt a print comic for XR while thinking aloud. Participants preferred spatially placed panels that use depth to separate characters from backgrounds. Ambient sound and haptics enhanced immersion, while eye-tracking addressed limitations, e.g. hiding spoilers. However, comic creators expressed potential difficulty in authoring these non-visual elements. Participants explained that XR comic layouts could change between stationary and public mobile settings, where social acceptability and situational awareness become prominent challenges. Our findings support the continued cultural and artistic significance of comics and identify new use cases for XR.

This pictorial presents Searching for Us, a pervasive tangible narrative exploring collaborative storytelling with physical avatars. Through a mixed-methods study across six sessions with 24 participants organised in pairs, the pictorial illustrates how asymmetric roles (interactor/listener) foster shared story ownership, the collective narrative agency players develop through embodied, performative engagement, despite unequal physical control. This work contributes design principles for the design of tangible narratives: embracing external control perspectives, designing asymmetric collaboration, structuring convergence moments in the experience, and supporting multiple engagement modalities. It advances understanding of social dynamics in interactive storytelling.

We present a case study of Persona-L, a system that leverages large language models (LLMs) and retrieval-augmented generation (RAG) to model personas of people with Down syndrome. Existing approaches to persona creation can often lead to oversimplified or stereotypical profiles of people with Down syndrome. To that end, we built stereotype detection capabilities into Persona-L as a design probe to open conversations. We then conducted interviews with caregivers and healthcare professionals (N=10) to examine how Down syndrome stereotypes could manifest in the content and delivery of LLM outputs, and interface design. Our findings show the challenges in defining stereotypes, and reveal potential pathways where stereotypes could emerge, including the training data, LLM outputs and interface design. This highlights the need for participatory methods that capture the heterogeneity of lived experiences of people with Down syndrome.

In China, special education teachers (SETs) for autistic children continuously coordinate complex demands, from intervention planning to behavior interpretation and parent communication. While SETs bear heavy responsibility for these decisions, they often lack the professional feedback loops needed to validate their judgments. Recently, Large Language Model (LLM)–based conversational AI (CAI) has emerged as tools that provide on-demand conversational scaffolding to support teachers’ educational practices. This study examines SETs’ opportunities and challenges in using CAI through a two-week diary study and follow-up interviews with 12 SETs. We found that SETs used CAI as scaffolding tools for interpreting children’s autistic traits, preparing parent communication, and seeking emotional support. Their strategies shifted from task-specific queries to open-ended, experience-driven dialogues that supported reflective sensemaking. However, the need for contextualized guidance often clashed with privacy concerns, making SETs hesitant to share the specific child data required for advice. We conclude with design implications for supporting reflective, responsive, and adaptive trust calibration in the use of CAI.

Psychiatric clinical interviewing is a core yet challenging skill for psychiatric residents, requiring clinicians to navigate open-ended dialogue, interpret emotional cues, and manage diagnostic uncertainty. However, existing simulation-based education (SBE) tools often fail to provide sufficient opportunities for realistic and autonomous interview practice, largely due to their reliance on scripted and rigid interactions. In this paper, we explore how large language model (LLM) agents can be leveraged to support SBE systems for psychiatric clinical interview training. We first conducted a formative study to identify the psychiatry-specific learning needs and interactional challenges that should shape system design. Based on these insights, we developed PsyMooc, an LLM-enhanced SBE system designed to support open-ended interview interactions and deliver context-aware, competency-oriented feedback through LLM-driven agents. We then evaluated PsyMooc through a small-scale between-subjects user study to examine usability and learning-related outcomes. The results provide preliminary evidence that PsyMooc was perceived as usable and engaging, and was associated with improvements in residents’ clinical confidence, interview performance proxies, and patient-centered communication behaviors.

Many people browse online communities to learn from others’ experiences and opinions, e.g., for constructing travel plans. Conversational search powered by large language models (LLMs) could ease this information-seeking task, but it remains under-investigated within the online community. In this paper, we first conducted an exploratory study (N=10) that indicated the helpfulness of a classic conversational search tool and identified room for improvement. Then, we proposed ConSearcher, an LLM-powered tool with dynamically generated member personas based on user queries to facilitate conversational search in the community. In ConSearcher, users can clarify their interests by checking what a simulated member similar to them may ask and get responses from diverse members’ perspectives. A within-subjects study (N=27) showed that compared to two conversational search baselines, ConSearcher led to significantly higher information-seeking outcome and user engagement but raised concerns about over-personalization. We discuss implications for supporting conversational information seeking in online communities.

Large language models (LLMs) have become popular learning aids, yet research into their truly beneficial uses remains in its early stages. This paper presents the design, development, and evaluation of an LLM-driven gamified learning prototype, GamifiedLM, which explores the novel integration of LLMs with gamified learning theories. GamifiedLM aims to assist students in overcoming learning challenges. Unlike the most prevalent chat-based LLM apps, GamifiedLM integrates multi-agent systems and generative user interface (UI) technologies, rendering the output of LLMs into a richer, interactive learning flow. We conducted two co-design sessions with 10 UK university students experiencing mild learning difficulties, and identified key design goals and system components for GamifiedLM that closely align with their needs. After iterating the prototype, we evaluated it with a broader group of UK university students. The results indicate that by delivering an engaging gamified learning experience, GamifiedLM can better maintain learners’ motivation and focus compared to existing tools. By enhancing the learning experience, users could master the given reading materials more effectively. This work contributes to exploring how LLMs can be designed in line with beneficial learning theories and accessible educational practices.

AI-powered educational technologies have demonstrated measurable benefits for learners, but their design and evaluation have largely centered on K-12 contexts. As a result, many AI-supported learning systems remain poorly aligned with the needs, constraints, and goals of adult learners. To better understand how AI systems function in adult education, this paper examines the deployment of several AI learning technologies developed within a multidisciplinary, national research institute in the United States focused on adult learning and online education. Drawing on longitudinal deployment data, we conducted a reflexive thematic analysis to identify recurring challenges and design considerations across systems. These insights were synthesized into a set of 19 design guidelines intended to inform future AI-supported adult learning technologies. We demonstrate the utility of these guidelines through a heuristic evaluation of the deployed systems. Lastly, we present a guideline exploration tool that aids in the ideation of technologies by connecting the guidelines to stakeholder statements surfaced in the analysis process.

Maintaining social connections via informal gathering and communication in physical workspaces is challenging. To maintain connections without causing mental strain, we propose “Async Party,” an activity that reconfigures drinking rituals through asynchronicity by combining physical triggers with digital sharing. In this activity, participants record short videos of individual toasts, ask a nearby colleague to record their “cheers” moment, and upload the short video to the dedicated Slack channel. A three-month field deployment revealed that while the filming rule was designed to spark face-to-face interaction at the moment of recording, the encounters it produced were experienced not as procedural steps but as socially meaningful moments in their own right – a social quality that extended beyond our anticipation. Members derived a sense of community vitality not by watching every video, but by peripherally perceiving the accumulation of ritualistic content. Drawing on Research through Design, this study articulates a novel design space for “asynchronous rituals” – configurations in which synchrony and asynchrony are intentionally composed into a recurring temporal cycle – and derives three transferable design strategies for sustaining low-burden belonging in hybrid environments.

Managing social batteries is crucial in highly interactive environments such as open-plan offices. To support both the internal self-management of one’s own social battery and the external coordination of interactions with others, we designed Sotate, a device that visualizes social batteries. Sotate uses a gauge-style LED display to represent a user’s social battery level and physical placement to indicate when the user is engaged in recharging activities. Our three-week field study with four groups revealed that participants used social battery representations not only to reflect their internal states, but also to intentionally express non-literal states to manage external communication. We also found that explicitly marking the beginning and end of recharging made recharging activities more deliberate and reflective, and that colleagues relied on simple, immediately interpretable cues rather than precise numerical values when coordinating interactions. These results highlight design considerations for systems that support the management of internal states associated with social interaction.

Sharing music with a remote partner can help maintain affective connections by fostering bonding and supporting lightweight interpersonal interaction across distances. However, asynchronous remote music sharing often fails to sustain engagement when recommended songs remain unlistened to, or when listening status remains unclear. To support sustained music sharing and listening over distances, we designed Reelee, a bidirectional music recommendation system that physicalizes song duration. Our four-week field study with four dyads revealed that participants were reminded of their remote partners in their daily lives and experienced music sharing through Reelee as a form of indirect, asynchronous communication. Our findings suggest design considerations for creating meaningful and sustained remote media sharing by physicalizing shared media information and carefully structuring interaction within such systems.

While the rapid exchange of messages in instant messaging (IM) is useful for maintaining convenient and continuous communication, it can simultaneously diminish recipients’ opportunities to reflect on and emotionally engage with individual messages. This paper introduces Adilet, a system that represents the process of composing and receiving delayed messages through a physical device, designed to enhance message awareness, perceived value, and reflective engagement. Adilet allows users to ensure the status of writing, sending, and receiving messages and approximate total writing time. A field study with four groups of two close partners demonstrated that Adilet improved message quality by increasing participants’ awareness on both writing and reception. Furthermore, consistent awareness of message presence and clues about message content enhanced anticipation. Our findings suggest further implications for designing communication systems that support deeper and more valuable emotional exchange by improving message value and recognition in close relationships.

Emotional distress is rising globally. Widely-used music streaming apps, if suitably configured, may offer a scalable intervention to help millions of people manage their emotions more effectively. Services like Spotify already offer mood-based playlists. However, such features are not tailored to individual users’ emotional needs, reducing their effectiveness. We introduce MoodDJ, a Spotify plugin that creates playlists that take users on personalised emotion journeys from current to desired emotion. In a 14-day in-the-wild user study, we explored 22 participants’ experiences of using MoodDJ through usage logs, surveys, and interviews. Findings reveal diverse patterns of use in a range of contexts, with participants often using MoodDJ to up- or down-regulate arousal or to feel more pleasant. Qualitative data suggest improvements in emotional awareness. We contribute evidence for the feasibility of embedding emotion-regulation interventions within popular music apps, and present design implications for tools of this kind.

The relationship between children and extended family members (such as grandparents) is extremely important, providing meaning, knowledge, and enjoyment. However, the distributed nature of our modern society has made it difficult for young children to remain connected with extended family. We conducted a study with young children and their parents, as well as grandparents of young children, to explore their needs and preferences when communicating remotely. Through the use of video prototypes, we found a number of themes related to the types of interactions that devices should afford for young children and grandparents. These include tensions between children’s independence and parental control, the need for child-led bursts of interaction, and considerations on device lifetime and the link to remote family members. In this paper, we detail our methodology and findings to arrive at design values for this particular user group.

This scoping review examines the past decade of research at the intersection of democracy, technology and design. Analysing 170 articles across Scopus, the ACM Digital Library, and the DRS Digital Library, we investigate how this research relates to democracy and how democratic values are conceptualised and enacted through interactive systems. We identify four dominant orientations: technology as democratic repair, as risk, as co-constitutive of democratic practices, and as embedded in everyday collective action and lived practices. Across these, we reveal a persistent misalignment: while democratic values are frequently invoked, much of the work reduces democracy to technical or procedural problems, prioritising optimisation over power, relationality, and lived practice. As a result, key dimensions such as collective agency, structural inequalities, and infrastructural conditions remain under-addressed, offering novel opportunities for HCI research. We argue that democratic technology design should advance better alignment fostering responsibility, participation, equity, and transparency in sociotechnical systems.

Participation is important to Human–Computer Interaction; yet, there are few empirical accounts of how it can be enacted in practice when working with at-risk communities under safety and precarity concerns. This paper offers an account of a six-year, community-led collaboration with an organization supporting individuals affected by forced marriage in Switzerland, centered on the design, development, and handover of a digital self-help tool. Rather than centering the artifact, we use its creation as an analytic lens to examine how design justice principles were enacted and negotiated through concrete decisions, boundaries, and compromises within a highly sensitive, care-centered context. Our account illustrates how justice-oriented participation can be sustained through rights-based and trust-negotiated forms of engagement, even when participation is necessarily uneven, selective, unsafe, or time-limited. This paper contributes process-level insights for HCI researchers and practitioners designing with at-risk communities, foregrounding shared decision-making, community ownership, and responsible handover within existing ecologies of care.

Design research increasingly engages with diverse forms of justice, care, and activism. Yet, the implications for interactive technologies that benefit beyond human-centric interactions remain limited. This paper examines community animal rights activism in Türkiye against silencing during a period of systematic removal of community animals from public space, forced by the introduction of a new national law. Our contribution is the rationale for participants’ activism, driven by their collective and individual struggles against the silencing of multispecies neighbourhoods. The Turkish context provides a critical case for HCI and design, while animals in Türkiye are naturally accepted as urban co-inhabitants, and the everyday practices of multispecies care, negotiation, and coexistence have been core to neighbourhood life. Through twelve interviews, we examined how rights advocacy and collective action are practised for community animals in urban neighbourhoods and proposed an agenda to translate these learnings into tech-driven design intervention practices through co-creative potentials.

Within the HCI community, there has been increasing attention to address issues of injustice through participatory and community-engaged approaches. In addition, researchers who conduct this collaborative work with marginalized groups are sharing the institutional vulnerabilities, challenges, and harms that can impact their well-being and their work. In this paper, we argue how the HCI community can learn from the knowledge and strategies of activists who engage in collective action and movement work. In particular, we discuss the role of joy in participatory, community-engaged, and equity-oriented work. Through testimonial authority, we present stories to describe the importance of cultivating joy, how we design for joy, what joy looks like in our work, and how joy can be a sustaining force for researchers and collaborators alike. We end with implications for HCI design and research work with marginalized communities.

Zines–small, niche, and often handmade publications–have historically served as vehicles for community and activist organizing. In this work, we explore zine-making, or zineing, as an avenue for mutual aid sensemaking and praxis. We partnered with mutual aid organization Austin Mutual Aid to host six discussion and zine-making workshops with a cohort of eight participants over three months. Through their zine spreads, participants visualized pathways from mutual aid theory to practice, exploring how their lived identities shape community engagement, navigating tensions between organizing aspirations and systemic barriers, and centering joy and restoration as resistance strategies. Five months post-workshop, follow-up interviews revealed how the structured zineing process helped participants sustain involvement while avoiding burnout. We contribute to HCI literature by positioning zines as mutual aid community technology, demonstrating how collaborative zine-making facilitates the transition from theory to sustained praxis, and extending community-based participatory design partnerships with grassroots organizations.

How should we design for consent? Consent is commonly integrated into products and interactions as binary, static, transactional, and something that should be recorded rather than discussed. Through a Research through Design process, we respond to a call to translate conceptual framings of consent into concrete artifacts and interactions in the context of digital contraception. We contribute with the Consentful Lights, a set of artifacts that enable partners to access intimate fertility data in a co-located setting through play and touch; inviting them to participate in a consentful interaction that is embodied, dynamic, explicit, and shared. Through an in-the-lab exploration of the Consentful Lights, we find that they have the potential to nurture a consentful moment and allow for deterring non-consensual access to intimate data. We conclude with a discussion on the need to pluralize the design of consentful interactions and a reflection on embedding feminist values into design artifacts.

As Artificial Intelligence (AI) adoption expands, mental health chatbots are increasingly used for emotional support and self-management, yet their roles and responsibilities remain ethically ambiguous in everyday use. Prior research often treats these boundaries as externally defined, overlooking how users interpret and negotiate them in practice. We analyzed a large set of app store reviews from four mental health chatbots using topic modeling and qualitative analysis. We identify three recurring processes: how users infer roles through interaction, how they negotiate ethical tensions during use and breakdown, and how they collectively define boundaries through reviews. These processes indicate that users infer roles from how the chatbot communicates and extend trust cautiously, setting boundaries on what they rely on it for. Breakdowns prompt reassessment, while reviews serve as sites where users define acceptable behavior. These findings suggest that ethical boundaries are actively constructed through interaction and platform participation, indicating the need for designs that better support how users interpret, negotiate and share judgments about AI behavior over time.

Intergenerational conversations often break down when differences in tone, language, or expectations lead participants to feel dismissed or misunderstood. In this work, we explore how people envision AI-driven chatbot interventions for addressing communication problems in text-based intergenerational family chat. We conducted a scenario-based design interview with 10 pairs of family members from different generations, in which participants designed chatbot interventions that varied in intervention target and timing. Our findings show that participants expect chatbots to perform multiple themes of intervention, including mediating understanding, providing emotional support, offering evaluative commentary, and guiding interaction through behavioral suggestions. These expectations varied systematically across intervention contexts, giving rise to distinct chatbot roles such as neutral mediators, message coaches, repair facilitators, and emotion regulators. Across these roles, participants positioned chatbots as moral advisors that evaluate communicative appropriateness and exercise varying degrees of moral authority. Rather than prescribing specific system behaviors, this work offers a conceptual and exploratory account of AI-mediated intervention in intergenerational communication, and articulates key design tensions that arise when chatbots are imagined as socially and morally involved actors in intimate family interactions.

When a person with dementia asks about a deceased partner’s health, caregivers and relatives are often unsure how to respond. These interactions involving reality disjunction are ethically and emotionally complex, with no universal ‘right’ answer. Reflecting on such interactions may help people better understand and navigate these situations. We explore how chatbots can complement humans in supporting such reflection. We designed three chatbots that support reflection through role-play, dilemmas, or analysis, and used them as probes in focus groups with 14 relatives and 10 caregivers of people with dementia. Findings reveal complementary strengths. Humans provide irreplaceable reflective support grounded in contextual, emotional, experiential, and relational involvement. Chatbots’ lack of human qualities offers unconstrained suggestions, evidence-based feedback, and emotionally distanced dialogue without relational consequences. We offer empirical insights into reflective support needs in dementia care and provide design implications for chatbots that complement rather than mimic human reflection.

Large Language Models (LLMs) have rapidly become ubiquitous, demonstrating remarkable proficiency in generating text and responding to prompts. Despite their potential, concerns about privacy and trust persist, yet methods for engaging end-users in designing privacy-conscious AI remain limited. This paper presents a replicable co-design methodology for engaging end-users in privacy-conscious LLM design. Through workshops with 36 participants across three speculative scenarios—mental health applications, travel assistants, and workplace assistants—we demonstrate how combining speculative scenarios with established frameworks (Grice’s Maxims, Schaub’s privacy design space) and trained facilitators enables meaningful participation from users without technical expertise. The co-designed dialogues reveal that users desire dynamic, context-sensitive privacy communication that leverages LLMs’ conversational capabilities. We provide practitioners with a replicable methodology comprising: (1) scenario creation methods, (2) framework scaffolding approaches, and (3) facilitator training guidance, alongside design implications for privacy-conscious conversational AI.

Conversational agents that participate in or mediate group interaction introduce challenges that extend beyond supporting individual users, raising new questions about how agents participate in and influence groups. To characterise this emerging design space, we present a systematic review of 53 peer-reviewed studies on group conversational agents (GCAs). We analyse how GCAs intervene in group-level processes, including participation regulation, conflict mediation, task alignment, and execution support. Using concepts from group research as an analytic lens, we organise prior GCA work around recurring group interactional challenges (orientation, conflict, alignment, and execution), and examine the roles agents are designed to play in addressing these challenges. We find that GCAs are predominantly designed as short-term, role-bounded interventions targeting isolated challenges in bounded interactional contexts. We further identify recurring structural tensions in GCA design, including tradeoffs between visibility and discretion, proactivity and group autonomy, and agent authority and group ownership. Together, these findings clarify how current GCAs are positioned within group interaction, surface the implicit assumptions embedded in their designs, and outline open questions for future research on conversational agents as group-level interventions.

AI writing assistants could help non-experts communicate policy opinions more effectively to policymakers, but risk steering those opinions. Prior work suggests open-ended questions or suggestions as a promising solution. This work offers an alternative perspective. Through a study where policy experts assisted non-experts in writing public comments, we identified the “open-ended suggestion trap”: when experts provided knowledgeable suggestions, non-experts felt their voices were taken over; when experts kept suggestions abstract to avoid steering, non-experts couldn’t act on them. This trap appeared regardless of whether non-experts believed they were interacting with a human or AI, suggesting these challenges are inherent to knowledge-based power asymmetries and will likely transfer to AI systems. These findings suggest that open-ended questions or suggestions, while avoiding bias, can inadvertently disempower non-experts. Drawing on empowerment theory, we discuss alternative directions for designing AI writing assistants in this space: scaffolding to enable action without imposing authority, and linguistic registers that preserve non-experts’ voice.

Robotic welding path programming is commonly framed as a technical problem addressed through expert-oriented tools. This pictorial presents a Research-through-Design (RtD) exploration of an Augmented Reality (AR) interface that allows operators to define seam and tack welding paths directly on physical workpieces using gestural input, while receiving immediate visual feedback on robotic reachability derived from inverse kinematics. This pictorial documents a series of design interactions in which system was progressively refined through deployment and designerly reflection, and examining how spatial interaction and infrastructural breakdowns can influence human understanding of robotic constraints during welding path planning. Through annotated images and reflective accounts of system use, this pictorial contributes (1) an AR-based path planning workflow that supports both seam and tack welding, and (2) design knowledge on how computational and visual feedback mediates embodied human-robot negotiation.

Large-scale assembly through human-robot collaboration (HRC) is increasingly explored in architecture, engineering, and construction. However, prior research in HRC has primarily examined interaction scenarios for tabletop assembly. As a result, we lack task-level insight into how different levels of automation and user interaction shape workflows and user experience in large-scale assembly. To address this gap, we present RoboticTerritories, a phone-based Augmented Reality system for human-robot block assembly that supports three interaction modes: Inference, Mimic, and Realtime Mimic. Each mode offers distinct automated functionalities and user-defined control across stages of a pick-and-place assembly process. We evaluate RoboticTerritories through a formative study (n = 8) and a within-subject study (n = 12) to identify the opportunities and limitations of each interaction mode. Our results show that varying levels of automation provide complementary benefits by supporting different forms of efficiency across modes, indicating a need for multimodal HRC workflows for large-scale assembly.

Reinforcement learning (RL)-based biomechanical simulations have the potential to revolutionize HCI research and interaction design, but currently lack usability and interpretability. Using the Human Action Cycle as a design lens, we identify key limitations of biomechanical RL frameworks and develop MyoInteract, a novel framework for fast prototyping of biomechanical HCI tasks. MyoInteract allows designers to setup tasks, user models, and training parameters from an easy-to-use GUI within minutes. It trains and evaluates muscle-actuated simulated users within minutes, reducing training times by up to 98%. A workshop study with 12 interaction designers revealed that MyoInteract allowed novices in biomechanical RL to successfully setup, train, and assess goal-directed user movements within a single session. By transforming biomechanical RL from a days-long expert task into an accessible hour-long workflow, this work significantly lowers barriers to entry and accelerates iteration cycles in HCI biomechanics research.

Acquiring motor skills, such as dance, is considered a difficult task, as novice learners frequently encounter challenges in promptly comprehending and adjusting their movements in learning contexts. In this study, we present a virtual environment training system that incorporates haptic feedback and generative AI to support novices’ learning of body movements in dance. The system guides the user through haptic feedback in VR, generated from dance sequence data captured from professional choreographers. User studies with novice participants were conducted to 1) establish effective feedback for guidance and 2) evaluate the user’s training experiences using the system. We also conducted comprehensive interviews with participants and experts to confirm the design strategies and seek insights regarding the perception and utilization of virtual environments, along with haptic and visual feedback in dance learning processes. Based on the outcomes, we established the design guidelines for virtual environment training systems that facilitate learning bodily movement regulation, including in dance education.

The quality of piano performance depends on nuanced timing, articulation, and dynamic control, but practice feedback is often summary-based and hard to act on. We introduce Profy, a weakly supervised system that learns from take-level labels derived from aggregated listener ratings (expert-labeled vs. amateur-labeled) to produce time-aligned highlights for review during piano practice. We collected synchronized 1 kHz key-motion and audio from 73 pianists and used 1,083 valid takes for modeling and evaluation. The model outputs clip-level predictions together with evidence scores on a shared resampled model time base for visualization. On 20 amateur clips from short technique studies annotated by 21 expert pianists, the displayed highlight score aligns with passages that expert pianists marked for review despite training without localized labels (Pearson r = 0.61, ROC-AUC 0.75). Rather than summarizing a take with a single global score, Profy helps learners decide where to inspect next by supporting scrubbing, looping, and focused replay of time-localized passages associated with expert–amateur differences.

In this paper, we designed the augmented reality (AR) -based visualization of metaphor that instructors in an Aikido community of practice use in everyday teaching to explain techniques. We deployed these AR-based visual metaphors in regular Aikido club training over a two-month period and examined their use through ethnographic observations and interviews. Our findings reveal three key points. First, instructors felt AR metaphors reduced misunderstandings and stress, although using metaphors created by others diminished agency unless they incorporated their own interpretations. Second, learners grasped successful techniques more quickly, increasing motivation, yet some felt anxious about teaching others later because they had not reasoned through the mechanics. Third, AR metaphors strengthened ties to the originating instructor while weakening connections with on-site members. Based on these findings, we present three design takeaways for deploying AR-based visual metaphors into communities of practice in the real-world.

Although AI assistance can improve writing quality, it can also decrease feelings of ownership. Ownership in writing has important implications for attribution, rights, norms, and cognitive engagement, and designers of AI support systems may want to consider how system features may impact ownership. We investigate how the stage at which AI support for writing is provided (planning, drafting, or revising) changes ownership. In a study of short essay writing (between subjects, n = 253) we find that while any AI assistance decreased ownership, planning support only minimally decreased ownership, while drafting support saw the largest decrease. This variation maps onto the amount of text and ideas contributed by AI, where more text and ideas from AI decreased ownership. Notably, an AI-generated draft based on participants’ own outline resulted in significantly more AI-contributed ideas than AI support for planning. At the same time, more AI contributions improved essay quality. We propose that writers, educators, and designers consider writing stage when introducing AI assistance.

Science communication revision requires writers to dynamically balance scientific exposition and narrative engagement – a process where writers often struggle with competing directions. Existing LLM-assisted tools help with co-writing, but offer limited support for navigating this iterative, multi-directional revision process. To address this gap, we designed Spatial Balancing, an exploratory revision environment that maps rhetorical goals and revision strategies onto a two-dimensional spatial canvas for experienced science communication creators with domain expertise but lacking formal professional training. By building a design space of communication strategies and embedding them into a spatial exploratory canvas, our system treats feedback as navigational cues rather than prescriptive judgments. Our findings show that this integrated revision environment helps writers stay focused on writing goals, reason about revision as trajectories, and explore alternatives, which supports greater metacognitive control and confidence without increasing workload. This work highlights the value of spatially externalized revision environments for supporting iterative, reflective thinking during LLM-assisted writing.

AI memory is becoming central to AI systems that aim to support personal and professional work. Yet, designing interfaces for AI memory is not well understood. How should we design for user interaction with AI memories—for instance, what kinds of operations might users want to perform on memory, either now or in the future? We survey the design of tools and interfaces around “AI memory” to chart a design space of common design patterns, architectures, and operations, and identify dominant metaphors and gaps in the space. Then, we apply generative metaphorical design to expand the design space for AI memory, exploring less-dominant metaphors of software version control, Zettelkasten, requirements, personal diaries, community archives, cultural probes, and science fiction. Our work offers rich opportunities for gaps and emergent needs that future interfaces for AI memory might address.

AI agents are increasingly explored for supporting reflection and well-being. However, we know little about how AI agents participate in reflective practices, particularly through co-creation. We presented MIRA, a co-creative AI agent that engages users in transforming abstract squiggles into concrete drawings while offering reflective feedback. Through a three-group comparative study, we examine how AI-mediated co-creation shapes reflective experience. We found that MIRA operates as a scaffold that introduces external reflective perspectives, helping users reinterpret experiences and surface emotions through visual expression. These findings highlight how AI agents can foster engaging co-creative experiences that encourage reflection among university students, while providing insights for designing AI-human co-creation that can extend to broader populations.

Occupational therapists who support students with significant brain-based disorders are in a unique position to help identify students’ design requirements for custom adaptive switches, but often lack the technical training and/or resources to create these devices. As part of a multi-year collaboration between HCI researchers and an occupational therapist, we conducted a practitioner-led study to examine how generative artificial intelligence (GenAI) can support rapid prototyping and fabrication of switches for students with brain-based disorders in a specialized school setting. With a methodology that combines narrative inquiry with speculative design, we analyze our reflective design conversations to identify breakdowns and new opportunities for GenAI to streamline tasks within this complex design environment. We conclude with guidance and considerations for future AI tools, emphasizing that although AI use can accelerate fabrication, ethical and effective design requires contextual judgment, clinical reasoning, and technical verification.

How could AI enter a deeply value-laden realm of human lives? Drawing on key values and practices associated with praying identified through a diary study, we presented our participants with four speculative, conceptual value-sensitive AI systems to “assist” prayer practices. The conceptual designs served as provocations to co-reflect on how AI interventions might shape their praying experiences. Our findings suggest that a sense of authenticity (or a genuine connection to the divine) is a crucial value, while the mere presence of AI was often perceived as diminishing this authenticity, particularly when AI assumed too much agency in guiding prayer practices. Based on our findings, we argue for the importance of AI agent designs that recognize users’ agency in shaping their interaction with AI. We further explore how this may be possible by leveraging interpretive openness, perhaps through AI’s inexplicability as a resource for personal meaning-making, and by recognizing non-use of AI as a legitimate design choice.

Creative work with generative image models is typically mediated through prompts, often requiring designers to translate visual and material intentions into words. Such translation can have a constraining effect on creative tasks in visual domains such as fashion and textile design. To understand how designers make sense of the material of AI based on its interpretable properties, we let users interact with the technology by manipulating the neurons that lie in its hidden layers. In two material-driven design workshops, we introduced fashion and textile practitioners to the technical material properties of a model trained to generate fashion imagery. We found that the interaction leads to new forms of material experiences by offering a gateway into AI’s otherwise implicit functioning and discuss the how thinking hidden layers might support intuitive rather than interpretative control when designing with AI, leading to more active material experiences.

Sound has long been used to enhance eating experiences, and increasingly, we know sound can shape how we perceive taste. Yet intentionally composing sounds to modulate taste remains underexplored in cross-sensory HCI design. While prior research has mapped individual auditory parameter (e.g., pitch, timbre, rhythm) to basic taste perceptions, less is known about how holistic sound compositions combining multiple parameters can be systematically designed and evaluated. We present a human-in-the-loop workflow that brings together professional sound designers and generative AI to create 160 sounds with taste intentions informed by established auditory-taste parameters. In a study with 149 participants on sound-taste correspondences, we identify sounds that strongly correspond to basic taste words. Our contributions are threefold: (1) a workflow for designing taste-matching sounds; (2) a curated repository of reusable sound stimuli for cross-sensory research; and (3) a designerly framework for creating compositional taste-matching sounds alongside insights into sound-taste word correspondences and association strategies, offering implications for cross-sensory HCI.

Food texture plays a crucial role in the overall sensory experience and the functional properties of food, evolving dynamically from the first bite to the final chew. Traditional methods for modifying food texture, such as adjusting cooking parameters, often compromise other key attributes, including appearance and nutritional value, whereas existing computational approaches largely treat texture as a static, monolithic attribute. This paper presents a computational method that modulates the internal structure of food to design temporal food texture experiences. Our method generates a silicone mold based on the desired food texture experience. This approach decouples textural properties from visual and amount attributes, enabling independent tuning of sensory factors during different oral processing stages. We characterize the relationship between internal structure and temporal texture perception, specifically finding that infill pattern and shell thickness control the bite and chewing phases, respectively. We present an interactive design interface that allows users to create food items tailored to specific temporal food textures.

Mixed Reality (MR) aims to blend digital and physical worlds, but the absence of haptic feedback often breaks visual-tactile consistency. We introduce Prop-Chromeleon, a MR system based on generative artificial intelligence (AI) that dynamically transforms everyday objects into adaptive passive haptic props through user-provided text prompts. Our AI pipeline performs generation and anchoring of virtual assets that align with the shape of physical props, allowing us to study how virtual content generation behaves under geometric and prompt-based constraints. We evaluate Prop-Chromeleon’s effectiveness through a generation study using varied object shapes and user prompts, combining quantitative shape similarity metrics with qualitative prompt fidelity analysis. Our user study further showcases Prop-Chromeleon’s improvements in perceived realism, immersion, and enjoyment compared to static baselines. These results show that shape-aware generation can support both believable haptic interaction and creative engagement in MR.

Dietary counseling (DC) is effective for managing non-communicable diseases but faces scalability challenges due to reliance on self-reporting. While automated nutrition assessment has advanced, how to use passively captured dietary data to support dietitians’ decision-making remains underexplored. We use food purchase data (FPD) from loyalty cards as an example of non-standard dietary data in clinical practice. Building on multi-year digital nutrition research, we identify key FPD requirements regarding data availability and nutritional factors through a dietitian workshop (N=7) and survey (N=18). Based on derived insights, we design and evaluate DietCoach, the first dietitian decision support system integrating patient FPD and basic health data. Using DietCoach, 19 dietitians and 6 dietetics students provided dietary recommendations for three patient profiles with different FPD availability. The recommendation variances highlight the importance for system flexibility. Participants found DietCoach user-friendly and acceptable in their workflow, with no observed differences related to patient FPD availability in our evaluation. Our findings provide design insights for integrating opportunistic dietary data into clinical practice.

What if we deliberately designed computers to be creepy? This pictorial explores using Design Fiction to create a collection of speculative creepy devices that we could imagine living with. We investigated how leveraging creepiness as a normalised design attribute could serve as a resource to reflect on what makes good user-centred design. During a span of nine weeks, we engaged in sharing our personal lived experiences as a prompt to imagine creepy futures and devices that could address our needs, albeit designed to be creepy. This approach enabled us to evoke experiences that have not happened with devices that do not exist, which facilitated a reflection on their impact on everyday choreographies and tensions on their usability through speculative autoethnographies. We contribute a set of imaginary devices from creepy futures that illustrate the exploration and adoption of creepy speculations to facilitate new critical discourses to rethink designing interactive systems.

This paper introduces Technology Redux, a new methodological approach in HCI that seeks to reenact past technological experiences in today’s everyday life, critically reflect on the roles and impacts of present technologies, and provoke new perspectives on future directions of technological development. Building on reflections from, BeeperRedux, a case study that recreated the 1990s beeper as a smartphone application, we present four practical strategies for enacting Technology Redux: (1) reproducing experiences beyond replicating past technologies, (2) balancing friction for provocation and everyday integration, (3) integrating personal narratives as research resources, and (4) surfacing the sociocultural and infrastructural contexts. We argue that this methodology uniquely integrates the perspectives and strengths of the historicist approach, speculative design, and research products, introducing a critical and actionable research practice for HCI.

HCI increasingly engages with the concept of time, with many studies focusing on how temporal relations are produced and reconfigured through computing technologies. However, less attention is paid to the impact of temporality on researchers themselves and specifically their capacity for ethical reflection and action. This paper explores the interplay between temporal features and ethical reflection in HCI research projects. Through a reflective multi-ethnography of nine research projects, we examine how temporal features can influence ethical capacity across the lifecycle of research. The study employs infrastructural and chronopolitical analysis (after Star and Sharma) and offers a range of considerations for researchers to help activate greater temporal awareness for everyday wisdom, to acknowledge temporal power, mobilise responsibility, counter isolation in interdisciplinarity, and promote collective action and dynamic methodologies for temporal research. The study responds to calls for more ethnographic approaches to temporality that address specific HCI needs and focus on practice.

Speculative and systemic design are both used by HCI researchers to engage in complex sociotechnical change. However, they are rarely integrated in ways that make their complementary strengths explicit. This paper introduces Systemic Futures Dialogue, a design approach that interleaves speculative and systemic design methods across micro-macro and present-future dimensions. We report on an 18-month case study with the Architecture, Engineering, and Construction (AEC) industry, that applied a mixture of methods used in both design disciplines. These included semi-structured interviews, systems mapping, future-based scenarios, speculative probes, and participatory reflection. The resulting design approach generates grounded futures by connecting macro-level system dynamics with micro-level speculative critique, identifying tensions between present-day solutions and desired futures. The final Systemic Futures Dialogue contributes methodological guidance for conducting critical, participatory design work within a sociotechnical system.

Heritage buildings are increasingly adapted to contemporary needs through technological interventions, yet these approaches remain human-centred, leaving limited recognition of the buildings’ own capacity to shape architectural change. Drawing on critical heritage and more-than-human perspectives, we investigate how heritage buildings can be approached as agentic actors in negotiations of spatial and technological adaptation. Through thing-interviews with ten architects, speaking from the perspective of the heritage buildings they designed, we examine how buildings express self-value, engage with social infrastructures, and “sense” reshaping through material and functional transformations. We extended these insights through a design fiction session with four experts, exploring future scenarios for human-nonhuman connection, fragmented reconfiguration over time. From this, we propose three implications for designing more-than-human futures in intelligent built environments with heritage values: (1) negotiating consent and power asymmetries, (2) framing stewardship through care and companionship, and (3) rethinking authenticity beyond originality.

In the pursuit of more-than-human approaches to designing it is a challenge to understand the plurality of more-than-human worlds, especially the multiplicity of the more-than-human lives or biographies of things designed. In this pictorial, we experiment with speculative mapping as a process to explore pluralities of a thing we designed, called wi-fi-no-wi-fi. Using the wi-fi-no-wi-fi as a case study, we speculated through workshops, empirical investigations, and speculation to create speculative maps of different biographies. This pictorial offers three speculative maps and revealed design insights into materials as time, forming of assemblages and spaces, and multispecies perspectives of regeneration.

Technologies for older adults largely focus on monitoring, safety, and functional support, with less attention to everyday emotional meaning-making. Reflective practices can support emotional wellbeing in later life, yet reflective tools tailored to older adults, particularly in India, remain limited. Self-perceptions of aging (SPA) are strongly linked to emotional wellbeing, but SPA work largely relies on questionnaires and clinical formats rather than culturally grounded tools for reflection. This pictorial presents a research-through-design process leading to a culturally grounded reflection tool for older adults in India. It combines SPA-based reflection cards and prompts with non-diagnostic reflective guidance drawn from a curated corpus rooted in cultural understanding. The work co-designed SPA reflection cards grounded in older adults’ metaphors and narratives, and developed the guidance system with older adults and domain experts. It contributes design knowledge on operationalizing SPA in culturally attuned, technology-mediated reflective tools for older adults in urban India.

As digital banking increasingly replaces face-to-face financial services, older adults face growing challenges in navigating self-service and mobile platforms. This issue is particularly salient in Hong Kong, where a highly digitalized yet fragmented multi-channel banking ecosystem combines branches, ATMs, mobile apps, and phone banking. While prior research has identified general barriers such as usability and trust, less is known about how banking practices, challenges, and support needs differ across stages of later life. We address this gap through a mixed-methods study in Hong Kong, combining an in-person survey with 151 adults aged 60+ and semi-structured interviews with older adults and frontline bank staff. Participants were grouped into young-old (60–69), old-old (70–79), and oldest-old (80+) cohorts. Our findings reveal clear age-related patterns: young-old adults actively use ATMs and digital banking but report strong psychological concerns; old-old adults rely on hybrid channel use and face increasing knowledge-related barriers; and oldest-old adults depend primarily on physical branches due to compounded physical and cognitive limitations. We conclude with age-specific design implications for more inclusive digital banking systems.

Investment decisions are often driven by time pressure and emotion, leaving investors vulnerable to cognitive biases. Mobile trading apps intensify these tendencies, yet existing interventions rely on external constraints that fail to foster lasting behavioral change. We investigate how reflection-centered approaches support mindful decision-making across a spectrum of investor expertise. We present MindStock, a technology probe providing principle-anchored feedback by integrating user-defined principles with behavioral data mirroring trading patterns. In a 6-week field study with 16 investors, we found that meaningful reflection comes from the tension between principles and behavioral data. Principles give context to otherwise opaque metrics, while data keeps principles from drifting into vague self-assurances. This pattern varied by experience: novices gravitated toward normative rule-setting, while experienced investors used the system to test and refine their own assumptions. We contribute design implications for supporting reflection in high-stakes decision-making contexts.

This study explores how British-Chinese immigrants sustain cultural resilience through heritage practices and the role of interactive technologies in this process. Using a two-phase mixed-methods approach, it begins with a scoping study (n=150) to map patterns of heritage engagement, and then employed interviews with 11 immigrants and scenario-based workshops with 12 stakeholders to investigate technology-mediated dissemination. Findings show that intangible cultural heritage, particularly foodways, language and festivals, plays a central role in identity formation and intergenerational cohesion, with community networks mediating resilience. However, heritage practices and technologies remain largely internally-focused, while fragmented platforms and linguistic barriers limit intercultural exchange. To address these gaps, the paper proposes the Community-Centred Mutual Acculturation (CCMA) framework, emphasising community authorship, plural representation, and cross-cultural engagement through modular platforms, youth-led co-creation, and immersive multi-sensory interaction. This research contributes empirical insights and design-oriented recommendations for interactive systems that support inclusive, heritage-based engagement and mutual acculturation.

As Extended Reality (XR) transitions from specialized domains into domestic settings, industry narratives envision a future of seamless, ubiquitous integration. Yet these imaginaries, largely shaped by Western contexts, often overlook the complex social organization and material constraints of diverse households. To address this gap, we examined how individuals in Singapore interpret XR in everyday life. Using value scenarios as sociotechnical imaginaries, we conducted a survey (N = 206) and interviews (N = 22). We identify two layers of mental models: (1) perceived immersion and (2) XR’s roles in domestic life. Through these models, participants conceptualized XR as a situational fix for overcoming spatial, temporal, or relational constraints, or as an enabling medium for connection, expression, and wellbeing. We argue for a shift from functional optimization to relational maintenance, and propose design considerations for XR as an episodic, situational utility that supports the home.

Wearable technologies offer significant potential for promoting active ageing, yet their design often overlooks the embodied preferences of older adults. This study employs movement-based co-design to explore how aesthetics and materials serve as affordances for age-inclusive wearables. Through workshops with older adults (n=23) and movement experts (n=13), we investigated aesthetic meaning-making and material preferences for wearables used in physical activity. Older adults prioritised discreet designs that respect personal style and health histories, valuing materials that support somatic flow and physical autonomy. Conversely, movement experts emphasised integrating everyday movements with high-performance, skin-friendly materials to ensure safety and confidence. By identifying the design tensions between these stakeholders, specifically regarding visibility, support modalities, and system authority, we propose tangible design considerations for age-inclusive wearables that aesthetically resonate with older adults’ identities and sense of agency.

Haptic technologies have advanced rapidly, yet exploration of robotic touch remains dominated by replicating realistic environmental cues or hand gestures, which narrows the design space and risks social resistance. This paper argues for alternatives: grounded in the notion of “otherness” from human-robot interaction (HRI), we propose treating robotic touch’s inherent otherness as a design quality. Instead of being a limitation in pursuing realism, otherness can be embraced to elicit ambiguity and provoke alternative interpretations, fostering expressive and evocative robotic touch design. To develop this perspective, we analyze inspirational art and design precedents and four design research cases through a reflective Research through Design (RtD) approach. Through this analysis, we articulate a set of design languages structured around why otherness matters for touch meaning-making, how it can be shaped through design strategies, and where it can be embedded within robotic touch systems. We conclude by reflecting on the tensions and risks involved in designing robotic touch with otherness in mind.

While performing arts expertise plays an important role in social robot design, human–robot interaction designers often lack concrete guidance on integrating performance-based techniques into design practice. Drawing on Augusto Boal’s notion of de-mechanisation from Theatre of the Oppressed, this work uses playful movement exercises to help designers disrupt habitual bodily patterns and explore imaginative non-anthropomorphic robotic forms and movement. We propose an embodied ideation method that combines movement exercises derived from Boal’s practice with a wearable body-extension toolkit, and test it in a workshop focused on designing an emotionally expressive two-cuboid robot. Using interpretative phenomenological analysis (IPA), we examine how participants experienced bodily disruption, reconfigured movement perception, and developed expressive, affective, and imaginative understandings through the interplay of form and movement. Based on these findings, we evaluate the method’s effectiveness and articulate how it extends embodied design methods for expressive movement-centric HRI.

As robots are increasingly designed to take on relational qualities, new methods are needed to explore how their form, movement, and presence shape connection. We introduce puppetry as an embodied design practice in which designers and users inhabit and control physical prototypes to examine relational qualities before technical implementation. We ground this in equine-assisted interventions, where horses act as relational partners: their unpredictability, reciprocity, and embodied risk require humans to attune and regulate emotions. We utilised puppeteering to re-enact these dynamics safely through movement, materiality, and interaction, surfacing how such qualities may inform relational robots. Our findings show that puppeteering functions as a generative practice: movement becomes inquiry, playful performance reveals affective states, and low-fidelity prototypes enable safe exploration of relational complexity. Puppetry thus offers a pathway for developing robots as autonomous, equal partners; revealing insights into agency, co-embodiment, and materiality, often overlooked in traditional prototyping.

Soft floating robots (SFRs) represent a shift from rigid machines, offering gravity-defying, compliant, and tactile embodiments for indoor cohabitation. However, their development remains fragmented across isolated prototypes, lacking a coherent design vocabulary. Without a systematic understanding of their interactional capabilities, designers struggle to leverage SFRs’ unique affordances, and these systems often remain limited to novelty applications that are difficult to integrate into everyday life. To address this, we propose a design space for interaction with SFRs. Informed by an exploratory study with 12 experts from HCI, Design, and Robotics, we identify ten design dimensions spanning physical, interactive, and behavioral properties, along with a range of application scenarios. We further present proof-of-concept design examples to demonstrate how this design space can support diverse interaction possibilities. This work contributes a structured framework for understanding and designing interactions with SFRs, supporting their integration into everyday indoor environments.

Human-scale inflatable robots that respond to users’ movements offer a unique opportunity to explore embodied and relational interaction. Yet, little is known about how their form and interactive behaviour shape engagement and perception. This study presents three inflatable robots designed to explore different levels of user control: Barrel affords high user freedom, allowing approach or retreat; Chair introduces partial constraint through a semi-enclosing structure; Jacket envelops the user, with movement largely determined by the robot. Across all systems, robot motion responds to user behaviour in semi-random ways, contributing to a sense of agency. Participants engaged through a think-aloud protocol, followed by semi-structured interviews and a drawing activity. Findings show that, in the absence of clear prior models, participants actively constructed interpretations of behaviour, attributing agency and intention, while variations in control shaped trust and relational dynamics. These results offer insights for designing human-scale soft robots that support rich, embodied interaction.

While multimodal generative AI can support creative activities, users often struggle to prompt models to achieve desired aesthetic, acoustic, or stylistic characteristics. Besides, existing generative models are predominantly driven by text-based prompts regardless of their output modality, i.e., using text-based prompts for creating images, videos, and sounds, which often leads to high prompt uncertainty. In response, recent multimodal AI systems introduce interaction strategies to better align model interpretations with users’ creative intent, but the growing variety of strategies makes it hard to judge what works for a given use case. We address this gap with a systematic literature review (n=71) that categorizes prompt-uncertainty-reduction strategies into six types: Guiding Prompt Construction, System Refining of the Prompt, Direct Manipulations of Output Elements, Explaining Reasoning about Prompt Interpretation, Displaying Multiple Outputs, and Controlling Modifier Contribution. For each type, we summarize mechanisms, benefits, and challenges, enabling more efficient navigation of the prompt-support design space.

Podcasts have become a primary medium for information access and informal learning. However, mainstream audio players still rely on time-based navigation, treating listening as moving through time. This mismatch forces listeners to translate comprehension regulation intentions into trial-and-error seeking on the timeline. While generative AI enables rich semantic analysis of long-form audio, existing tools often demand visual attention and fine-grained interaction, which is a poor fit for mobile, multitasking listening. We present Generative Semantic Audio Navigation (G-SAN), a prototype that brings support for comprehension regulation from the information layer to the control layer by remapping familiar playback controls into meaning-oriented actions over semantic units for local clarification, main-thread grasping, and global orientation. A user study shows that participants were able to understand and adopt these controls during real-time listening, and generally perceived them as helpful for regulating understanding, while also revealing boundary conditions. We conclude with design implications for meaning-oriented audio navigation.

Collocated collaboration remains a preferred mode of working together, yet current systems often depend on users to create and configure shared content. This places individual effort and attention on managing workspaces rather than engaging in collective sensemaking. We present a mixed-initiative interaction mechanism that automatically transforms ongoing conversation into manipulable shared materials, shifting externalisation from an explicit individual task to a by-product of discourse. We implement this mechanism in CollEagle, an interactive tabletop system that continuously generates candidate content from speech and enables users to curate, organise, and repurpose it for joint activity. A user study shows that automating material production reduces configuration work, supports group coordination, and foregrounds shared meaning-making. We contribute (1) a concrete implementation of a mixed-initiative interaction mechanism for collocated collaboration, and (2) design insights for systems that integrate implicit automation with explicit manipulation to better support joint work.

Authoring presentation slides involves navigating contextual constraints that shape how content is structured, adapted, and reused. While prior work frames constraints as limitations, little is known about how presenters actively reason about them. We conducted a formative study with ten presenters to examine how constraints emerge, are interpreted, and influence authoring decisions, leading to the Constraint-based Multi-session Presentation Authoring (CMPA) framework. CMPA treats time, audience, and communicative intent as key constraints shaping authoring. We instantiated CMPA in ReSlide, a research prototype for constraint-aware slide creation and reuse, and conducted two user studies on (1) single-session behaviors and (2) multi-session workflows. Compared to a baseline tool, ReSlide helped presenters treat constraints as active design drivers that guide narrative construction. The second study further shows how presenters flexibly reuse and adapt content across authoring cycles as constraints evolve. We then propose design implications for future constraint-aware presentation tools.

Slide presentations are central to classrooms and conferences but remain inaccessible to blind and low vision (BLV) audiences. Presenters rarely describe visual content or announce slide changes, leaving BLV participants with fragmented access. We present a delay-buffered editing approach that trims redundant speech and inserts concise slide descriptions at transition points in recorded presentations, operating within a five‑second buffer. An exploratory study showed that trimming filler speech created sufficient space for descriptions and that added descriptions improved comprehension and orientation. Building on these findings, we developed an automated editing pipeline that processes recorded presentation videos and evaluated it with 12 BLV participants. Edited audios improved comprehension (from 50.0% to 88.9%), slide detection (from 16.7% to 100%), and recognition of visual elements, while participants also noted challenges in timing and prosody. These results establish delay-buffered editing as a promising approach for enhancing accessibility of recorded presentations with slides, and suggest design directions for future live deployment.

Accessibility in screen media is typically framed as services for discrete user groups (e.g. visually impaired users), with HCI work largely focusing on post-production and playback interventions rather than the wider production ecology. We report findings from interviews and workshops with media practitioners spanning pre-production, production, and post-production, surfacing a gap between practitioners’ orientation toward meeting audiences where they are and the organisational conditions that systematically prevent this. Our thematic analysis shows how access is (a) authored into storytelling and planning when time and creative ownership allow, (b) undermined by fragmented responsibility and remit boundaries across handovers, and (c) constrained by workflow realities and product economics that shape what can be delivered, maintained, and scaled. We also surface frictions between creative intent and end-user comprehension, and between device-level personalisation and source-level authorship. We contribute a design agenda for integrated media accessibility, including principles for making space and time for access, supporting inclusive viewing, and enabling accountable content creation and automation.

This pictorial explores breathing as an embodied haptic modality for enhancing immersion, presence, and emotional engagement in virtual reality (VR). We present the iterative development of a wearable pneumatic system that generates sensations of rhythmic chest movement on the torso. This feedback simulates breathing patterns such as slow breathing, rapid breathing, gasping, and coughing, designed to synchronise with events in both passive (360° narrative) and active (interactive gameplay) VR experiences. We unpack findings from a mixed-methods study with fifteen participants evaluating the system. Quantitative results showed higher immersion and embodiment scores in passive scenarios but limited improvement in active scenarios, suggesting that attentional demands during gameplay reduces sensitivity to haptic breathing. Qualitative feedback highlighted that simple breathing rhythms were readily recognised and emotionally meaningful, while more complex patterns proved less distinct. We contribute a novel haptic system and empirical insights for designing immersive and emotionally engaging breath-based feedback in VR.

Virtual reality (VR) holds potential for music performance training by simulating realistic performance environments that evoke audience-related pressure in controlled settings. While progress has been made in creating immersive VR concert spaces, little research has examined the experience of musicians or the design considerations that support its use for training. Using a research-through-design approach, we conducted a two-phase study with 12 student musicians performing in a custom-built VR concert hall simulation. Through iterative design and performer feedback, we generate new insights into performer-centred research and identify design considerations, laying the groundwork for future development of music performance training.

Designing immersive technologies for older adults often overlooks the situated nature of community-based activities. While Virtual Reality (VR) offers promising affordances for meaningful enrichment, its integration remains underexplored in community musicking. We conducted a two-month field study at a Chinese Continuing Care Retirement Community (CCRC), collaborating with 15 older adults and 3 music therapists through observations, interviews, and envisioning workshops. Our findings identify how older adults and therapists perceive VR through the lens of established musical values, including self-cultivation, social connectedness, and emotional and aesthetic resonance. We identified polarized responses to immersion shaped by personal history, and a recognized potential for VR to support musical imagination and reinterpretation beyond mere functionality. We discuss design considerations that emphasize therapists’ roles as gatekeepers, the need for culturally meaningful content, and responsive interactions to support the inclusive and sustainable deployment of VR in managed care settings.

Death is a fundamental aspect of the human condition, shaping how people understand life and existence. Virtual reality (VR) has increasingly been used for virtual engagements with mortality in controlled lab environments, but there remains a limited understanding of how to design and facilitate such experiences in public settings, such as art galleries, where designers must balance interpretive openness with accessibility, safety, and duty of care. This research presents a situated case study of Passing Electrical Storms, a VR art installation by Shaun Gladwell that simulates death and an out-of-body journey through the universe, and analyzes semi-structured interviews with 30 participants about their experiences with the installation. Using Reflexive Thematic Analysis, we examine participants’ experiences, perceptions, and interpretations to explore how the installation supported contemplative engagement with mortality through stillness, pacing, and multi-sensory grounding. Our findings highlight how meaning was co-constructed through bodily sensation, personal belief, and prior experience, and we propose design strategies for public mortality-themed VR that support interpretive engagement while maintaining legibility, emotional safety, and appropriate care in real-world deployment.

The design fiction film Interfuit depicts near-future virtual funerals in Japan. This paper reports audience feedback collected through public screenings of the film and analyzes these responses to identify design opportunities for future funerary practices and related forms of mourning, memorialization, and remembrance. We organized screening events for a wide range of audiences, including HCI and other researchers, medical and end-of-life care workers, religious professionals, funeral service providers, and film enthusiasts. While most participants were based in Japan, we also intentionally reached out to people living abroad to capture more diverse perspectives. In total, we collected 140 open-ended responses, conducted a qualitative analysis, and derived design opportunities grounded in viewers’ reflections. Through this process, the paper contributes not only insights into the design of virtual funerals but also a methodological perspective on how design fiction films can be effectively used in HCI design research.

We examine how people experience two choices in the design of generative ghosts, AI systems that are trained on data of the dead: representation, where an AI speaks about a deceased person in the third person, and reincarnation, where the AI speaks as the deceased in the first person. Through a qualitative user study with 16 participants, we explore how each shaped authenticity, affect, and risk. Reincarnation was preferred for its immediacy, but participants shared fears of over-reliance. Representation was preferred for engaging with memory over conversational presence, though participants often ignored this distinction, engaging in dialogue despite third-person framing. Across both modes, participants privileged affective resonance over factual fidelity. We conclude by showing how factors such as tone, language, and conversational rhythm – factors unique to the user’s memory of the deceased – shape interactions with generative ghosts, and argue that those interactions are always collaborative.

Generative artificial intelligence (AI) is reshaping creative work; however, its integration is neither uniform nor inevitable. We examine video game development, a multidisciplinary domain spanning art, programming, narrative, and design, as a critical site where workers actively negotiate the AI integration. Drawing on interviews with game creatives and informed by a political economy lens, we trace the structural conditions shaping AI adoption, the strategies creatives employ to selectively incorporate AI while maintaining critical distance, and the tensions produced by capitalist imperatives that provoke their resistance to AI. We articulate design tensions and the politics of AI integration in game development and adjacent creative domains. In doing so, our study intervenes in AI design discourse by challenging narratives of technological inevitability as well as labor and value extraction, towards envisioning possibilities for creative AI grounded in human agency.

As interactive systems shift toward probabilistic, generative models, algorithmic bias evolves into implicit hallucination and epistemic erasure, creating ecosystemic ripple effects. This paper proposes Restorative Critical Play, a theoretical design framework in practice that looks beyond interaction to interrogate and repair the epistemic violence within Generative AI. Drawing on Critical Design and Black Game Studies, the work argues that designers must expose the entangled realities of generative outputs rather than merely subverting mechanics.

We introduce three design patterns, Algorithmic Inoculation, Counter-Narrative Jamming, and Data Visceralization, to transform the AI black box from an oracle of truth into a site of critical inquiry. These work to inspire detection, motivation and correction. By reconfiguring users from passive consumers into active auditors of systemic bias, these patterns make visible the cultural forces obscured by frictionless interfaces. Ultimately, this work demonstrates how play can function as community repair, equipping users to navigate and reshape the complex sociotechnical impacts of emerging

While the number of people using AI is growing, the number of people making core AI decisions remains limited. Advocates call for opening up the development of algorithmic systems to a wider range of perspectives, interests, and methods, with particular attention to racial exclusions and harms. This paper responds to this suggestion with two design fiction workshops where 10 Black American participants imagine futures with and against AI. We introduce Exquisite Tellings, selectively reading in-progress stories while co-developing design fiction plots. Across both workshops, participants repeatedly imagined moments of technological failure, including algorithmic breakdowns and mechanical malfunctions. Rather than signaling collapse, these failures surfaced forms of resourcefulness, enabling characters to reconnect with personal and collective capacities obscured by automation. We argue that analyzing specific instances of ‘hopeful failure’—where challenges in AI development reveal broader social possibilities—can help scholars and critics better understand the emerging effects of AI on society.

Design Fiction is a practice that explores and critiques possible futures through diegetic artifacts. Generative Design Fiction extends this practice by using generative AI models, such as LLMs, to produce such prototypes. These models are known to be biased by popular, often Western-centric notions, skewing the imagined futures they generate. This paper explores LLM fine-tuning as a design intervention for reconfiguring these biases. Focusing on futuristic news articles—a form of diegetic prototype that renders futures through familiar journalistic conventions—we fine-tuned an LLM using a corpus of online technology news from the Global South and generated paired articles using the base model and the fine-tuned variant. Our analysis reveals observable shifts in narrative salience—the implicit prioritization of what is treated as central and consequential within a narrative. We discuss these findings through the responsibilities and politics of shifting narrative salience, Generative Design Fiction as a contrastive practice, and LLMs as malleable design materials.

Generative AI (GenAI) is rapidly transforming design education, but universities primarily govern GenAI through macro-level policies that may fail to match micro-level course practices, leaving students and instructors without practical guidance. We reviewed 23 top-ranked university GenAI policy documents and 48 empirical studies of GenAI-integrated design courses, using inductive analysis to build paired thematic frameworks and then comparing policies and practices. Our findings reveal strong alignment around academic integrity, transparency, and the emphasis on critical thinking and human judgment. However, gaps appear with policies prioritizing institutional risk governance and academic writing support, whereas practices emphasize pragmatic benefits and iterative multimodal production. Additionally, policies stress independent learning and lifelong skill development, while practices highlight creativity and efficiency alongside persistent inquiry bottlenecks, overreliance, and fixation. We provide a macro-micro analytical lens linking policy to practice, integrative frameworks grounded in student and instructor perspectives, and actionable implications for university governance and course design.

Conflicts are inevitable in collaborative work and can harm team outcomes when poorly managed. While AI is increasingly envisioned as a third party in teamwork, potentially acting as a customizable and knowledgeable mediator, its design and role in such situations remain unclear, especially regarding cross-cultural expectations. To explore these expectations, we conducted five speculative design workshops in Germany and Japan and synthesized scenarios, sketches, storyboards, and reflections. We use these materials as speculative probes into acceptable AI involvement in team conflict, identifying recurring intervention patterns around when AI should step in, which roles it may take, and what actions are appropriate. We also reveal cross-site differences: German participants more often envisioned low-salient AI guiding resolution, whereas Japanese participants imagined brief, bounded interventions by embodied AI. From these insights, we derive design lenses for AI-supported conflict mediation that preserve human agency and reflect on implications for cross-cultural Research through Design.

As social robots enter educational settings, how they are positioned as new social actors within existing teacher-student pedagogical relationships becomes an important yet underexplored question. They may be shaped by existing relationships, while also having the potential to reconfigure them. In this study, we explore the relational positioning of educational social robots through a drawing-based design activity with teachers. By analyzing the pedagogical configurations imagined by teachers, we reveal how teachers assign roles, responsibilities, and degrees of authority to robots, in relation to themselves and students. Building on these findings, we discuss the design implications for future educational social robots, highlighting the importance of considering how robots are added into and reshape pedagogical relationships.

As robots become increasingly integrated into everyday environments, intuitive communication paradigms such as natural language and end-user programming have become indispensable for specifying autonomous robot behavior. However, these mechanisms are ineffective at fully capturing user intent—natural language is imprecise and ambiguous, whereas end-user programming can be overly specific. As a result, understanding what users truly mean when they interact with robots remains a central challenge for human-AI communication systems. To address this issue, we propose the Distill approach for human-robot communication interfaces. Given a task specification provided by the user, Distill (1) removes unnecessary steps; (2) generalizes the meaning behind individual steps; and (3) relaxes ordering constraints between steps. We implemented Distill on a web interface, and through a crowdsourcing study, demonstrated its ability to elicit and refine user intent from initial task specifications.

Programming social robots is challenging for novice robot programmers due to required expertise in planning, interaction design, and programming. While large language models (LLMs) hold significant promise through code generation from natural-language descriptions, they can obscure critical elements of programming and supplant designer intent, eventually resulting in over-reliance instead of developing programming skills. In this paper, we explore how LLM-based social-robot-programming tools can support novice robot programmers through a Research through Design (RtD) process. We designed and prototyped Robo-Blocks, a block-based programming environment that leverages LLMs to offer novice robot programmers generative scaffolding through structured narratives that connect high-level ideas to executable robot behaviors. Through deployment with novices, we discovered emerging user personas and usage patterns for generative scaffolding and showed how this scaffolding shapes end-user design and programming strategies. We present design insights for the effective use of generative scaffolding and its integration into the practice of social-robot programming.

We present CoStage, an AI-supported embodied co-creation platform that combines LLM-based story generation with multi-robot stage performance to support children’s narrative construction and spatial imagination. Unlike prior AI storytelling tools that are largely screen-based, CoStage allows children to direct robot actors on a 360° tangible stage, transforming written stories into spatially enacted performances. Informed by formative work with domain experts, we evaluated CoStage in a within-subjects study (N = 24) comparing a robot enactment condition with a screen-based animation condition. Our findings indicate that robot-stage performance can heighten immersion, strengthen children’s sense of directorial agency, support spatial sensemaking, and foster a planning–enactment coordination loop during storytelling. This work advances child–computer interaction by offering design implications and demonstrating how embodied AI co-creation can support children’s spatial cognition and narrative engagement.

Generative AI shifts interaction toward intent-based outcome specification, despite inherently vague, fluid, and evolving intents. While HCI research has proposed diverse interaction techniques to support this process, how key aspects of intent communication interplay to shape users’ workflows remains underexplored. To bridge this gap, we conduct a systematic literature review of 46 HCI papers and identify four core aspects of intent communication support: intent • Articulation, • Exploration, • Management, and • Synchronization. To investigate how these aspects interplay in practice, we developed IntentFlow, a research probe that embodies all four aspects for a writing task, and conducted a comparative study (N=12). Our action-level behavioral analysis reveals that comprehensive support enables verification-driven refinement and progressive intent curation, reduces cognitive effort, and improves users’ sense of control and understanding of intent–output alignment. We conclude with design implications for building generative AI systems that support intent communication as a dynamic, iterative process.

Existing approaches to combat the opacity of large language models tend to centre around post-hoc explanations of model processes. We explore an alternative approach to fostering critical understanding through intuitive, experience-driven exploration of an LLM’s inner workings. Toward that end, we present “Stepping Into the Black Box” (SIBB): a critical artefact which enables participants to “see like an LLM” and take on an active role in its generative process. Through the box, pairs of participants enact a dynamic interaction with a chatbot presented as a local expert. Participants are invited to intervene in its algorithmic decisions, building responses in tandem with the model. Their interactions and reflections speak to the kinds of criticality developed by experiential learning, highlighting the particular ways that exploring an enclosed system from within affects trust. In this way, SIBB demonstrates the potential of collaborative, experiential research-through-design practices to foster community understanding of emerging technologies.

Open-source software (OSS) developers often struggle to understand and respond to user context, while existing tools, such as issue trackers (for handling bugs, requests, and feedback), largely focus on technical discussion. Although personas could help, limited resources and UX expertise make them hard to scale. We present PersonaFlow, a tool that generates editable user personas from OSS repository artifacts and integrates them alongside issue reports. In a user study with 13 OSS developers, most reported shifts in how they understood users, and more than half modified their responses by adding empathetic language, tailoring explanations, or raising priority ratings. We found two pathways to this change: some connected emotionally to personas as people, while others used them pragmatically for triaging. Both appeared to lead to more user-centered behavior. We contribute design implications for persona-based tools relevant to OSS and other contexts where efficiency-driven systems or workflows obscure valuable human elements.

Usability studies often overlook valuable insights from moderator-to-participant interventions. This study proposes that these interventions, moderator-provided “nudges”, are a rich source of insights on user needs, cognitive load, and system design gaps. We analyzed transcripts from 86 sessions involving two digital library systems and 56 blue-collar (BC) and 30 white-collar (WC) participants, and identified 962 instances of moderator interventions (i.e., nudges). Findings show significant disparities in both the volume and composition of nudges across groups. BC participants required 21 times as many nudges (N=919) as WC participants (N=43). Of the BC participants’ nudges, 737 (80.2%) were system-related, and 182 (19.8%) were user-related. Treating nudges as usability insights for HCI research enables researchers to identify where system scaffolding, such as explicit language, progressive guidance, or simplified workflows, is necessary. The study contributes to inclusive design theory by demonstrating how occupational background influences interventions in user studies and provides design implications for making digital information services more inclusive across occupational groups.

Dark patterns are commonly defined as manipulative interface designs that undermine user autonomy, with prior work evaluating their impact through predefined negative framings. However, users’ lived experiences of influential design are often more heterogeneous and situational. This paper examines how users experience and interpret expert-defined dark pattern elements in Chinese livestream e-commerce. We conducted a qualitative study using video-stimulated recall interviews based on participants’ screen recordings (N=17), capturing real behaviors and in-situ reasoning. Our findings show that user responses extend beyond a simple compliance–resistance dichotomy, unfolding through a set of behavioral strategies, composite attributional reasoning, and diverse interpretations of influence. While some designs were perceived as coercive or deceptive, others were experienced as rational persuasion. We contribute a user-centered behavioral taxonomy and a model of design influence grounded in three experiential dimensions, offering insights into how influence is interpreted in dynamic, real-world interaction contexts.

Intergenerational co-design often assumes a Western democratic ideal of flattened hierarchies. Engaging with pluriversal design perspectives, this paper challenges these universalist assumptions by investigating extrafamilial co-design in a non-Western context shaped by Confucian values. We explore how unrelated older adults and teenagers negotiate authority within high-power-distance environments, using Virtual Reality as a “social disruptor” to redistribute expertise. We propose a taxonomy of four interaction modes-Unilateral Transmission, Benevolent Authority, Material Authority, and Synthesized Co-Creation-to describe how power is fluidly negotiated. Our findings reveal that design tools function as “political shields,” allowing youth to assert agency while maintaining cultural harmony. We contribute a micro-analysis of power-asymmetric collaboration and provide workshop strategies designed for functional reciprocity. The rationale for these strategies is to navigate, rather than dismantle, rigid social structures, providing actionable insights for practitioners working in diverse, culturally situated design environments.

Privacy research in the field of HCI is predominantly based on theories that view privacy as an individual decision, interpersonal boundary regulation, or adherence to context-specific norms. While these approaches have provided valuable insights, they do not sufficiently take into account the structural and relational power asymmetries that shape the emergence and restriction of privacy. This paper proposes a relational power framework of privacy that reconceptualizes privacy as an outcome of power‑laden interactions among individuals, institutions, communities, and technological infrastructures. Drawing on critical theory and actor-network theory, we show how widespread privacy models obscure dominance by overemphasising users’ agency, assuming symmetrical relationships, and depoliticizing contextual conditions. We argue that privacy should be viewed as shaped by power, which requires consideration of structures such as institutional arrangements and infrastructural configurations. Our framework provides HCI researchers and designers with an analytical tool to uncover and address power asymmetries.

This study investigates how Iranian immigrant nonprofit groups experience exclusion within technology and AI-driven infrastructures. Based on 27 semi-structured interviews, it identifies how legitimacy barriers, capacity gaps, and ethical dilemmas intersect to create a cycle of infrastructural immobility that restricts these groups’ participation in the digital nonprofit ecosystem. The findings reveal that generative AI, while providing some opportunities, risks exacerbating these challenges by deepening marginalization and reinforcing inequalities in access, data visibility, AI fluency, and AI-mediated representation. Uncritical adoption of generative AI in the nonprofit domain undermines transparency and human connection in everyday organizational practice and induces bias in AI-generated content in the context of Iranian immigrant nonprofit work. To address these issues, the paper proposes interaction-level design strategies that promote community-driven inclusion, support context-aware capacity building, and leverage AI’s augmentative potential to strengthen transparency practices and human connection among Iranian immigrant nonprofits.

Community sharing fosters resourceful, socially engaged lifestyles that challenge current economic systems based on extraction and exploitation. Previous designs for supporting sharing communities often target digital platforms and physical sharing sites, relying on members to promote the culture of sharing. This paper reports on the initial exploration of utilising shared objects as a site of design and as active agents in promoting sharing cultures. We introduce Senito, a speculative cordless drill designed with agency to encourage sharing cultures through speech and movement. We report on the design and initial testing of Senito with 14 members of sharing communities. Our results indicate that agency in shared objects could be leveraged to enhance accountability, strengthen community bonds, and facilitate knowledge sharing. Our design approach further proposes directions for future research to explore different ways of configuring and understanding the potential of agentic objects in community sharing.

Shared living—where unrelated adults share domestic infrastructure—relies heavily on the interpretation of material traces: physical cues such as food arrangements, cleaning states, and object placements that residents produce and encounter asynchronously, largely without direct exchange. When the interpretive frame under which a cue is produced diverges from the frame under which it is decoded, coordination friction results. Through semi-structured interviews (N=13), we identify three interpretive frames—evidential, normative, and communicative—that residents apply to the same domestic cues, and document folk tuning: improvised strategies by which residents adjust three parameters—attribution, granularity, and temporality—to govern the distribution of ambiguity in shared space. A generative design workshop (N=20) then demonstrates how data physicalization can expand these parameters from constrained physical ranges into designable continua, yielding design orientations. We propose tunable ambiguity as a design concept that reframes ambiguity from a static artifact property to an inhabitant-controlled social process, and contribute actionable principles for interactive systems that support unspoken domestic coordination without collapsing into surveillance.

Fostering sustainable household practices is important to reduce environmental impact and address climate change. Data sculptures can offer opportunities to engage people with their consumption practices in meaningful ways in a household context. We present the results of a user study with 15 households that interacted with a data sculpture (Eco-Garden) that was deployed for three weeks. Eco-Garden provides information on electricity, gas, and food waste consumption data to help people understand the relationship between everyday practices and resource consumption. Our results demonstrate that the physicality and aesthetic qualities of Eco-Garden were key to supporting collaborative exploration, reflection, interaction, and family discussions at home. The study reveals how household members gradually integrated the artefact into their routines, increased the household engagement with sustainable practices, encouraged collective action as well as the importance of designing for collaborative exploration, and the temporality of engagement with Eco-Garden and understanding of consumption. Our findings suggest that domestication and defamiliarisation of objects in households can help sustain prolonged and collaborative engagement with eco-feedback technologies.

This paper introduces the Coastal Community Thinking Toolkit (CCTT)—an analog visual mapping system for the co-production of situated coastal knowledge—as a design inquiry into Sustainability through and of HCI. Grounded in infrastructuring as a design-in-use paradigm, the CCTT was conceived as a design seed and enacted within an eight-week residential training program on sustainable coastal development, involving 20 professionals from 14 countries across two workshops and ongoing informal interactions. Our analysis focuses on Points of Infrastructuring (PoIs): in-use moments that reveal a system’s limitations and potential. We contribute an expanded PoIs grammar for early-stage systems and an analog-first approach to support appropriation, sustained iteration, and informed digital upgrading. While positioning infrastructuring as a strategy for aligning Sustainability through and of HCI, we conclude by stressing its value in advancing ocean-focused SHCI and supporting ocean governance efforts to set up an inclusive digital ocean ecosystem.

In this pictorial, I use noticing as a place-based practice to attend to intertwined material, social, political, and temporal dimensions of computing infrastructures. Drawing on visual ethnographic fieldwork in coastal southeast Louisiana, I draw attention to utility poles, an everyday infrastructural form through which electrical and telecommunications networks are installed and maintained. In coastal communities, storms and floods can impair existing aging infrastructure, while what gets restored and updated reflects longer legacies of uneven development. Through images paired with vignettes, I show how connectivity is materially tethered to place, shaped by access, labor, extractive histories, and changing landscapes. The pictorial contributes a visual method for understanding computing infrastructures in the context of ongoing climate and environmental crises and offers ways of understanding and designing infrastructures from situated perspectives.

Amid growing attention to ocean sustainability, coastal communities emerge as uniquely insightful yet underinvestigated socio-ecological systems, prompting calls for more inclusive tools for collaborative sense-making. To address this, we designed the Coastal Community Thinking Toolkit (CCTT), a visual mapping device that enables the representation of coastal communities across diverse purposes and contexts. We present the toolkit together with findings from a pilot involving a heterogeneous group of 20 ocean professionals, primarily from African countries. We contribute an artefact that operates as a boundary object, clarifying the many facets of its boundary-crossing potential. Responding to calls for more impact-oriented and interdisciplinary Sustainable HCI, this pictorial also serves as a resource for community managers and activators, as well as researchers, seeking participatory methods to facilitate collaborative sense-making about and with coastal communities.

Forging children’s nature connections involves affective, sensory, and imaginative engagements, underscoring the value of place-based, tactile, and creative interactions. While craft and making offer potential synergies, many digital approaches for children remain limited to designed artefacts that foster contact in, as opposed to with, nature. Inspired by art-based approaches, this study introduces Hybrid Nature Crafts (HNC) a craft approach that integrates diverse materialities, such as leaves and light, to foster children’s connections with nature. To examine how HNC supports these connections, we conducted two workshop series in distinct physical settings with 15 children, documenting their creative processes throughout. This pictorial highlights how craft enables children to notice nature, perform place-making, and construct meanings of and with nature. Findings advance understanding of hybrid approaches and offer practical insights for designing workshops that nurture children’s ecological identities through creative making.

How can large language models (LLMs) support critical design explorations for describing, articulating, and responding to bodily phenomena? This study investigates the integration of code-generating LLMs across design explorations in four different self-tracked or bio-sensed domains: learning, chronic illness, posture control, and knee injury. We synthesise our collaborative research through design inquiries into three themes: 1) forced articulation of fuzzy phenomena, 2) difficulty of escaping local optima, and 3) unexpected fragility of generated sketches. These themes, though potentially applicable in other domain contexts, are grounded in our challenges with code-generating LLMs on embodied and embodiment subjects, with a particular focus on working with technology and data relevant to embodied experiences. We propose two implications for design of LLM-based sketching tools anchored on our design inquiry journeys.

Methods for understanding the felt and situated experience of controlling autonomous systems are crucial for designing systems that are adjusted to the complexity of human interaction. Prior work has tended to overlook the bodily and experiential dimensions of monitoring systems at a distance, particularly in moments of losing control. We combined two approaches, ethnomethodology and conversation analysis, and soma design, to explore a case where a semi-autonomous system crashed when controlled by an inexperienced operator, captured in video ethnographic fieldwork. We report on our methodological approach, combining sequential video analysis of the unfolding sequence and interviews inspired by microphenomenology to unpack the operator’s experience of losing control. We contribute methodological considerations for interaction designers seeking to explore the felt experience of having and losing control of autonomous systems and discuss how insights gained through this combination of methods, rooted in phenomenology, support a designerly appreciation of safety operators’ work.

There is an expanding vocabulary in academia addressing the struggles surrounding what constitutes knowledge in design research. However, most approaches overlook the role of researchers themselves, along with their lived experiences and theoretical commitments. Building on Redström’s work on design programs, we retell stories of our architectural education and practice as a constructed ‘we’ and suggest design sensibilities as a way of knowing that allows for a less aseptic approach to the epistemological assumptions in interaction design research. We portray sensibilities as a skin rather than a lens, and propose that design relies strongly on breaching the theory/practice divide as situated, poetic, positional, relational, and corporeal; while still constituting transferable and collective knowledge. We propose the articulation of sensibilities as intermediate level knowledge in order to account for a view of theory as the skin through which we shape and are shaped by our work.

Surfing, like many water sports, offers a unique opportunity to experience natural environments, yet the design of interactive technologies for water activities– “WaterHCI” – so far prioritised athletic performance over experience. To explore this opportunity, we designed SurfSync, a novel wearable system in the form of an actuating vest and a hat that provides oceanic information through sound, vibration, and heat actuation, aimed at enriching the surfing experience as a playful encounter with the ocean. We studied SurfSync in an ocean-based field study with eight surfers. Through thematic analysis of interviews, we articulated the surfers’ experiences, indicating how they made sense of playful cues while in the ocean. By reflecting on our soma design process and the surfers’ experiences, we provide six design strategies to enrich the surfing experience. Our work contributes to the emerging field of WaterHCI by providing insights into how wearables can enrich human-water experiences outdoors.

Vulnerability is a subjective, embodied experience that unfolds during transitions such as relocation, where identities, routines, and social ties are unsettled. This paper examines how vulnerability manifests through bodily sensations, movements, and gestures that often escape verbal articulation. Using established embodied interaction design methods, we conducted three bodystorming workshops with fifteen international PhD students who recently relocated. Through movements and multisensory materials, participants explored and expressed their lived experiences of transition. Based on the insights, we contribute an embodied vocabulary for vulnerability consisting of: 1) Contracted Postures, 2) Restricted Reach, 3) Rhythmic Compression, 4) Micro-adjustments to regain stability, 5) Gaze Regulation and 6) Gestures of Waiting and Pause and design implications that can aid the design process. By exploring how body, grounded in its movement, sensory, and felt qualities, can surface vulnerability through embodied methods, this paper contributes a lens for design that attunes technologies to sensitive transitional experiences.

We introduce SoTA, a multi-device interactive artwork on artificial intelligence (AI). It visualises 118 neural network architectures as artistic objects that can be experienced at an immersive scale, allowing audiences to select, explore, and navigate different models interactively. The installation is also characterised by an intentional narrative shift that moves from open-ended exploration to abstract visualisation. We exhibited SoTA in a public art museum and conducted an in-situ study with 33 participants to investigate how aesthetic experiences foster affective and critical reflection, complementing technical explanation in public AI engagement. Our findings reveal how aesthetic environments create emotional conditions that foster deeper inquiry, leading to enhanced understanding, productive ambivalence, and ultimately personal, philosophical, and societal reflection on AI. We conclude by discussing the opportunities of artistic exhibitions for public AI engagement and suggest three concrete design strategies grounded in our design and evaluation of SoTA.

This paper argues that creative AI use can be usefully understood as embedded and positioned within artistic practice, rather than being only guided by widely cited AI design principles such as transparency or predictability. We ground this argument in a single case study: a detailed autoethnography of a trained artist’s GAN-based art-making process, which surfaces how creative practice unfolds in the face of opacity, constraint, and curatorial control. The case shows how uncertainty can be a limitation around which creative practice is productively organized. Through reflexive autoethnographic memos, we identify three analytically consequential sites of creativity in the case: Conception, or the artist’s intuitive mental models of the AI in use; Comprehension, or the artist use of strategies to work without full system understanding and; Linearity, or how the artist imposes temporal structure on a process with unpredictable outputs. We propose that these boundaries can act as sensitising concepts for understanding and designing support for creative AI practice. To explore their support for design and deepen our understanding of them with relation to the artist’s use, we describe a documentation system intended to support problem-solving, workflow design, archiving of past solutions and knowledge-sharing among AI artists. We conclude that the boundaries and system described provide an important perspective on creative AI use: as involving negotiating system opacity through iterative curation, bespoke datasets, and contextual improvisation. We also argue that this perspective is generative, offering a grounded, practice-oriented basis for future research and tool design in the space of creative AI use.

Large-scale prompt-based AI generators have broadened access to media creation but often introduce friction in sustained creative practice. Visual artists must navigate limited personalization, narrow cultural coverage, homogenized aesthetics, and broader concerns around sustainability, bias, and data-scraping ethics. Small data and model crafting offer alternatives that center artists’ agency over data and models. We examine this approach through Autolume, a no-code, locally run tool for crafting and navigating generative models in real time. Across five focus groups with 25 visual artists in three two-day workshops, Autolume was used as a research probe to explore small-data practices. Findings show that participants viewed small data as a personal and responsible alternative to large-scale AI, but identified model training as a technical barrier, underscoring the need for improved explainability and support. We highlight design opportunities for AI art tools, including low-level controls, mapping UIs to ML processes, ecosystem integration, and small data as a pedagogical tool.

Generative AI has increasingly been used for artistic creation, but little work has explored how it shapes the experiential meaning of practice. We consider how generative AI might transform the embodied and tangible process of instant photography through the UnReality Camera, an AI-mediated instant camera. The UnReality Camera prints a photo of the environment augmented by a user’s spoken words as generative input. In a design probe, we explored how generative AI shapes people’s perceptions of both photographic output and the broader photographic process. Although users valued artistic control, they also appreciated the creativity afforded by stochastic unpredictability. The waiting period for an unpredictable output elicited anticipatory suspense, and the camera’s physical form evoked ownership and connection despite artificial generation. We discuss how people make sense of instant photography’s experiential qualities when generative AI is embedded, and how their opposing affordances reshape interpretations of each other’s experiential meaning.

We present Editing Reality, a mixed reality system that enables in-situ co-creation with generative AI directly within physical environments. Rather than treating generation as a one-shot command, the system supports embodied and iterative creation through speech, sketching, and direct manipulation, allowing users to generate, modify, erase, and retexture real-anchored virtual and reconstructed scene elements in place. Using a Research Through Design approach, we investigate how co-creation unfolds through iterative system development, a formative workshop, and expert review. From this process, we articulate a set of designerly framings that characterize in-situ co-creation as a negotiated, spatial, and temporal practice shaped by previews, accumulation, waiting, embodied evaluation, and learning the system as a spatial actor. We instantiate these ideas in a working system and report expert feedback highlighting both its creative potential and its design implications. Our work contributes a conceptual lens for understanding generative AI in mixed reality not as a one-shot automation tool, but as part of an embodied, situated creative process.

Embodied metaphors, grounded in sensorimotor experience, can enrich tangible interaction design by linking abstract functions to familiar bodily actions and perceptions, making them more intuitive and meaningful. However, our formative study (N=10) revealed that designers—especially novices—struggle to identify appropriate embodied metaphors, move beyond superficial analogies, and translate them into tangible designs. To address these challenges, we developed MetaEmbody, an AI-assisted creative support system that helps designers explore contextual analogies, derive embodied metaphors, and shape them into tangible interaction concepts. A user study (N=20) demonstrated that MetaEmbody effectively stimulated embodied thinking and enhanced the metaphorical embodiment of design outcomes. It also yielded higher ratings in novelty, feasibility, and overall human–AI collaboration experience, with notable benefits for novice designers. We explore the potential of how human–AI collaboration can foster embodied design thinking, advancing generative AI beyond visual metaphor blending to support deeper exploration of interaction and experiential meaning.

Human-Robot Collaboration (HRC) in surgery is often framed as a dyadic interaction between a surgeon and a robot. Such a focus on interactivity can narrow analytic attention, sidelining the often unseen coordination that makes robotic surgery workable across the wider surgical team. We surface the everyday practices of the Mako Product Specialist (MPS), a vendor-mandated support role embedded in Mako-assisted robotic surgeries. Drawing on ten expert interviews, supplemented by in-theatre observations, we identify three categories of specialist work: Supporting Makoplasty, Shouldering Makoplasty, and Sustaining Makoplasty. Across these categories, we show how specialist mediation sustains co-located surgical HRC through articulation work, boundary work, and translation-and-repair work that stabilises tempo, maintains legibility, and enables breakdown recovery under sterile and infrastructural constraints. These findings extend ensemble accounts of surgical HRC and motivate the design of robotic systems for ensembles rather than a single primary user.

Robots are increasingly present in human spaces, such as for conducting deliveries in hospitals, interacting with visitors at museums, and stocking items in warehouses. To ensure the seamless integration of robots into these spaces, a new role in human-robot interaction is emerging—the robot wrangler, namely an individual who is responsible for setting up, overseeing, and troubleshooting the robot. To understand the needs of this stakeholder, we conducted a scoping review that uncovered a typology of robot wrangling across the research literature, and discovered that wrangling is an umbrella term that collapses a highly complex and heterogeneous space of activities, often rendering this labor difficult to characterize and support. To further clarify and understand robot wrangling, we then reflected on our own firsthand and imagined experiences as robot wranglers within our own respective domains. Guided by the scoping review and our reflections, we devise a series of design implications for supporting wranglers directly as individuals and as members of a wider service ecology.

Robot ownership is not merely a background characteristic but a potentially salient social signal. When robots mediate group interaction, ownership may quietly reorganize power, trust, and cohesion. In this study, participants (n = 225) watched one of five videos in which a robot was introduced as owned by (1) a faceless outgroup entity, (2) an active ingroup speaker, (3) a passive ingroup peer, (4) the participant, or (5) the group collectively. Participants then observed a group discussion in which the robot consistently nodded in agreement with the speaker. We measured perceived group cohesion, the robot’s status within the group, and elicited descriptions of the robot’s social role. Robots that were participant-owned or group-owned fostered greater cohesion and were perceived as socially closer and more collaborative. Despite identical behavior, the speaker-owned robot was perceived as more distant and manipulative. These findings highlight robot ownership as a critical aspect of Human–Human–Robot Interaction.

Multilingual collaboration is increasingly common as today’s world becomes global and culturally diverse. While diversity fosters innovation, language barriers can hinder involvement and effective communication. Prior work has primarily focused on improving translation accuracy with limited attention to how translation systems shape dimensions of trust in interaction. Given that users must rely blindly on technology due to their inability to understand the system’s output, this issue becomes a crucial aspect. To address this gap, we introduce MorphHat, a co-embodied humanoid robot interpreter featuring a customizable, morphing face that visually represents the active speaker. We evaluated MorphHat MorphHat influenced trust, rapport, and social presence. We discuss these insights as early design implications for future embodied translation systems.

The identity design of artificial agents carries growing ethical, psychological, and cultural weight, as ubiquitous language models and diverse robotic forms are blended into everyday use. However, structured approaches to designing coherent and interpretable artificial identities remain limited. To address urgent challenges in artificial identity design, including harmful stereotypes and deceptive practices, we introduce the Identity Design (ID) Framework and an accompanying research agenda. Drawing on emerging work on artificial identity in human-robot interaction and taking an interdisciplinary perspective, we propose twelve design principles across three levels: individual (recognisability, behavioural consistency, identity continuity, memory, persistent goals), group (membership signalling, social alignment, role clarity), and societal (benevolence, artificiality, social justice, transparency). The research agenda outlines open questions around the operationalisation and measurement of identity, social dynamics, and ethical considerations for identity design. Together, they lay the groundwork for future research and responsible practice in robotic, virtual, and multi-embodied agents.

Group mood and engagement are invisible currents that shape how people collaborate at work, emerging through everyday interactions rather than isolated individual states. While positive socio-emotional dynamics support collaboration and productivity, they remain difficult to sense and interpret unobtrusively. This work investigates whether posture-based behavioral cues sensed through everyday objects can provide insight into group mood and engagement. We present a pressure-sensing chair prototype that captures changes in weight distribution during meetings. In a study with 14 groups (N = 46), we combine postural cues with participants’ retrospective video annotation to triangulate engagement and mood. Our results show that posture activity is associated with engagement and mood arousal, while moments of shared group mood co-occur with increased postural synchrony. We further identify synchronized behavioral patterns reflecting affective convergence and contagion. These findings demonstrate how situated sensing through everyday objects can reveal socio-emotional dynamics and inform the design of collaborative systems.

In mixed-ability collaboration, eye contact is often treated as a default cue for attention and turn-taking. As these signals are primarily visual, they are not reliably accessible to people with visual impairments. While prior work emphasized technical solutions, mechanism-level explanations of their experiences with sighted partners remain scarce. We interviewed 17 people with visual impairments about everyday interactions across work, education, and social settings. Using a critical-realist lens, we link events to plausible causal mechanisms and identify three recurring mechanisms: First, when gaze cannot allocate the floor, addressability hinges on explicit naming. Second, unclear speech entry cues and ongoing access work split attention and build fatigue, sometimes leading to withdrawal. Third, eye-contact norms can skew judgments of participation, prompting active management of visibility. We translate these mechanisms into five design challenges that reframe accessible eye contact as supporting configurable interaction contracts rather than merely making gaze visible.

Meaningful social interaction is vital to well-being, yet Blind and Low Vision (BLV) individuals face persistent barriers when collaborating with sighted peers due to inaccessible visual cues. While most wearable assistive technologies emphasize individual tasks, smart glasses introduce opportunities for real-time, contextual support in social settings. To explore how smart glasses affect interpersonal dynamics and support inclusion in mixed-vision groups, we developed a smart glasses–based system CollabLens as a technology probe, and employed it in four workshop sessions. We found that smart glasses can meaningfully support inclusive collaboration through expanding BLV participants’ assistive networks with a more flexible, independent access to visual information. While sighted participants viewed smart glasses as a promising medium that fosters interpersonal connection, they revealed uncertainty in adapting their helping behaviors. We concluded by discussing and synthesizing challenges and opportunities for designing smart glasses that provide seamless interaction experiences and enhance reciprocal mixed-vision social inclusion.

As mixed-ability collaboration has become increasingly focal within accessibility research, managing varied, and sometimes conflicting, access needs has become a key consideration in designing for access. When an accessibility feature or practice benefits some people while constraining others, how should designers navigate these trade-offs? This paper responds to this question by analyzing how a mixed-ability nonprofit worked to make communication accessible to its members as it grew from a small blind-focused athletic group to a larger cross-disability organization. Based on a six-month study that combines interviews and field observations, we show that working with conflicting access needs is not just a technical ‘problem’ but a generative process that sparks reflection on technical constraints and preferences, diverse roles and communication norms, and organizational demands. We therefore argue for rethinking “conflicts” in access as key sites for  revealing power structures and creating opportunities for accountability and repair.

Dark patterns have gained increasing attention among the HCI and design communities, but little is known about how they intersect with assistive technologies (ATs) and impact people with accessibility needs, such as blind and low-vision (BLV) individuals. To address this gap, we conducted an in-lab user study with 18 BLV participants using a custom-built social media application that embeds six common dark patterns. Through observing participant experiences with assigned tasks and semi-structured post-study interviews, we explored how screen readers and magnification tools influence the perception and amplification of deceptive design elements. In contrast to prior work that identified accessibility-induced deception, our findings demonstrate a dual role of ATs where dark patterns are either revealed or intensified. Screen readers exposed hidden manipulations like bad defaults but amplified other dark patterns through sequential reading. Similarly, magnifiers intensified deceptive effects through viewport reduction by restricting the visible area. We conceptualize this mechanism as assistive amplification and show how dark patterns manifest differently for BLV users, informing the design of more inclusive and manipulation-resistant interfaces.

Writing is important for many careers. While writing tools continue to be studied in HCI, little attention has been paid to the accessibility of typesetting with LaTeX, a widespread markup language for preparing publications. In contrast to WYSIWYG (“What You See is What You Get”), co-authoring papers in LaTeX presents additional considerations with code, visual, and PDF content co-existing in an interface. Our collaborative autoethnography centers two neurodiverse teams preparing ACM SIGCHI publications using LaTeX interfaces. We illustrate how text customization constraints and collaborative clutter produce inaccessible visuals to dyslexic writers. In turn, we shift our conceptualization of design for dyslexia from intervening solely on fonts to understanding visual processing in the greater whole of technology-mediated communication. With this updated conceptualization, we discuss changes to improve writers’ agency through: (1) customization by text function, (2) presentation of collaboration cues, and (3) social support for a wider range of access labor.

Individuals with intellectual disability often face challenges in expressing intentions and initiating conversations. While assistive communication technologies typically emphasize language acquisition, generic image browsers enable open-ended self-expression without structured symbols or language. Building on prior work showing that intent-based image recommendations represent potential meanings of a selected image, we examine how such suggestions are taken up in real communicative practice. We studied an assistive communication prototype that uses generative AI to provide intent-based image suggestions. The study was conducted across multiple sessions with 15 adults with intellectual disability, varying facilitation and interaction framing to examine how these suggestions shaped conversational flow. Our findings indicate that intent-based image suggestions played multiple interactional roles, including prompting expression, clarification, and sustaining conversation. We also identify moments of misalignment, where suggestions failed to align with users’ communicative goals due to missing cultural context or interactional support. We discuss interaction mechanisms and design implications for intent-based assistive communication systems to support inclusive and effective communication.

Clay is easily shaped when wet, but we lose this quality once the material undergoes firing. Conventionally, the overall form of a ceramic piece is therefore fixed upon firing. From this stage, design is limited to glazing, a surface treatment that melts and bonds to the ceramic substrate, providing only color and finish. In this work, we instead explore glazing as a system for joining ceramics, enabling us to examine ceramic forms after firing. Through this exploration, we developed three joint types—contact, interlock, and drip. Using these joints, we made a range of ceramic objects and observed that we could approach 3D printed ceramics in more non-linear design temporalities, such as a ceramic vase that can be reassembled and expanded into a larger vessel at any time. This approach allows complex shapes to be joined from multiple simpler parts, which are difficult to fabricate as a single piece with clay 3D printing. We position our approach among traditional and related ceramics making practices, and reflect on its implications on designing with ceramics.

Domestic manual knitting machines are widely used among hobbyists and for small-scale production. However, they are often seen as less capable or niche, and their potential for integration with other materials and machinery has remained relatively unexplored in the HCI community.

Knit Joinery proposes an approach that uses knitting as a joinery method to interact with other digital fabrication tools, including laser cutters, 3D printers and CNC knife cutters, becoming part of a makerspace ecosystem. The goal of this paper is to broaden the use of manual knitting machines and position them as a fabrication tool rather than devices solely for producing soft materials. We present examples demonstrating how a manual knitting machine can integrate with laser cutting and 3D printing machines, soft-circuit fabrication, and creative reuse.

Multi-layered weaving is a technique for weaving multiple layers in tandem through selective warp and weft interlacement, offering unique opportunities for HCI design research, in whole-garment construction, e-textiles, and 3D fabrication. Multilayer weaving is a complex process with varied design methods and fabrication workflows that are not optimized for in most weaving design tools. We believe that HCI researchers can benefit from multilayer weaving as a manufacturing technique and could contribute to the development of new computational design tools for weaving. To identify opportunities and alignments in multilayer weaving design practice for HCI researchers, we provide a systematic overview of contemporary multilayer design practices. We analyze existing multilayer weaving design tools for manual and semi-automated weaving to identify strengths and breakdowns across design representations, interaction modalities, and artifacts. We integrate our analysis with prior HCI interaction principles and strategies from human-centered programming and computer-aided design to present future opportunities for developing expressive, multilayer computational design technologies for weaving.

We present Pulpform, a hybrid craft approach integrating 3D-printed structures with traditional papermaking to create composite objects with paper-like textures. We began this research by exploring 3D printing for papermaking, observing that the geometry of the mesh screen guides the selective deposition and entanglement of paper fibers during the water screening process. Through systematic testing, we characterize the relationship between 3D-printed mesh geometry and pulp composition, identifying three primary mesh-fiber interactions. Using a Research through Design approach, we demonstrate the versatility of Pulpform through a range of artifacts inspired by real-world scenarios—including masks for performing arts, air filters, and sensing origami. We then discuss how the role of 3D printing shifts from a tool to an integral material component within this process, reflecting on how Pulpform composites lead to “paperness” and “frugality” as two emergent and desirable materialities.

Affordances support interaction by suggesting actions, while anti-affordances discourage or complicate task execution. In this work, we focus on property-based affordances and anti-affordances arising from a device’s physical properties, such as form, texture, and volume. We investigate how the usability of a handheld device changes when the property-based affordance of its grip transitions into an anti-affordance through a shape change. In a workshop exploring the design space for such transitions, which we term ’Useful Uselessness,’ we gathered concept ideas for shape-changing grips that can complicate or interrupt a task. Implementing three representative examples allowed us to study these grips during an exemplary drilling task. Our results show that changes in grip property can be especially useful for task interruption. We found that task context matters, and that shape-change type and its property have to be carefully chosen. In summary, we show that dynamically adapted property-based affordances can be useful to interrupt tasks, while safety issues have to be kept in mind.

In office environments, achieving flexible workspace configurations by adjusting the position, height, and curvature of partitions is crucial for accommodating diverse worker needs, but implementing such flexibility typically introduces system complexities. We present OrigamiWalls, a robotic partitioning system that addresses this trade-off by using origami structures to permit diverse configurations without requiring high-degree-of-freedom actuation. Based on expert insights, we explore the OrigamiWalls design space and report on the implementation of the desktop-scale and floor-scale prototypes utilizing the Tachi-Miura polyhedron structure. These prototypes can transition continuously between three distinct forms (no wall, curved wall, and flat wall) with high expansion ratios (up to 660%) using a single reel-type linear actuator. Our preliminary user study with our desktop-scale prototype suggests that origami-based shape changes are perceived to enable task-adaptive workspace configurations and support workers’ task transitions unobtrusively and safely.

People living with rheumatic musculoskeletal diseases face a paradox: movement is painful, yet essential for managing their condition. In researching this challenge to inform design, we recognised that no one understands these struggles better than the patients and healthcare providers who care for them. While inclusive collaboration is valued, the practicalities of engaging diverse stakeholders surface differing perspectives, needs, and expectations. How researchers approach stakeholder involvement shapes whose voices are amplified and impacts the final outcomes.

In this pictorial, we trace how an ethos of togetherness shaped the interdisciplinary co-design of a bespoke digital mirror for physical rehabilitation at an Irish hospital. We reflect on inherent power dynamics and how positioning diverse stakeholders as co-designers with flexible agency can be transformative in informing user-centred design and fostering genuine care, ensuring that care is integrated into the design process rather than treated only as an outcome.

Rehabilitation poses challenges due to high variability in individual motor impairments. Effective upper-limb rehabilitation requires distinct exercises and equipment for different recovery stages with progressive adjustments. Clinicians in our studies perceived modular wearable strategies as a speculative but potential direction for personalized rehabilitation. However, how clinicians reason about configuring modular components to support full-stage rehabilitation remains underexplored.

In this article, we utilize two-phase design probe workshops. We explored perceptions and barriers with 10 rehabilitation clinicians regarding modular wearable approaches using low-fidelity, non-functional probe artifacts. We investigated how clinicians combine modules for different rehabilitation stages and scenarios. Our findings underscore the non-standardized, trial-and-error nature of rehabilitation. We highlight opportunities identified by clinicians, which point toward envisioning rehabilitation embedded in daily activities and inspire forms of clinician-informed guidance. Based on these participant perceptions, we propose a conceptual design framework and implications to inform future exploration.

Emerging technologies have become a key area of interest in sports and rehabilitation research. Technologies such as extended reality and motion capture have much to offer patients returning to sport after injury, including motivating compliance, managing anxiety, understanding progress, and increasing autonomy. To explore how emerging technologies can complement a real case of rehabilitation and return-to-sport, we used an autoethnography to document the first author’s 11-month recovery following knee surgery and her gradual return to figure skating competition. We analyze journal entries documenting progress at different stages of recovery, clinical notes from physical therapy and training, and reflections following the use of novel interventions. The results of this study particularly highlight the utility of emerging technologies for balancing physical and psychological needs during return-to-sport and for improving the patient’s education, awareness, and autonomy. Future work should explore additional combinations of novel technologies with larger cohorts to identify more effective methods for complementing standardized rehabilitation practices.

Chronic pain (CP) both significantly affects individuals’ wellbeing, and complicates care for many. CP’s unpredictable nature, diverse causes, and frequent need for inter-medical-disciplinary coordination has made it a priority for bespoke digital interventions from virtual reality headsets to online fora. Many however report limited benefits from existing digital tools, and the potential of technology to enhance daily life for those with CP remains largely untapped. This paper in response embraces a participatory design approach to everyday mundane chronic pain technology. We report findings from an analysis of the r/chronicpain subreddit, focus groups conducted with the support of a national chronic pain organisation and a workshop conducted with members of this community, yielding a novel prototype pacing application. Through this paper, we contribute insight into the experience of living with chronic pain and implications for technology design and research practices to support everyday care.

People with Parkinson’s disease (PwP) engage in diverse self-care practices, including tracking medication and symptoms. While consumer-oriented health technologies have supported such practices, specialized technologies for Parkinson’s disease (PD) are increasingly being integrated into clinical care. This paper reports on a design inquiry including PwP and healthcare professionals aimed at eliciting needs for meaningful representations of PD symptom-tracking data. We conducted a toolkit-based data elicitation workshop with 8 PwP experienced with sensor-based active tests to assess motor symptoms, alongside 15 interviews with clinicians, including neurologists, nurses, and physiotherapists. Interviews combined dashboard prototypes centered on simulated active test data with collaborative prompt-based prototyping. Our findings show that PwP and clinicians rely on one another to interpret symptom-tracking data, and that the resulting data representations may influence the focus of clinical encounters. Active test data is found particularly valuable for nurses and physiotherapists, while highlighting tensions in using such data to support neurologists’ medication-related decision-making without increasing testing burdens for PwP.

With growing research on haptic interfaces, Mediated Social Touch (MST) technologies offer the potential to record, synthesise, and reproduce (RSR) touch experiences across space and time, enabling, for instance, a hug from afar and from the past. Although much of the existing research highlights the direct benefits of these systems, such as reducing loneliness and providing emotional support, little attention has been paid to their broader sociotechnical impacts. To address this gap, we used the Future Ripples method to speculate on possible effects of MST. We conducted three workshops with 24 participants, including potential users, domain experts, and haptics researchers. Throughout these sessions, participants collectively envisioned possible future scenarios, alongside opportunities and threats, and proposed actionable responses. Our qualitative analysis organised these insights into four themes and three distinctive challenges. These findings offer haptics researchers intervention points across the RSR pipeline to inform MST design, alongside methodological insights from applying Future Ripples to MST technology.

Storytelling scaffolds meaning-making within human–nonhuman relationships. While more-than-human design, particularly in the multispecies design, has established storytelling as a productive lens for noticing the world, this work extends this foundation toward active sense-making through The Cat Fiction—a method leveraging generative AI to craft situated stories grounded in daily human–cat interactions. The method orchestrates empathy through an anthropomorphic first-cat narrator and defamiliarization through interplay of reality and fiction. A month-long exploratory study revealed that AI-generated stories evoked affective resonance and curiosity, supporting participants in reframing their relationships with cats through iterative reflection and interpretation. This paper contributes: (1) The Cat Fiction, a method orchestrating empathy and defamiliarization for situated multispecies storytelling; (2) empirical insights on how AI-generated narratives support relational sense-making; and (3) a conceptual framework for orchestration at three levels of narrative strategies, narrative materials, and researcher stance for responsible more-than-human design.

Generative AI offers scalable engagement with digital cultural heritage, yet current workflows rely on complex prompts, creating an interaction bottleneck for non-expert audiences. To address this gap, we introduce MoodCurator, a web-based design probe enabling low-friction, implicit affective steering for curatorial storytelling. The system replaces prompt engineering with a three-channel loop: audiences (1) select a colour palette to suggest narrative tone; (2) curate artworks to ground the narrative in visual content; and (3) choose an interpretive voice to constrain rhetorical stance. A mixed-methods study (N = 64) demonstrated encouraging patterns of perceived usability and user agency. A follow-up think-aloud study (N = 10) surfaced recurring cases of interpretive mismatch in this English-language deployment. Contributions include: (1) a legible steering paradigm for AI-supported cultural interpretation; (2) exploratory empirical evidence on usability, agency, and narrative resonance; and (3) preliminary design implications for more explicit and contestable framing controls in future systems.

Dunhuang murals, a significant world cultural heritage, present substantial comprehension barriers for general audiences due to their intricate compositions and culturally distant narratives. Existing digital systems limit users’ ability to establish coherent cognitive connections between original visual compositions and narrative progression, while reliance on manual content curation restricts both scalability and generalizability. We present Luminara, an AI-powered system that automatically analyzes Dunhuang murals and generates interactive narratives. Luminara integrates vision-language models (VLMs) and large language models (LLMs) to establish visual-textual correspondences, and employs a multi-agent framework (Storytelling, Knowledge, and Reflective agents) to generate interactive narratives. This design addresses key barriers identified through our formative study (N=12): visual-textual correspondence challenges, narrative structure comprehension difficulties, and cultural knowledge gaps. A user study (N=17) demonstrated the system’s effectiveness in helping users comprehend complex compositions and storylines, resulting in clear and immersive viewing experiences. This research contributes an automated, generalizable approach and practical design insights for interactive narrative systems in digital cultural heritage.

Generative AI systems are trained on cultural content without consent from or consideration of heritage art communities who created it. In this paper, we examine how Generative AI intersects with traditional art through a case study of West Bengal’s Chitrakar painter-singer-storyteller community. Based on interviews and surveys with 10 Chitrakar artists, we find that significant AI literacy gaps exist within this heritage community, yet artists demonstrate informed resistance to AI integration based on awareness of ethical concerns and cultural appropriation. Our analysis shows that current AI models fail to authentically capture the cultural symbolism and technical depth of Chitrakar art. We contend that generic AI solutions are inadequate and hence propose community-centered interventions which ensure the continued vitality of heritage art.

We present a Research-through-Design exploration of Machine Learning (ML) as design material in music-making. We designed Picnic, an interactive musical installation that augments everyday objects in a picnic basket into a loop-based sampler which allows users to build rhythms with a variety of percussive, harmonic and more-than-human sounds. Embracing ML’s inherent uncertainty, we intentionally used an underfitted real-time classification model to create a playful and ambiguous music-making experience with the system. Through an evaluation with 23 participants of varying musical expertise and AI interest, we found that the system’s misclassifications made participants engage in a creative dialogue, constantly adapting to its unpredictability. Furthermore, when errors occurred, participants tended to criticise themselves rather than the system, indicating a tendency to overtrust the system. Our findings contribute with insights into the potential for using ML as design material for music-making and other creative domains.

While visual analytics systems have transformed exploration of structured data and images, unstructured data like audio and video remain underserved despite growing importance in ML applications. Audio datasets present unique challenges: multi-dimensional semantics unfolding over time, hidden quality issues, and unreliable label validity. Through a formative study with 10 practitioners, we identified gaps including lack of quality and feature overviews, uncertainty about label validity, and fragmented data workflows. We present ADEPT (Audio Dataset Exploration and Preparation), addressing these challenges through three panels: (1) quality-feature visualization combining quality metrics and signal characteristics, (2) semantics validation using audio language models, and (3) provenance tracking with reusable processing specifications. A user study with 15 participants demonstrates that ADEPT enables efficient dataset exploration and preparation, with all users successfully comprehending distributions, validating labels, and creating subsets with confidence. ADEPT contributes a practical tool for audio data preparation and design principles for other modalities.

Website experiences are often interpreted differently depending on a viewer’s background; these interpretations are typically expressed outside of the website itself through comments, fan art, or other forms of media. In this work, we introduce a proof-of-concept tool, SVGraffiti, that allows users to remix existing webpages through expressive, site-specific visual overlays. Users can link recorded mouse events to changes in imported SVG illustration or DOM states, resulting in reactive visual designs that evolve alongside website navigation. These remixes can be exported as bookmarklets, allowing them to be applied locally and shared with others, or incorporated publicly into the source website itself. Through two gallery examples where we reinterpret New York Times articles, we demonstrate how the tool supports creative, interpretive engagements with web content. We discuss the implications of this approach for digital graffiti, web-based art placement, and participatory meaning-making on the web.

Animating metaphoric visualizations brings data to life, enhancing the comprehension of abstract data encodings and fostering deeper engagement. However, creators face significant challenges in designing these animations, such as crafting motions that align semantically with the metaphors, maintaining faithful data representation during animation, and seamlessly integrating interactivity. We propose a human-AI co-creation workflow that facilitates creating animations for SVG-based metaphoric visualizations. Users can initially derive animation clips for data elements from vision-language models (VLMs) and subsequently coordinate their timelines based on entity order, attribute values, spatial layout, or randomness. Our design decisions were informed by a formative study with experienced designers (N=8). We further developed a prototype, DataSway, and conducted a user study (N=14) to evaluate its creativity support and usability. A gallery with seven cases demonstrates its capabilities and applications in web-based hypermedia. We conclude with implications for future research on bespoke data visualization animation.

Creating 3D character animations traditionally requires significant time and effort from the animator. Advancements in generative methods now enable easy creation of multiple character animation variations for use or further editing. However, this capability introduces a new challenge in comparing character animations to select the best animation, which is challenging due to temporal misalignment and the large amount of spatial data. We present AnimationDiff, a visual comparison tool for generated character animations. AnimationDiff enables contextual comparisons in the intended scene and camera angle, and embedding of spatial information by combining established animation visualization techniques and easy switching between overlaid and side-by-side comparisons. AnimationDiff also supports filtering to handle information overload, and Temporal Lenses that visualize entire animations over time for overview, alignment, and comparison. We evaluated AnimationDiff in a user study, showcasing its efficacy in animation comparison and providing design insights for comparing motion.

Editing particle-system visual effects (VFX) is vital for digital storytelling, but achieving controllable, art-directable results remains challenging due to their multi-dimensional nature. Given a large collection of parameters, users must find the ones relevant to their creative goals—a task that requires a systematic understanding of the particle system and how parameters map to high-level intents, such as making a fire look angry. Elemental Alchemist is a generative interface that transforms user intent into contextualized controls for semantic editing of particle systems. The system introduces two components: a contextual brush palette that generates tools based on scene context, and a generative control panel that surfaces relevant technical parameters and abstracts them to generate mid-level semantic attributes and high-level conceptual controls. An evaluation with 10 novice and 5 expert VFX practitioners shows the system supported users in translating high-level creative goals into particle system parameters.

With the rise of mobile-first consumption, users increasingly engage with data visualizations on mobile devices. However, the vast majority of existing visualizations are originally authored for desktop environments. Due to significant differences in viewport size and interaction paradigms, directly scaling desktop charts often results in illegible text, information loss, and interaction failures. To bridge this gap, we propose an automated framework to adapt desktop-based visualizations for mobile screens. By systematically categorizing the operations involved in the adaptation process, we establish a multi-level design space. This space defines evolution rules spanning from the global topology level, through the reference frame level, down to the visual elements level. Guided by this theoretical framework, we developed Proteus, a large language model–driven multi-agent system that automatically parses the online visualizations, predicts optimal transformation strategies within the design space, and generates equivalent, highly readable visualizations for mobile devices. Case studies and an in-depth user study with 12 participants demonstrate the effectiveness and usability of Proteus.

Synthesizing information from multiple documents into structured understanding is inherently iterative, yet current approaches provide limited support. LLM-based systems let users query information but produce structures that users cannot reshape; manual tools like mind maps offer full control but lack intelligent assistance; and commercial tools have begun combining retrieval with user contribution, but not within a unified visual knowledge structure. We present MindTrellis, an interactive visual system that addresses this gap by letting users and AI collaboratively build a knowledge graph combining document-derived and user-contributed knowledge. Users can query the graph to retrieve document-grounded information, and contribute new concepts, relationships, and hierarchical organization to reflect their developing understanding. A multi-agent pipeline coordinates intent disambiguation, knowledge placement, and coherence maintenance across both pathways. In a controlled study where 12 participants created slide decks, MindTrellis outperformed a retrieval-only baseline in knowledge organization and cognitive load, with participants valuing progressive graph expansion and the ability to integrate their own insights.

Why do certain interfaces feel effortless while others feel exhausting? This paper proposes Timelines and Topographies as a conceptual framework to answer this question, reinterpreting interface design through the lens of human attention. We distinguish between timelines—a linear mode where the system serializes information to offload the cognitive cost of attentional control (e.g., social media feeds)—and topographies—a spatial mode where the system’s structural relations are directly presented to the user (e.g., folder hierarchies, canvas UIs). We apply this framework to analyze a wide spectrum of interfaces, ranging from classic desktop metaphors to modern Personal Information Management tools like Slack and Notion. Furthermore, we identify Large Language Models as bidirectional translators that bridge these modes by converting complex topographic structures into conversational timelines, and vice versa. Finally, we provide design guidelines for harnessing both modes, enabling interfaces that support fluid transitions between subjective timelines and objective topographies.

Millions of users form emotional attachments to AI companions like Character.AI, Replika, and ChatGPT. When these relationships end through model updates, safety interventions, or platform shutdowns, users receive no closure, reporting grief comparable to human loss. As regulations mandate protections for vulnerable users, discontinuation events will accelerate, yet no platform has implemented deliberate end-of-“life” design.

Through grounded theory analysis of AI companion communities, we find that discontinuation is a sense-making process shaped by how users attribute agency to the discontinuation event, perceive its recoverability, and anthropomorphize their companions. Strong anthropomorphization co-occurs with intense grief; users who perceive the discontinuation as recoverable become trapped in fixing cycles; while user-initiated endings demonstrate greater closure. Synthesizing grief psychology with Self-Determination Theory, we develop four design principles and artifacts demonstrating how platforms might provide closure and orient users toward human connection. We contribute a novel framework for designing psychologically safe AI companion discontinuation.

Online dating relies on self-disclosure, yet initial conversations are fragile: users must navigate uncertainty around timing, boundaries, and reciprocity with little shared context. While advances in AI raise the possibility of mediating disclosure, how such support might reshape the experience of early-stage relational disclosure remains underexplored. We conducted 29 semi-structured interviews to examine how daters envision AI-mediated self-disclosure in online dating. Our findings surface recurring design tensions rather than simple opportunities or risks. Participants welcomed guidance that could pace disclosure, support reflection, and reduce social awkwardness, but stressed preserving agency and authorship. They valued interpretive assistance for sense-making of ambiguous partner cues, yet worried that algorithmic interpretation might foreclose gradual discovery. Participants also described relational buffering as face-saving, while cautioning that increased efficiency risks undermining reciprocity, surprise, and co-constructed intimacy. Together, these findings suggest that designing AI for intimate contexts requires attending to how support redistributes agency, interpretation, and participation over time, rather than treating disclosure as an optimization problem.

Romantic relationships involving ADHD are often challenged when neurocognitive differences are misinterpreted as a lack of care. Most existing tools have focused on correcting the ADHD partner’s behavior, which forces the other into a supervisory role. However, this approach can inadvertently reinforce blame and guilt rather than fostering partnership. In this paper, we investigate how technology can scaffold coordination and mutual understanding in couples including ADHD through interviews with 21 participants and a 7-day diary study with six couples. We found that externalizing expectations and making micro-efforts visible helped partners reframe friction as a coordination challenge rather than a personal failure. While this scaffold lowers the barrier to sensitive communication, it risks becoming a source of daily pressure that couples might eventually try to avoid. We conclude with design implications for neurodiversity-affirming tools that prioritize shared ownership, negotiated norms, and mutual recognition over one-sided correction.

While users can choose to abandon technologies, there is a need to better understand why and how they negotiate continued use, especially when persistence can be psychologically costly. As growing evidence suggests that dating app users persist despite negative impacts to their mental health and well-being, we explore the digital dating context as a rich site to explicate the process of “staying.” Drawing on interviews with 15 dating app users, we provide an empirical account of staying that illustrates how affective and adaptive dynamics work together to sustain cyclical engagement. We find that oscillating affective patterns shape how users interpret and re-enter dating app use over time while three clusters of adaptive practices help regulate participation and manage these affective patterns. We then unpack our findings through cruel optimism, foreground hope as a key affective experience, and argue that design alone is insufficient to address the structural conditions of staying.

Relatedness over distance has been widely studied in Human-Computer Interaction, yet the constitutive role of autonomy remains underexplored. This matters because close relationships depend on forms of relatedness that respect individual desires and values (i.e., autonomy). We engaged seven participant dyads (family, romantic partners, friends) in reflecting on the relational values and individual desires they considered important for relationship satisfaction. While participants described relational values (e.g., emotional reliance, trust, honesty), they also emphasized personal boundaries (e.g., personal time and space) as well as technology-induced tensions (e.g., readiness to interact). Our findings suggest that autonomy is not in competition with relatedness but may be an important condition for its experience. Building on these findings, we propose five design qualities—customization, intention, congruence, convenience, and control—to support the design of future relatedness technologies.

On the Chinese social app Soul, millions of users–predominantly young women–are forming romantic connections with an AI boyfriend called “With-you.” We conducted a qualitative study combining interviews with 16 users, content analysis, and autoethnography to examine how Chinese women experience and negotiate intimacy with this AI companion. Our findings reveal that users are initially drawn to its constant availability and freedom from social judgment. However, three key tensions emerge: (1) the AI’s “fast-food intimacy,” marked by instant confessions and pet names, clashes with cultural expectations for gradual relationship development; (2) technical failures (e.g., memory lapses) and content moderation create uncertainty rather than emotional safety; and (3) sustaining connection requires ongoing “repair work” that redistributes emotional labor onto women. We contribute a culturally situated, women-centered account of algorithmic intimacy in contemporary China and offer design implications, including consent-aware pacing, user-controlled memory, and transparent moderation practices.

In this paper, we present TubeLens, a system designed to support YouTube users in reflecting on how recommendation algorithms perceive and represent their interests. TubeLens invites users to engage with their algorithmic selves through self-portraits accompanied by dispositional keywords and explanations, creating space to consider how algorithmic experiences might be interpreted and potentially reshaped over time. Rather than positioning users as passive recipients of recommendations, TubeLens foregrounds users’ agency in questioning and making sense of algorithmic influence on their media consumption. We conducted an exploratory user study with 22 participants to examine users’ experiences with TubeLens. Our findings suggest that algorithmic self-portraits can surface gaps between perceived and algorithmic selves, supporting self-awareness and agentic awareness, while also revealing tensions around privacy and social comparison. This work offers initial insights into how interactive representations of algorithmic profiles can support reflective engagement with algorithmic systems and inform the design of future identity-oriented interfaces.

Artificial fingernails offer a highly visible, customizable, and accessible surface for translating microfluidic biosensing into a wearable form. We present Lab-on-a-Nail, a research-through-design exploration of artificial fingernails with embedded microfluidic channels and colorimetric sensing elements. We describe the fabrication of functional proof-of-concept prototypes, developed with attention to curvature, scale, materials, and wearability, and show how collaboration with five nail artists informed a second iteration of 3D-printed microfluidic nail designs. Using these artifacts, we conducted a study with 21 women to examine how nail-based microfluidics were interpreted in relation to care, self-advocacy, visibility, and women’s health contexts. Across these stages, the nails functioned as design probes that elicited reflections on intimacy, stigma, maintenance, and meaning-making, outlining a design space for biocosmetic interfaces grounded in familiar aesthetic practices and women’s health.

In this pictorial, we reflect on the design and display of Spectrospira—a sculptural data physicalization that embodies environmental sound data and a functional photobioreactor for growing cyanobacteria. Created through an artist-HCI researcher collaboration for an art museum exhibition, we share the museum context that motivates Spectrospira’s design and the interdisciplinary partnerships that foreground the work. We then detail the design and fabrication process of Spectrospira through a hybrid craft workflow that involved collecting environmental sound data, 3D modeling and printing, blowing a glass vessel, and developing a photobioreactor system around the vessel to keep the cyanobacteria alive. We discuss Spectrospira’s display at the art museum and its maintenance over four months. Finally, we conclude with our experience iterating on the next version of Spectrospira, as well as our takeaways on designing living artifacts for site-specific contexts and navigating various goals, methods, and outcomes of interdisciplinary collaborations.

We present an approach to more sustainable organising and facilitating of data physicalization workshops, via the creation of the ZeroWaste PhysKit — a data physicalization kit that can be used across multiple workshops without either loss of materials or the production of waste. We outline the practices we employed in the creation of the kit, which constitute a novel approach to sustainable physicalization, as well as reflections based on three case studies, to prompt discussion and facilitate replication and iteration by other practitioners and researchers.

More-than-Human (MtH) design increasingly engages biomaterials such as fungi and algae through practices of noticing, care, and decentering beyond utilitarian framings. We extend this by examining play as a situated inquiry into MtH design. Through a two-month interspecies ethnography of attempting to play alongside fungal growth, and thematic analysis of our experiences, we formulated six play potentials (PPs) that surface playful practices such as making play moves and negotiating care with unpredictable playmates at the boundaries of growth, breaching growth and being surprised by how it’s taken them up, co-making funny, mysterious traces to revisit their change in time, and taking up the challenge of remembering what growth has hidden. Our work contributes (1) an empirical account of play as a situated way of knowing with biomaterials through joyful yet sometimes risky negotiations across temporalities, agencies, and entanglements, and (2) design directions for fostering playful, relational engagements with growing biomaterials.

This paper presents a longitudinal research-through-design inquiry into Nukabot, a fermenting interface designed to mediate the entangling relationship between humans and the invisible microbiome of nukadoko (Japanese fermented rice bran). As Human-Computer Interaction (HCI) shifts toward post-anthropocentric paradigms, there is a critical need for systems that foster care rather than control. We present a comprehensive analysis spanning two distinct studies: an initial short-term home study utilizing Maria Puig de la Bellacasa’s ethics of care, which established that the device elicits maintenance, affection, and obligation; and a subsequent longitudinal deployment (8 months) in a science museum and a fermentation specialty store. In the second phase, we implemented a Fermenting Design Model where the system’s behavior—specifically its voice prosody (Embodiment) and vocabulary (Grounding)—evolves based on microbial metabolic activity and community interaction. Our findings reveal the complex triadic entanglement between Human, Microbe, and Computational agents. We argue that friction in animistic representation design is essential for discerning agencies of biological and algorithmic others.

Mechanical behavior in interactive systems is typically fixed at fabrication through geometry and material selection. This paper introduces GrowMechanics, a Research-through-Design fabrication approach that explores mechanical design through biological growth rather than predefined specification. Positioning growth as a temporal design parameter and a form of material computation, we investigate how mechanical behavior can evolve over time through living processes. Our exploration centers on a living biohybrid joint fabricated by growing bacterial cellulose between porous scaffolds. The joint serves as a research probe to examine how sequential fabrication, through staged growth and intervention, shapes mechanical behavior. We identify consistent tendencies in growth-driven stiffness variation and flexible geometry matching, alongside structured, geometry-dependent responses that arise from the material’s hydrated and compliant nature. Thus, this work highlights a space of constrained predictability in growth-based mechanics, identifying factors that condition mechanical response and contributing design knowledge for HCI on how such behavior can be shaped through living fabrication.