注目の論文一覧

Generative Artificial Intelligence (GAI) offers new opportunities for reconstructing these unrecorded memory scenes, yet existing web-based tools undermine users' sense of agency through disengaging and unpredictable interactions. In this work, we advance three design arguments about how slow, tangible interaction can reshape human–AI relationships by making temporality, embodied agency, and generative processes experientially legible. We instantiate these arguments by presenting Memory Printer, a tangible design exemplar that combines silk-screen printing metaphors with text-to-image generation. The design features layered reconstruction that decomposes image generation into incremental steps, a physical wooden scraper enabling embodied control over image revelation, and built-in printing that produces tangible photos. We examine these arguments through a comparative study with 24 participants, exploring how participants engage with, interpret, and respond to this interaction stance. The study surfaces both opportunities—such as vivid memory evocation, heightened sense of control, and creative exploration—and critical tensions, including risks of false memory formation, algorithmic bias, and data privacy. Together, these findings articulate important boundaries for deploying generative AI in emotionally sensitive contexts.

We present ViSTAR, a Virtual Skill Training system in AR that supports self-guided basketball skill practice, with feedback on balance, posture, and timing. From a formative study with basketball players and coaches, the system addresses three challenges: understanding skills, identifying errors, and correcting mistakes. ViSTAR follows the Behavioral Skills Training (BST) framework—instruction, modeling, rehearsal, and feedback. It provides feedback through visual overlays, rhythm and timing cues, and an AI-powered coaching agent using 3D motion reconstruction. We generate verbal feedback by analyzing spatio-temporal joint data and mapping features to natural-language coaching cues via a Large Language Model (LLM). A key novelty is this feedback generation: motion features become concise coaching insights. In two studies (N=16), participants generally preferred our AI-generated feedback to coach feedback and reported that ViSTAR helped them notice posture and balance issues and refine movements beyond self-observation.

Interacting with multiple objects simultaneously makes us fast. A pre-step to this interaction is to select the objects, i.e., multi-object selection, which is enabled through two steps: (1) toggling multi-selection mode --- mode-switching --- and then (2) selecting all the intended objects --- subselection. In extended reality (XR), each step can be performed with the eyes, hands, and voice. To examine how design choices affect user performance, we evaluated four mode-switching (\Semipinch, \Fullpinch, \Doublepinch, and \Voice) and three subselection techniques (Gaze+Dwell, Gaze+Pinch, and Gaze+Voice) in a user study. Results revealed that while \Doublepinch paired with Gaze+Pinch yielded the highest overall performance, \Semipinch achieved the lowest performance. Although \Voice-based mode-switching showed benefits, Gaze+Voice subselection was less favored, as the required repetitive vocal commands were perceived as tedious. Overall, these findings provide empirical insights and inform design recommendations for multi-selection techniques in XR.

While generative AI tools are increasingly adopted for creative and analytical tasks, their role in interpretive practices,where meaning is subjective, plural, and non-causal, remains poorly understood. This paper examines AI-assisted tarot reading, a divinatory practice in which users pose a query, draw cards through a randomized process, and ask AI systems to interpret the resulting symbols. Drawing on interviews with tarot practitioners and Hartmut Rosa's Theory of Resonance, we investigate how users seek, negotiate, and evaluate resonant interpretations in a context where no causal relationship exists between the query and the data being interpreted. We identify distinct ways practitioners incorporate AI into their interpretive workflows, including using AI to navigate uncertainty and self-doubt, explore alternative perspectives, and streamline or extend existing divinatory practices. Based on these findings, we offer design recommendations for AI systems that support interpretive meaning-making without collapsing ambiguity or foreclosing user agency.

In social virtual reality (VR) platforms, players can embody "non-human" avatars, which are representations whose appearance or skeletal structure diverge from typical human characteristics. This capability fosters the emergence of distinctive cultures of social interaction. This paper reports on interviews with users who employ such avatars, investigating (1) motivations for their adoption, (2) their impact on social interactions, and (3) challenges encountered when employing them in social contexts. Our findings reveal that users adopt "non-human" avatars both to escape the expectations and norms associated with the human body—thereby enabling more relaxed social communication—and to gain access to new forms of embodied experience and creative self-expression. The study also provides empirical evidence and discussion on the cultures of social interaction mediated by alternative embodiments, changes in bodily perception resulting from prolonged use, functional and social challenges related to avatar use, and the design strategies and etiquette practices developed to overcome them.

Eye strain presents a significant challenge in human-information interaction in virtual reality (VR), as prolonged exposure contributes to various eye problems. This study introduces a new gaze redirection method called EyeXRciser, which passively activates eye movements to help prevent eye muscle stiffness during VR reading. This method achieves gaze redirection by slowly shifting the relative position of the text window within the user’s field of view through a head-bound coordinate system. We implemented our method with two different redirection speeds (i.e., unnoticeable speed at 0.03 $rad/s$; noticeable speed at 0.12 rad/s) and conducted a user study (N=24) comparing with a baseline using a fixed text window. Results show that both our methods successfully minimized the decline in accommodative ability caused by prolonged reading, without negatively impacting reading comprehension. Results also show that the unnoticeable redirection speed produced less subjective discomfort, eye fatigue, and reading distraction than the noticeable speed.

Large Language Models (LLMs) hold promise in supporting creative writing, yet the role of proactive AI in collaborative writing remains underexplored due to concerns around human agency and disruption. To investigate effective strategies for proactive AI support, we conducted a Wizard-of-Oz study simulating two suggestion styles: intrusive suggestions (next-sentence completions) and non-intrusive suggestions (exploratory proposals), where participants completed two story outlining tasks under each style, receiving real-time proactive suggestions from a human wizard acting as the AI. Both quantitative and qualitative results show that proactive AI can enhance creativity and accelerate writing. However, we observed a trade-off between AI involvement and perceived human agency. This trade-off was moderated by how strongly AI stimulated users—greater inspiration led to stronger perceived agency even under high AI involvement. Based on wizards' behavior, we offer guidance on suggestion style and timing to better balance creativity and agency for future proactive AI writing systems.

Hybrid meetings are the new reality, yet they lack the richness of face-to-face interaction. In shared spaces, virtual or physical, interaction relies on more than words: proximity, non-verbal cues, and subtle movements all shape communication. Proximity captures how close we stand, where we face, and how we move around others. This paper investigates how proxemics in dyad and triad conversations translate across physical and virtual contexts. We conducted a study with 24 participants in four groups, completing social tasks under four conditions: face-to-face, co-located XR, remote XR, and hybrid XR. Our instrumentation of physical and virtual environments enables direct comparison. The work contributes a rich open dataset of 2.3 million rows across 32 columns, supporting comparative and replicable analysis. This is the first study to compare proxemics across face-to-face, co-located XR, remote XR, and hybrid XR, offering a foundation for understanding how social space translates across contexts.

Given the growing prevalence of fake information, including increasingly realistic AI-generated news, there is an urgent need to train people to better evaluate and detect misinformation. While interactions with AI have been shown to durably reduce people's beliefs in false information, it is unclear whether these interactions also teach people the skills to discern false information themselves. We conducted a month-long study where 67 participants classified news headline-image pairs as real or fake, discussed their assessments with an AI system, followed by an unassisted evaluation of unseen news items to measure accuracy before, during, and after AI assistance. While AI assistance produced immediate improvements during AI-assisted sessions (+21\% average), participants' unassisted performance on new items declined significantly by 15.3\% in week 4 compared to week 0. These results indicate that while AI may help immediately, it may ultimately degrade long-term misinformation detection abilities.

Online communities often develop shared symbolic vocabularies that strengthen insider bonds but implicitly marginalize newcomers. On Chinese platforms, this dynamic is exemplified by “absurd language,” a style distinguished by irony, exaggeration, and local memes. While this form of expression fosters in-group intimacy, it creates significant cultural barriers for “Sino-digital non-natives.” This study investigates how AI can mediate cultural integration beyond mere translation. We developed an AI mediator integrating Chain-of-Thought (CoT) and Retrieval-Augmented Generation (RAG) to scaffold this journey. A mixed-methods evaluation (N=14) demonstrates significant improvements in comprehension accuracy over a baseline LLM. Crucially, our qualitative analysis reveals a novel five-stage model of cultural integration. This model charts the user's journey from peripheral observation to confident participation, detailing the AI's evolving role from “expert guide” to “creative collaborator.” Our findings illuminate the dynamics of agency and trust, offering a framework for designing AI as a catalyst for community integration.

Sketching provides an intuitive way to convey dynamic intent in animation authoring (i.e., how elements change over time and space), making it a natural medium for automatic content creation. Yet existing approaches often constrain sketches to fixed command tokens or predefined visual forms, overlooking their free-form nature and the central role of humans in shaping intention. To address this, we introduce an interaction paradigm where users convey dynamic intent to a vision–language model via free-form sketching, instantiated here in a sketch storyboard to motion graphics workflow. We implement an interface and improve it through a three-stage study with 24 participants. The study shows how sketches convey motion with minimal input, how their inherent ambiguity requires users to be involved for clarification, and how sketches can visually guide video refinement. Our findings reveal the potential of sketch–AI interaction to bridge the gap between intention and outcome, and demonstrate its applicability to 3D animation and video generation.

Effective conceptual design collaboration requires teams to build shared mental models (SMMs). Although Extended Reality (XR) technologies support design collaboration, they often lack structured cognition support for such alignment. To address this, we conducted this research within the sandbox of automotive design, and firstly identified key cognitive challenges in its collaboration. We then developed XSynth, a GenAI-powered XR system grounded in Concept–Knowledge Theory. XSynth scaffolds designers’ reasoning, externalizes individual mental models as knowledge graphs, and merges them into a unified graph to facilitate SMMs building. We evaluated XSynth in a within-subject experiment containing 10 design teams (N=30) using mixed-method approach. Results showed that XSynth significantly reduced workload, enhanced creativity support, strengthened perceived SMMs, and improved design performance. This research contributes to HCI by introducing the design and implementation of a theory-grounded, GenAI-powered, XR-based cognition support tool. It also offers empirical evidence into the effectiveness of XSynth, and design implications for future cognition support tools in collaborative settings.

In what ways do generative AI (GenAI) tools embedded in mainstream design software affect UI design practices and outcomes? In this study, we examine the use of FigmaAI, a GenAI feature within the industry-standard platform Figma. We conducted a within-subject study with 16 professional UX designers, each of whom completed two high-fidelity UI design tasks in a think-aloud session: one conventionally, without AI, and one with FigmaAI. We analyse the design process itself alongside experiences and reflections on using and not using AI, gathered from adjacent semi-structured interviews and post-task questionnaires. Our findings suggest that GenAI reshapes UI workflows, shifting them from additive to subtractive processes: designers refine AI drafts rather than building interfaces from scratch. While AI reduces workload and accelerates initial setup, it also constrains exploration, limits perceived ownership, and produces designs that are more visually and structurally similar.

\textit{What if future dining involved eating robots?} We explore this question through a playful and poetic experiential dinner theater: a tangible design fiction staged as a 2052 Paris restaurant where diners consume a biohybrid flying robot in place of the banned delicacy of ortolan bunting. Moving beyond textual or visual speculation, our “dinner-in-the-drama” combined performance, ritual, and multisensory immersion to provoke reflection on sustainability, ethics, and cultural identity. Six participants from creative industries engaged as diners and role-players, responding with curiosity, discomfort, and philosophical debate. They imagined biohybrids as both plausible and unsettling—raising questions of sentience, symbolism, and technology adoption that extend beyond conventional sustainability framings of synthetic meat. Our contributions to HCI are threefold: (i) a speculative artifact that stages robots as food, (ii) empirical insights into how people negotiate cultural and ethical boundaries in post-natural eating, and (iii) a methodological advance in embodied, multisensory design fiction.

We present a fabrication system called AirForce that allows users to create large-scale, load-bearing animated structures from a single inflatable tube. AirForce builds on the personal fabrication of animated truss structures, based on which it replaces not only the static elements with tube, but also introduces tube-based actuators that integrate with that same tube. This ‘single-tube’ design affords efficient single-person assembly, excellent power-to-weight ratio, easy transport and setup, and 100% material reuse. We show three variants of actuators: buckling actuators for pushing, muscle actuators for pulling, and telescoping actuators for large forces. Our blender plugin enables users to place actuators in structures and export instructions for efficient fabrication. We demonstrate a 6DOF motion platform that lifts humans and an 8m high animatronic T-rex that animates with 3DOF, enabled by custom hardware components. In our technical evaluation, the three actuators delivered 480N, 1420N, and 2330N peak forces, respectively.

Aggressive driving often triggers anger and retaliatory behaviors, posing threats to traffic safety. This paper proposes an AI-driven apology mechanism based on an Augmented Reality Head-Up Display (AR-HUD), which delivers immediate apologies on behalf of offending drivers during traffic conflicts and repairs damaged social relations through prosocial lies. We conducted a 2 (scenario risk: high vs. low) × 5 (apology depth) mixed-design experiment (N = 40) to evaluate its effectiveness. Results show that AI apologies enhanced positive emotions and forgiveness intentions while reducing anger, with participants also perceiving psychological benefits. These effects were consistent across both high- and low-risk scenarios. Our findings offer a practical design pathway for human-AI emotional regulation in traffic contexts.

We propose and explore the concept of Partial Participation, facilitating remote collaborators to contribute to meetings in which they are not able to fully participate via an AI agent acting as a proxy. During the meeting, users can monitor LLM-generated real-time meeting updates and respond to questions posed by other attendees. Through a mixed-methods user study with 24 participants using our prototype, ProxyMe, we investigated how the frequency of updates (high vs. low) and the type of response style (multiple choice vs. text input) impact perceived presence and mental workload. Our findings reveal that no single setup is universally optimal, and the partial participation fosters a moderate level of social presence and attentional mental workload. Our contributions introduce partial participation as a new paradigm for remote collaboration and highlight how AI can mediate participation when full presence is not feasible.

Many potential benefits of data physicalizations are thought to stem from their tangible nature and their presence in a shared space, which allows for physical interaction in a social environment. Prior research has studied where observers touch data physicalizations and how these touches depend on task. However, limited research explores the moment-by-moment details of touches, gestures, and verbal dialogue and how these interactions contribute to the larger data physicalization sensemaking process. This case study offers a new data analysis from a previous study to provide fine-grained accounting of one participant’s touches, gestures, and conversations around a data object. We identify four types of touches and gestures and describe relational patterns between individual interactions. This work provides a foundation for further exploring the reasons people touch or gesture with data physicalizations, connecting these efforts with gesture studies research, and identifying design implications for data physicalization.

Despite increasing interest in Hybrid Digital Boardgames (HDBs) that necessarily combine smart technology with a physical board- game, little is known about how the design of hybrid functions impacts player experience (PX). Additionally, it is unclear whether existing, videogame-centric, PX measures apply to tabletop set- tings. We designed a mixed-methods study to examine the PX of a boardgame in both its published form and as a custom HDB. We learned that players’ expectations of the “core” gameplay influence their perceptions of technology, highlighting a balance between the values and drawbacks of hybridity in relation to its impact on their sense of autonomy, fairness, and challenge in play. Our results also highlight methodological considerations for future tabletop PX studies, and suggest the existence of a Goldilocks Factor where the game offers “just right” hybridity that satisfies players without impacting core gameplay.

Habitual static stretching is recommended in many ergonomics guidelines, but continuing this habit remains challenging to workers. We address two factors causing this difficulty: most workers lack an understanding of the appropriate mechanisms of stretching, and stretching sessions require the interruption of ongoing tasks. We propose Screen-Directed Stretching, a novel technique that supports neck stretching in extended reality (XR) workspaces without interrupting work. Our technique temporarily repositions the virtual screen that the user is focusing on, guiding them to rotate their head in yaw, pitch, and roll directions and to maintain a posture for a specific duration, thus facilitating muscle extension. Through two preliminary user studies, we developed a prototype that seamlessly switches the screen's coordinate system between world-bound and body-bound frames of reference, balancing practical workability and good stretching guidance. A user study (N=16) demonstrates that our technique effectively induces stretching movements while minimizing loss of task efficiency.

Scrolling is ubiquitous in our daily computing experience. We explore how single-handed microgestures can be used for scrolling. Based on an analysis of the basic components necessary for scrolling, we selected 3 microgestures: Tap, Hold and Drag. Considering both rate and position controls, we designed 4 microgesture-based scrolling techniques adapted to these 3 microgestures. We contrasted these 4 techniques in a laboratory experiment with 24 participants who performed 2 tasks: a reciprocal selection task, where participants scrolled the view to reach and select a target; and a counting task, where participants scrolled the view to count image occurrences. Our results suggest that the technique based on Drag microgestures with rate control is the most effective for scrolling operations, regardless of the task. This work demonstrates that microgestures, with their advantages for frequent everyday tasks, offer a promising approach to continuous and efficient scrolling control.

Creative tasks require creators to iteratively produce, select, and discard potentially useful ideas. Now, creativity tools include generative AI features (e.g., Photoshop Generative Fill) that increase the number of alternatives creators consider through rapid experiments with prompts and random generations. Creators use tedious manual systems for organizing their prior ideas by saving file versions or hiding layers, but they lack the support they want for reusing prior alternatives in personal work or in communication with others. We present HistoryPalette, a system that supports exploration and reuse of prior designs in generative image creation and editing. Using HistoryPalette, creators and their collaborators explore a "palette" of prior design alternatives organized by spatial position, topic category, and creation time. HistoryPalette enables creators to quickly preview and reuse their prior work. In creative professional and client collaborator user studies, participants generated and edited images by exploring and reusing past design alternatives with HistoryPalette.

The dark patterns, deceptive interface designs manipulating user behaviors, have been extensively studied for their effects on human decision-making and autonomy. Yet, with the rising prominence of LLM-powered GUI agents that automate tasks from high-level intents, understanding how dark patterns affect agents is increasingly important. We present a two-phase empirical study examining how agents, human participants, and human-AI teams respond to 16 types of dark patterns across diverse scenarios. Phase 1 highlights that agents often fail to recognize dark patterns, and even when aware, prioritize task completion over protective action. Phase 2 revealed divergent failure modes: humans succumb due to cognitive shortcuts and habitual compliance, while agents falter from procedural blind spots. Human oversight improved avoidance but introduced costs such as attentional tunneling and cognitive load. Our findings show neither humans nor agents are uniformly resilient, and collaboration introduces new vulnerabilities, suggesting design needs for transparency, adjustable autonomy, and oversight.

This paper looks at texture---middle-level components---as an important aspect of drawing. We present a hardware tool, penPal, that is designed to support dynamic mark-making and direct creative actions at this level. By incorporating a tendon-driven continuum robot, penPal’s tip can move independently, giving the user a new axis of creative control. Combined with the user’s own manipulations, penPal allows for emergent combinations of computer and manual control over the rapid generation of diverse textures. Through a 10-participant study and a professional artist commission, we examine how users negotiate control by integrating multiple coordinate systems (their body, the paper, and penPal’s tip) as they construct compositions. We suggest some benefits of supporting users at the texture level, such as the ability to shift the primary focus of their activity, the ability to selectively defamiliarize the creative process for generative potential, and for pleasure.

Prototyping with 3D printing depends heavily on virtual modeling, which requires expertise and often leads to scale mismatches and inefficient iteration. Physical deformation through heating is possible but challenging, as heating is difficult to control and deformation remains complex. We introduce TF-Shell, a thermoformable shell that enables repeatable, localized thermoforming of 3D-printed prototypes. Leveraging shape-memory properties, TF-Shell allows 3D-printed objects to achieve volumetric deformation, restoration, and shape memorization within the physical prototyping process. With customizable features, it can be embedded into free-form models through a design tool. A user study shows that TF-Shell provides intuitive, convenient physical modification and expands iteration beyond virtual modeling. Technical evaluations confirm its thermoformability and repeatability, establishing TF-Shell as a practical approach for integrating physical deformation into 3D printing workflows.

Existing research has examined how artificial teammates influence collaboration within teams, but far less is known about their role in shaping interactions between teams. In particular, it remains unclear how transparent integration of AI teammates influences intergroup biases in competitive contexts. To investigate this, we designed StarHarvest, an online game where two hybrid teams (each consisting of one human and one bot, either concealed or revealed) competed for resources while bots elicited prosocial or antisocial behaviors. Drawing on data from 240 participants, we analyzed behavioral choices, evaluations, and resource allocations toward ingroup and outgroup members. Our findings show that hidden bots fostered stronger within-team coordination but also allowed asymmetric retribution toward weaker opponents. By contrast, revealed bots were treated as secondary teammates, reducing cohesion and shifting responsibility onto human partners. We conclude with design implications for socially responsible integration of artificial teammates, highlighting tensions between group-level and agent-level identities.

Urban HCI investigates how digital technologies shape human behaviour within the social, spatial, temporal dynamics of public space. Meanwhile, robotic furniture research demonstrates how the purposeful animation of mundane utilitarian elements can influence human behaviour in everyday contexts. Taken together, these strands highlight an untapped opportunity to investigate how animated public furniture could mediate social interaction in urban environments. In this paper, we present the design process and in-the-wild study of mobile robotic benches that reconfigure with a semi-outdoor public space. Our findings show that the gestural performance of the benches manifested three affordances perceived by passersby, they activated engagement as robots, redistributed engagement as spatial elements, and settled engagement as infrastructure. We proposed an Affordance Transition Model (ATM) describing how robotic furniture could proactively facilitate transition between these affordances to engage passersby. Our study bridges robotic furniture and urban HCI to activate human experience with the built environment purposefully.

Users of Virtual Reality (VR) primarily sense their environment through audiovisual cues. The lack of haptic feedback on their body can make them unaware of virtual obstacles outside their field of view. This lack of sensing can cause the user to unknowingly penetrate virtual objects, breaking the scene’s plausibility and disrupting the experience of other users in the same virtual space. We propose a haptic belt that increases the user’s scene awareness by rendering signals of collisions and proximity to virtual objects around the user. In a user study, we show that the belt improves spatial awareness both in a fast, high-stress scenario where the user's attention is limited and during a relaxed experience where the belt is the only source of information. The belt enables users to move closer to obstacles while reducing unintended collisions

AI has been increasingly integrated into screenwriting practice. In refinement, screenwriters expect AI to provide feedback that supports reflection across the internal perspective of characters and the external perspective of the overall story. However, existing AI tools cannot sufficiently coordinate the two perspectives to meet screenwriters' needs. To address this gap, we present DuoDrama, an AI system that generates feedback to assist screenwriters' reflection in refinement. To enable DuoDrama, based on performance theories and a formative study with nine professional screenwriters, we design the Experience-Grounded Feedback Generation Workflow for Human Reflection (ExReflect). In ExReflect, an AI agent adopts an experience role to generate experience and then shifts to an evaluation role to generate feedback based on the experience. A study with fourteen professional screenwriters shows that DuoDrama improves feedback quality and alignment and enhances the effectiveness, depth, and richness of reflection. We conclude by discussing broader implications and future directions.

Silent, eyes-free text entry remains challenging when speech and touch are impractical. Prior wearable systems required custom sensors or limited users to a small vocabulary. We present MorsEar, an IMU-only earable framework that maps near-ear micro-gestures such as taps for dot/dash; slide/pull/circle for space/delete/send, into character-level Morse, enabling unrestricted composition with a compact lexicon for lightweight on-device autocorrect. The result is a low-bandwidth, reduced-exposure communication channel that works eyes-free and voice-free in accessibility scenarios, silent zones, and constrained environments. MorsEar infers words using a physics-aware preprocessing stack and a compact CNN, feeding a tempo-adaptive segmentation with rolling buffers; an on-device decoder provides real-time feedback entirely on-phone. In a 24-participant study (with four accessibility users) across Silent, Cafe, and Metro, MorsEar achieved CER 7.3% and WER 12.5% → 7.8% (Autocorrect), with median WPM of 9.3/9.1/5.8, respectively. Similar to other accessibility-oriented encodings such as Braille, Morse requires a brief familiarization period to learn the timing and rhythm of dots and dashes; after which, MorsEar shows that commodity earable IMUs can support discreet, low-exposure text entry that scales beyond discrete commands to language-level interaction.

Current AI writing tools aim to enhance authorial capacity yet often diminish authorial control and lack timely audience feedback. Through a formative study with fiction authors (N=10), we uncovered two critical tensions in human–AI co-writing: balancing AI scaffolding with authorial ownership, and the absence of contextual audience perspectives that shape storytelling during drafting. Guided by these insights, we designed Plotania, a co-writing system that combines proactive virtual readers offering real-time audience reactions with transparent attribution layers. A controlled study (N=20) revealed complex and counterintuitive effects: virtual reader feedback increased audience awareness but decreased perceived creative agency, transforming individual authorship into collaborative performance. Transparent attribution raised awareness of AI contributions but triggered identity anxiety and reduced AI usage. These findings reveal fundamental trade-offs in transparency design. We contribute design principles for "agency-preserving transparency" that balance information provision with creative empowerment, informing future transparency design in human-AI creative collaboration.

Solid knitting is a fabrication technique for producing dense 3D volumes through knitting. Designing such objects is difficult because yarn paths must remain continuous—formed from one (or a few) yarn(s)—while handling increases and decreases across three dimensions. To address this challenge, we introduce skCAD, a block-based design tool that allows users to compose 3D forms by stacking rectangular blocks, which are then automatically converted into solid-knitting patterns. To build skCAD, we also standardized a grammar of solid knitting that formalizes stitch and row operations, making it possible to construct patterns beyond basic shapes. Our tool and grammar enable the creation of complex solid-knitted objects, and help those interested to learn and explore this technique. We evaluated the system in a workshop with knitters, yielding insights into design needs and directions for future development.

Pre-visit information can enrich museum experiences, yet creates a dilemma: text-only descriptions can overwhelm without visual anchors, while viewing artworks in advance can spoil surprise. To address this tension, we introduce TwistLens, a docent-informed, AI-supported image transformation system that generates twisted previews--transformed images that convey interpretive cues while concealing original visuals. TwistLens extracts key cues from docent text using a structured taxonomy, then applies two strategies: EchoLens, which preserves intended description while altering representation, and DecoyLens, which distorts described information while maintaining representational coherence. A co-design study identified strategy preferences by information type, informing category-specific refinements. A controlled evaluation further showed that TwistLens preserves anticipation, triggers curiosity, and supports active learning without visual spoil. These findings demonstrate how semantically-aware image transformation can balance knowledge delivery and anticipation in museum contexts.

Internet memes are central to online communication, yet their visual humor is often lost in face-to-face (F2F) conversations. Augmented reality (AR) offers new ways to bring memes into F2F interactions, but it is unclear how memes can be integrated into F2F conversations using AR, and how they impact conversational dynamics. We surveyed meme users (N=29) to understand motivations and challenges in visualising memes in F2F conversations. With these insights, we developed an AR meme-sharing prototype and invited 12 pairs of friends to design AR visualizations for their memes and use them in conversations. Our analysis reveals two AR-unique visualizations: merging memes with one's body (The-Meme-On-Me) and situating oneself in meme environment (Me-In-The-Meme). We observed two integration patterns: using speech as setup before a meme punchline, and showing memes simultaneously with speech to amplify humor. We report users’ reactions toward AR memes, showing how it enables playful social interaction.

Existing research in steering often focuses on static paths. However, in real-world systems, such as interfaces for lasso and video games, there are no guarantees that the paths will maintain their initial width and distance during steering. We thus explore steering movements on a dynamically varying path, widening or narrowing. To this end, we empirically studied the impact of several task parameters - including changed path width, path occupancy duration, and position - on steering through the dynamically varying path. As a result, we found different steering movements and performance to the previous steering task in the static path in terms of movement time and error rate. Additionally, we tested various previous extended models and found poor fitness results (mean $R^2_{adjusted}=0.53$) compared to the presented models (mean $R^2_{adjusted}=0.92$). We believe our model will assist GUI and game designers in evaluating their applications.

While augmented reality (AR) research demonstrates benefits of embedded visualizations for gross motor training, its applicability to facial exercises remains under-explored. Providing effective real-time feedback for facial muscle training presents unique design challenges, given the complexity of facial musculature. We developed three AR feedback approaches varying in spatial relationship to the user: situated (screen-fixed), proxy-embedded (on a mannequin), and fully embedded (overlaid on the user's face). In a within-subjects study (N=24), we measured exercise accuracy, cognitive load, and user preference during facial training tasks. The embedded feedback reduced cognitive load and received higher preference ratings, while the situated feedback enabled more precise corrections and higher accuracy. Qualitative analysis revealed a key design tension: embedded feedback improved experience but created self-consciousness and interpretive difficulty. We distill these insights into design considerations addressing the trade-offs for facial training systems, with implications for rehabilitation, performance training, and motor skill acquisition.

We present Y-zipper, a novel three-sided 3D-printed zipper structure that enables three flexible strips to interlock and transform into a rigid rod-like form. Building on this flex–rigid transition mechanism, we further design a specialized slider to achieve rapid and reversible zipping interactions. This slider serves as the basis for three actuation methods—manual, dynamic mechanical, and static mechanical—which enable both remote control and automated closure and release. In addition, Y-zipper provides four motion primitives: straight, bend, coil, and screw, whose combinations extend the flex–rigid transition mechanism to spatial curve structures. To support customization, we develop a computational design tool that automatically generates zipper geometry based on input primitives, unfolds the structure for 3D printing, and embeds both teeth and compliant bridges. Controlled experiments evaluate its mechanical properties, repeatability, and actuation speed, demonstrating robustness and reliability. Finally, we showcase a series of functional prototypes, including a medical wrist brace, a kinetic art installation, and a rapidly deployable tent structure.

When given the opportunity, people tend to try to reach coarse breakpoints for work interruptions. Coarse breakpoints are frequently associated with less effort when resuming the task. We investigated how supporting task resumption with augmented reality (AR)-cues affects this behavior. In a mixed factorial experiment, 50 participants performed a physical sorting task that included deferrable interruptions with varying distances to a coarse breakpoint, either with or without an AR-cue indicating the next correct step after interruption. Participants with AR-cue accepted interruptions at fine breakpoints more frequently than those without a cue, except when the coarse breakpoint was one step away, and reported less stress. Our findings indicate that AR-cues attenuate but do not eliminate the need for specific task resumption strategies, such as reaching a coarse breakpoint, and reduce the stress. Considering AR-cues for task resumption may be particularly beneficial for time-critical interruptions and fast-paced work environments.

Parametric modeling is a prevailing 3D modeling approach in design, architecture, and engineering. The emergence of multimodal large language models (LLMs) brings a new opportunity to lower the entry barriers to this powerful tool. However, describing 3D geometries through natural language can be fuzzy and challenging. We introduce co-speech gesture, a natural and expressive interaction modality to complement text prompts for LLM-empowered generative parametric modeling. We first conducted an elicitation study to explore and categorize co-speech gesture expressions. Based on the findings, we designed a multimodal fusion pipeline that parametrizes gestures and synthesizes them with speech. This approach reduces language ambiguity by translating implicit user intentions into explicit parametric attributes, thus lifting the model generation performance. We conducted a two-session user study testing and comparing it with traditional language and sketch inputs. This work streamlines the parametric modeling workflow and explores novel multimodal interaction paradigms for LLM-empowered design and creation.

Computer Use Agents (CUAs) operate interfaces by pointing, clicking, and typing - mirroring interactions of sighted users (SUs) who can thus monitor CUAs and share control. CUAs do not reflect interactions by blind and low-vision users (BLVUs) who use assistive technology (AT). BLVUs thus cannot easily collaborate with CUAs. To characterize the accessibility gap of CUAs, we present A11y-CUA, a dataset of BLVUs and SUs performing 60 everyday tasks with 40.4 hours and 158,325 events. Our dataset analysis reveals that our collected interaction traces quantitatively confirm distinct interaction styles between SU and BLVU groups (mouse- vs.keyboard-dominant) and demonstrate interaction diversity within each group (sequential vs. shortcut navigation for BLVUs). We then compare collected traces to state-of-the-art CUAs under default and AT conditions (keyboard-only, magnifier). The default CUA executed 78.3% of tasks successfully. But with the AT conditions, CUA’s performance dropped to 41.67% and 28.3% with keyboard-only and magnifier conditions respectively, and did not reflect nuances of real AT use. With our open A11y-CUA dataset, we aim to promote collaborative and accessible CUAs for everyone.

Creating interactive papercrafts often involves the processes of craft making (e.g., folding, cutting, gluing, etc.) and fabricating the embedded functional circuitry. These two processes are usually separated in the current practice, making the workflow laborious and affecting the in-paper circuit stability. To address the issue of the separated crafting and circuiting processes, we present LiqMetCraft, a toolkit for creating electronics-embedded papercrafts through an integrated process. The toolkit allows users to construct the craft structure by folding and cutting, and forms the circuit traces simultaneously. This is achieved with liquid-metal-dyed paper-like fabric which partially becomes conductive due to the cutting/folding-induced pressure while the unpressed parts of the paper remain insulated. The toolkit consists of software interfaces for papercraft design and hardware components, mainly the liquid-metal-dyed paper-like fabrics and other off-the-shelf components, for physical prototyping. The user studies shows that the participants quickly learned the toolkit and found the integrated process of circuit assembly and shape formation to be engaging and inspiring.

AI VTubers, where the performer is not human but algorithmically generated, introduce a new context for fandom. While human VTubers have been substantially studied for their cultural appeal, parasocial dynamics, and community economies, little is known about how audiences engage with their AI counterparts. To address this gap, we present a qualitative study of Neuro-sama, the most prominent AI VTuber. Our findings show that engagement is anchored in active co-creation: audiences are drawn by the AI's unpredictable yet entertaining interactions, cement loyalty through collective emotional events that trigger anthropomorphic projection, and sustain attachment via the AI's consistent persona. Financial support emerges not as a reward for performance but as a participatory mechanism for shaping livestream content, establishing a resilient fan economy built on ongoing interaction. These dynamics reveal how AI Vtuber fandom reshapes fan–creator relationships and offer implications for designing transparent and sustainable AI-mediated communities.

Large Language Models (LLMs) are becoming increasingly ubiquitous in daily life, impacting decision-making across various domains. A substantial body of prior work has shown that individuals tend to evaluate positive predictions more favorably than negative ones---a phenomenon often referred to as the personal validation effect---across various non-AI prediction sources. Building on this foundation, we extend this well-established psychological effect to the context of LLM-based predictions, examining how prediction valence influences users’ perceptions when the source is an AI system. We investigate how positive AI-generated responses affect perceived validity, personalization, reliability, and usefulness of chatbot predictions, even when those predictions are fictitious and pre-scripted. In a study of 238 participants, positive predictions were perceived as significantly more valid (36% increase), personalized (42% increase), reliable (27% increase), and useful (22% increase) than negative predictions. These findings demonstrate that the personal validation effect persists in interactions with LLMs and underscore the substantial role of prediction valence in shaping user perceptions, with important implications for the design and deployment of AI systems across diverse applications.

In science fiction writing, ideation demands both novelty to construct fictional worlds and consistency to maintain internal and temporal logic within those worlds. While large language models (LLMs) are increasingly adopted as co-creators, generated ideas often lack surprise and struggle to maintain consistency. Moreover, current interaction paradigms of human-AI co-creation systems fail to support the fragmented and iterative nature of science fiction ideation. To address these challenges, we introduce Sci-Fi Spark, a human-AI co-creation system to support inspiration and organization in the ideation phase. The system features an Ideation Canvas for visualizing relationships between fragmented ideas, a Novelty Generator that applies computational creativity strategies to produce novel worldbuilding inspirations, and a Consistency Generator to produce context-aware storyline suggestions. A technical evaluation and a user study with writers show that Sci-Fi Spark enhances both novelty and consistency, while enabling iterative co-ideation with LLMs.

While it is proven that the individual construction of a data physicalization aids personal sense-making, little is known about how sense-making is negotiated when it is shared by multiple, co-located participants. Since participatory data physicalization can inadvertently prioritize dominant views, we interpreted data feminism principles to design a collaborative physicalization construction process that empowers stakeholders and participants to co-determine how meanings are represented. This process revealed how the interplay of physical and non-physical actions during construction negotiations supported collaborative sense-making among 14 groups of 55 participants during 4 workshops, enabling us to articulate how explicit power is embodied by the physicalization artifact and negotiated between authoring and collaborating participants, and facilitators; whereas tacit power operates through artifact meanings, participant identity and design decisions. By providing one operationalization of data-feminist critique into the form of design requirements, our contributions support the design of more equitable physicalization and visualization construction methods.

Museums aim at personalizing visits to offer tailored content and encourage visitors to come back. Unfortunately, museum professionals find it difficult to reflect on the vast number of visitor profiles when creating personalized visits. We propose Museo* a new concept to support their creation strategy by allowing the selection of visitor characteristics. Through an interactive design process, we instantiated this concept with MuseoTUI, that displays the progress of visit creation by illuminating physical tokens, and MuseoGUI that displays on a standard touchscreen. In an in-situ comparative structured observation, we evaluated both prototypes. The concept Museo* is well understood and accepted with both interaction styles. MuseoGUI was perceived as more efficient, while MuseoTUI provided better stimulation, higher user experience and the physical manipulation was described as a valuable support for empathy, reflection, and creation. Based on these findings, we present design implications for future systems supporting the creation of personalized visits.

Shape-changing wearables are known to convey emotions to wearers and observers, and jewelry is commonly worn for self-expression and to be seen by others. But how do individual shape change parameters impact the emotions communicated? In a first study, participants observed a shape-changing necklace; the second included wearing it. The necklace uses pneumatic finger actuators; fabrication details are provided. We systematically varied motion type, speed, amplitude and repetition, and exterior material to analyze emotions using Russell's circumplex model. Additionally, we asked users what they associated with each shape change. We found some surprising relationships between shape change parameters and the valence and arousal levels of emotions wearers and observers perceived. Symmetrical actuations were recognized more accurately and received higher valence and arousal ratings. Interestingly, even when wearers, who only felt motions, misidentified them, their ratings matched those from observers. Our findings support creating shape-changing interfaces that communicate emotions more precisely.

We share a speculative fabulation that imagines alternative relations to an increasingly polluted landscape, drawing from environmental illness narratives. The fabulation is brought to life through Ray-Flats, shoes that glow in proximity to toxic sites listed in the U.S. Environmental Protection Agency (EPA)’s publicly available database, and an accompanying navigational guide. The storied designs seek to make the extent of chemical pollution in our everyday landscapes perceptible while foregrounding environmental illness experiences as a resource for living in and navigating a toxic world. Bringing together crip and posthuman perspectives, we contribute: 1) an example of designing to stay with environmental precarity, 2) the concept of “felt space" for making diffuse and ongoing environmental problems affectively perceptible through embodied interaction design, and 3) critical reflections on using fabulations with material speculations to foreground marginal perspectives in design.

Acquiring tacit knowledge and practical skills often depends on direct observation and in situ training. AR offers an alternative by overlaying first-person step-by-step instructions that guide users through tasks such as assembly and repair. Previous work demonstrates the effectiveness of AR instruction for specific applications. In our experimental work, we systematically explore aspects of the broader design space. We conducted a controlled experiment (n = 40) to investigate three key factors identified in learning theory and XR embodiment research: imitation timing (parallel vs. sequential), limb visualization (hand vs. full arm), and limb visibility (opaque vs. semi-transparent). Across all conditions, participants followed AR instructions and afterward repeated the tasks from memory. We assessed performance, user experience, and retention. Our results show that parallel imitation is faster and increases embodiment, whereas sequential imitation enhances memory retention and comfort. Our findings provide guidance for the temporal and visual design of first-person AR tutorials.

Gastric interoception influences eating behavior and emotions, making its modulation valuable for healthcare and human-computer-interaction applications. However, whether gastric interoception can be modulated noninvasively in humans remains unclear. While previous research indicates that abdominal-sound-driven haptic feedback resembles gut sensations, its impact on gastric feelings and gastric interoceptive behavior is unknown. We conducted three experiments totalling 55 participants to investigate how gut-sound-driven audio-haptic feedback applied to the stomach (1) affects user's feelings (2) influences perception of hunger and satiety levels and (3) influences gastric interoceptive behavior, quantified with Water Load Test-II. Results revealed that audio-haptic feedback patterns (a) induced the feelings of hunger, fullness, thirst, stomach upset, (b) increased hunger level, and (c) significantly increased volumes of ingested water. This work provides the first evidence that audio-haptic stimulation can alter gastric interoceptive behavior, motivating the use of noninvasive methods to influence users' feelings and behaviors in future applications.