注目の論文一覧

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.

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.

Plants are rarely positioned as active participants in digital games, serving as decorative elements or passive sensors. We present Plant.play(), a plant–digital game system that positions a living plant as the sole player in a pet-simulation game. Using bioelectrical signals, environmental data, and circadian rhythms, the plant autonomously performs caregiving actions while humans engage as observers. A workshop with five game experts informed the system's design, which was then implemented and deployed in a four-day exhibition. Observational fieldwork and interviews with twelve visitors revealed how people initially sought control, then gradually shifted toward interpreting the plant's slow, unpredictable, and impartial behaviors as meaningful play. Participants formed emotional connections with both the plant and the virtual pet, extending these reflections to their relationships with nonhuman beings. Our findings contribute empirical insights into interpretive engagement with nonhuman actors and offer design considerations for future plant–digital game systems that embrace materiality, perceived agency, and more-than-human perspectives.

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.

Current touch-based interactions on earphones are limited by hygiene concerns and the small interaction surface. Recent works attempt to bypass these issues with mid-air gesture systems using active acoustic sensing. However, these signals may be audible and pose potential hearing risks. To address this, we propose FingerBar, a mid-air gesture recognition system for earphones that relies solely on microphones without active signal transmission. FingerBar leverages the distinctive friction sounds generated by finger gestures to achieve gesture recognition. We design a gesture filtering pipeline to maintain robustness against daily noise. An adversarial training strategy further enhances user-independent performance. From a set of 16 gestures, we identify the 7 most suitable for FingerBar based on user acceptability. Extensive evaluations demonstrate high accuracy and robustness. Furthermore, a user study confirms the practicality and acceptability of the system. Our findings highlight the promise of passive acoustic sensing as a user-friendly interaction modality for earphones.

This paper uses research through game design to explore how we can make video games that invite players to invent their own personal play-practices through making and changing rules. Through a reflective process of designing and playtesting a multiplayer game in which changing rules and parameters is the central mechanic, we have identified how we can create opportunities for players to exert their own creative authority on the structure of their play-practices. As our contribution, we present three design themes which aim to invite player authorship on practices of gameplay: opening up digital rules and parameters, bringing internal rules to the surface, and leaving space for internal goals. We also bring a larger discussion of these design patterns in which we investigate the duality of responsibility and freedom in play when we design for player creativity, and the role of video games as tools to make metagames.

The call for novel approaches in participatory design (PD) and co-design (CD) as well as in educational settings has become louder in recent years, especially when it comes to considerations of empowerment and inclusion of marginalized perspectives. Co-designing edularps (educational live-action roleplaying games) is an innovative approach that could be used in both settings. This paper reports on the combination of three studies carried out in an educational context focusing on design thinking. Findings include indications that through co-designing edularps cooperation and creativity can be practiced, and empowerment and intrinsic motivation can be experienced. Cooperation, creativity, and empowerment are essential aspects in PD and CD as well as in education for future skills and design thinking. Intrinsic motivation is relevant in educational settings since it correlates with successful learning. This paper focuses on an evaluative examination of how co-design of edularps influences the practice and fostering of creativity, collaboration, empowerment, and intrinsic motivation. Based on the findings, incorporating edularp co-design into the repertoire of tools used in PD, CD, and educational settings is recommended.

The use of AI in product design during early creative phases raises questions about its long-term consequences. Concerns are that extended AI use might inhibit creative cognitive processes, especially in novice designers. The aim of this study is to contribute to ongoing research in creative cognition and creative support tools such as AI in design. We conducted an exploratory study with 61 undergraduate students to analyze design exploration in sketching versus AI concept generation. The results indicate that AI groups produced a higher quantity and variation of total ideas (including text-based ideas), while sketch groups generated more image-based ideas. It was inconclusive whether the final image concepts from both AI and sketch groups were more creative. Additionally, homogenization effects were observed in the AI groups. Moreover, while the evolution of the design intent was evident in students who sketched, the focus in AI groups appeared to shift towards the tool (AI), which we analyzed as different design space exploration (DSE) prompting styles.

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.

This study investigates how avatar customization in virtual reality (VR) impacts trust formation between unacquainted individuals and how such trust transfers to subsequent face-to-face (FtF) meetings. A user study with 48 participants was conducted, where participants were assigned to either a ``Similar-Self'' condition, with avatars resembling their real-world appearance, or an ``Alt-Self'' condition, with creative avatars. The results showed that ``Similar-Self'' avatars led to higher initial integrity-based trust perceptions, though both avatar conditions exhibited similar trust growth during VR encounters. Trust carried over from VR to FtF with a brief recalibration period and ultimately increased beyond VR levels in FtF encounters. This research provides insights into how VR can support the development of trust in early-stage interactions and offers implications for Social VR platforms to better support trustworthy interactions across virtual-physical boundaries.

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.

Shaking a cup of wine or other fluids in virtual environments is engaging but has been limited by challenges in delivering real-time haptic feedback for liquid collisions. ShakeSense is a haptic rendering system that integrates electrotactile stimulation with physics-based simulation to deliver immersive feedback for liquid dynamics in handheld containers. It employs a high-density electrode array to deliver dynamic tactile sensations, conveying friction and pressure changes on the user's fingerpad. A dedicated end-to-end pipeline computes fingerpad forces from liquid-container-finger interactions, ensuring feedback aligns with natural fluid movement. Two studies evaluated ShakeSense’s performance and user perception. Study 1 showed that electrotactile patterns were distinguishable across directions, and synchronizing container movement with stimulation enhanced perceived force changes. Study 2 demonstrated that ShakeSense effectively simulated liquid motion, capturing multidimensional, coordinated interactions, and outperformed conventional Center-of-Mass approaches. Overall, ShakeSense provides clear, fine-grained tactile feedback for fluid interactions.

We present a new and accurate approach for gaze estimation on consumer computing devices. We take advantage of continued strides in the quality of user-facing cameras found in e.g., smartphones, laptops, and desktops — 4K or greater in high-end devices — such that it is now possible to capture the 2D reflection of a device's screen in the user's eyes. This alone is insufficient for accurate gaze tracking due to the near-infinite variety of screen content. Crucially, however, the device knows what is being displayed on its own screen — in this work, we show this information allows for robust segmentation of the reflection, the location and size of which encodes the user's screen-relative gaze target. We explore several strategies to leverage this useful signal, quantifying performance in a user study. Our best performing model reduces mean tracking error by ~18% compared to a baseline appearance-based model. A supplemental study reveals an additional 10-20% improvement if the gaze-tracking camera is located at the bottom of the device.

Deceptive/Manipulative Patterns (DMP) are interface designs, also known as "dark patterns," that manipulate user behavior. While considerable attention has been paid to their ethical and legal implications, empirical evidence about their real-world effects remains diffuse. This review synthesizes up-to-date experimental studies, focusing on works that quantify how (or whether) DMPs influence users. We also aggregate findings on interventions aimed at reducing DMP effects. Our synthesis highlights the experimental agreement that DMPs do significantly alter user behavior (with large variance in effect size) and that external interventions have been mostly unsuccessful in mitigating their effects. Lastly, we show that significant correlations between DMP effects and personal characteristics (e.g., age or political affiliation) are uncommon, indicating DMPs similarly affected nearly all populations tested. By summarizing the experimental evidence, we clarify the effects of DMPs, highlight gaps and tensions in the existing experimental literature, and help inform ongoing research and policy directions.

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.

As Artificial Intelligence (AI) conversational agents become widespread, people are increasingly using them for health information seeking. The use of off-the-shelf conversational agents for health information seeking could place high metacognitive demands (the need for extensive monitoring and control of one's own thought process) on individuals, which could compromise their experience of seeking health information. However, currently, the specific demands that arise while using conversational agents for health information seeking, and the strategies people use to cope with those demands, remain unknown. To address these gaps, we conducted a think-aloud study with 15 participants as they sought health information using our off-the-shelf AI conversational agent. We identified the metacognitive demands such systems impose, the strategies people adopt in response, and propose considerations for designing beyond off-the-shelf interfaces to reduce these demands and support better user experiences and affordances in health information seeking.

Exergames promise enjoyable physical activity through gameplay, yet players often face barriers that undermine engagement, safety, and retention. To date, knowledge about which barriers are encountered by end-users of commercial exergames and which mitigation strategies are used is limited. To address this gap, we conducted an online survey with 174 participants and provide a comprehensive organization of 60 reported barriers across six categories: physical, mental, social, environmental, technological, and game design. Key barriers include space limitations, social discomfort, addictive gameplay, and injuries. Our analysis reveals that while players try to mitigate barriers through ad-hoc strategies, issues like embarrassment, addiction, and harassment remain difficult to overcome. These findings highlight the need for more adaptive game designs, including dynamic spatial adjustments, personalized pacing mechanisms, and supportive social features. This work advances the understanding of exergame barriers and their impact and offers actionable insights for designing more inclusive and resilient movement-based games.

The absence of physical information during hand-object interaction in a virtual environment diminishes realism and immersion. Kinesthetic haptic feedback has proven effective in delivering realistic object-derived haptic cues, enhancing the overall virtual reality (VR) experience. Here, we propose kinesthetic illusion through a novel application of finger tendon vibration (FTV), which creates an illusory sensation of finger movement. To effectively apply FTV for virtual object interactions, we first examine the effects of short-duration FTV (<5 s) through 3 perception studies. Based on study results, we design 6 exemplary VR scenarios, representing the overall design space of VR object interactions, and 4 different haptic rendering strategies for FTV. We evaluated these rendering methods on each VR scenario and derived a design guideline for FTV application. We then compared FTV with no vibration and simple vibration, observing that FTV enhances VR experience by providing realistic resistance on the finger, greatly improving body ownership.

Ring interfaces have gained attention in wearable technology for their lightweight and hands-free design. However, their compact form factor limits them to conveying simple information, such as direction or notification, through vibration, electrotactile, or force feedback. In this paper, we introduce HaRing, a novel haptic ring interface equipped with a 4 × 6 pin array display. This dynamic display delivers rich spatial patterns that simple vibration cannot express, effectively conveying high-dimensional information such as directions, semantic symbols, and letters. Its design enables one-handed, eyes-free interaction that does not interfere with visual tasks. We conducted a series of perceptual and user studies to demonstrate its effectiveness, showing a high recognition accuracy of over 94% for complex letters after a brief training period. We anticipate that HaRing can serve as an innovative haptic-only interface for multitasking in real-world or VR environments with high visual load.

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.

The way users hold a smartphone depends on the interaction task, yet little is known about the fingers' engagement with the device's surfaces beyond the touchscreen. Such an understanding not only opens up opportunities for novel on- and off-screen interactions, but also the device’s possible physical affordances. We present a study (N=23) that examines the hands' physical engagement with the smartphone beyond the touchscreen across nine mobile interactions. Grasps were annotated from photographs, and contact regions were captured using residual heat traces from grasping the device. Our findings show that fingers and palms adopt a variety of support roles and postures when engaging with the smartphone's back and side edges. The hand-contact maps reveal distinct patterns, differing in contact frequency and placement. This work contributes an empirical characterisation of hands' back and edge engagement, highlighting design opportunities for future smartphone usage extending beyond the touchscreen.

Rhythm and articulation are essential for expressive guitar performance. Existing tools provide basic beat cues, whereas beginners often struggle to align with these cues when playing complex techniques, such as strumming and muting. Informed by a formative study with five instructors and grounded in embodied learning theories, we present FretFlow, a haptic vest-based tool that simulates common instructional practices to guide learners through physical interactions like tapping. The key to FretFlow is its design space that maps rhythmic and articulation patterns in various playing techniques to distinct haptic patterns, enabling authoring of haptic scores. FretFlow further dynamically adapts haptic intensity based on learners' real-time performance accuracy, accompanied by multimodal guidance across haptic, visual, and audio channels. We iteratively refined haptic designs across two rounds with 46 participants, followed by a two-week user study with 20 beginners. Results show that FretFlow improves learners’ rhythmic accuracy and expressive performance.

Virtual Reality (VR) enables users to engage with capabilities beyond human limitations, but it is not always obvious how to trigger these capabilities. Taking the lens of Affordance, we believe avatar design is the key to solving this issue, which ideally should communicate its capabilities and how to activate them. To understand the current practice, we selected eight capabilities across four categories and invited twelve professional designers to design avatars that communicate the capabilities and their corresponding interactions. From the resulting designs, we formed 16 guidelines to provide general and category-specific recommendations. Then, we validated these guidelines by letting two groups of twelve participants design avatars with and without guidelines. Participants rated the guidelines’ clarity and usefulness highly. External judges confirmed that avatars designed with the guidelines were more intuitive in conveying the capabilities and interaction methods. Finally, we demonstrated the applicability of the guidelines in avatar design for four VR applications.

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.

\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.

As LLM-based Conversational Avatars increasingly act as collaborators in hybrid indoor navigation, understanding how their personality traits influence human-avatar proxemic behavior is becoming crucial. Prior work has largely examined personality effects in static or one-sided interactions such as sitting, standing, or approaching. However, there is a gap in research on how avatar personality and motion-related factors (e.g., walking speed) shape proxemics when both the human and avatar are in motion. To address this, we developed an AR indoor navigation system featuring a Conversational Virtual Avatar (CVA) with three distinct personalities: Dominant, Warm, and Conscientious. The CVA guides users to destinations within the environment. In a between-subjects study ($N$=27), we found statistically significant effects of avatar personality and walking speed on proxemic behavior. Our work contributes to a broader understanding of the role of personality and walking speed of a CVA on human-avatar proxemic behaviour during navigation.

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.

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.

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

Zooming and panning are fundamental input actions for exploring complex 2D and 3D scenes and data such as images, maps, and designs. Multi-touch zoom/pan interactions have been proven effective on mobile devices, and have been directly ported to HMDs, where they are typically accomplished by analogous but relatively large-scale movements of both hands. We argue that such motions are inefficient and induce fatigue and explore how the eye-tracking features of HMDs can be leveraged to achieve improvements. We evaluated three interaction techniques that combine gaze with two-handed, one-handed, and head-based input in a study (N=24) that contrasts them against a baseline two-handed technique. The results indicate that gaze-assisted two- and one-handed techniques outperform the baseline (17%-36% faster), while our head-based technique achieves similar performance to the Baseline but leaves the hands free for other tasks. We further developed a VR application demonstrating these techniques and validating their practical applicability.

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.

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.

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.

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.

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.

Toxic behavior is a problem in online gaming platforms such as League of Legends (LoL), undermining player well-being and fairness. Platforms increasingly optimize “engagement” without distinguishing between positive and negative participation. Drawing on dual-process theory, we ask when hostile interactions can become economically productive. In an explanatory sequential mixed-methods study with LoL players, Study 1 (N = 430) models how reflective, System~2 brand bonds (i.e., brand personality, brand involvement, brand engagement) and negatively valenced, System~1 reactive responses (self-reported toxic behavior) relate to in-game spending. Study 2 (N = 80) uses reflexive thematic analysis to show how players interpret, repair, and channel frustration and hostility through cosmetics, events, and progression systems. Across studies, toxic behavior is positively associated with self-reported purchases and partially transmits the association between reflective brand attachments and spending. We contribute a dual-pathways account of how governance and monetization infrastructures can fold harmful engagement into value extraction, and we outline critical design provocations for centrally governed, highly monetized platforms.

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.

It has been suggested that the functional significance learners ascribe to virtual rewards in gamified systems can have a significant impact on intrinsic motivation. Yet, to date, there has been a lack of research examining this relationship empirically. In the present study, we therefore applied Cognitive Evaluation Theory to examine how learners’ (n = 162) interpretation of virtual rewards in Duolingo affected autonomy satisfaction, competence satisfaction, and intrinsic motivation. Based on a structural equation modeling approach, our findings suggests that the informational significance learners ascribe to virtual rewards positively affects competence satisfaction, and that autonomy and competence satisfaction together affect intrinsic motivation. For the HCI field, the results point towards the importance of moving beyond treating virtual rewards in gamified learning as a use-or-avoid decision, and instead consider how such design elements can be modified to provide increased encouragement and positive feedback.

As eye-tracking becomes increasingly common in modern mobile devices, the potential for hands-free, gaze-based interaction grows, but current gesture sets are largely expert-designed and often misaligned with how users naturally move their eyes. To address this gap, we introduce a two-phase methodology for developing intuitive gaze gestures. First, four co-design workshops with 20 non-expert participants generated 102 initial concepts. Next, four gaze interaction experts reviewed and refined these into a set of 32 gestures. We found that non-experts, after a brief introduction, intuitively anchor gestures in familiar metaphors and develop a compositional grammar; i.e., activation (dwell) + action (gaze gesture or blink), to ensure intentionality and mitigate the classic Midas Touch problem. Experts prioritized gestures that are ergonomically sound, aligned with natural saccades, and reliably distinguishable. The resulting user-grounded, expert-validated gesture set, along with actionable design principles, provides a foundation for developing intuitive, hands-free interfaces for gaze-enabled devices.

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.

While many modern gaming environments provide haptic feedback, laptop keyboard gaming remains largely without rich tactile interaction, despite a rapidly growing audience. In this paper, we propose the HapPalm interface, a novel laptop interface concept that delivers rich spatio-temporal vibrotactile feedback through the palmrest area, allowing players to feel game events with their palms. Our prototype uses dual 4×6 linear resonant actuator arrays. To render various game events with the HapPalm interface, our first study aims to create a haptic pattern dataset. Iterative design workshops identified 11 haptic pattern templates, of which our second study validated that they convincingly convey diverse game events. Our final study embedded these patterns into a custom game, showing that spatial haptics significantly improved fun, immersion, realism, and presence compared to non-spatial or no-haptic conditions. HapPalm interface demonstrates that palmrest-based haptics can enrich keyboard-only laptop gaming, providing an expressive and immersive tactile channel for future laptop interfaces.

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.

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.

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.

Virtual Reality (VR) depends on haptic feedback to create immersive experiences. Traditional passive proxies align physical props with their virtual counterparts but remain limited in scalability and expressiveness, or require bulky actuators to support reconfiguration. We introduce User-reconfigured Haptics, an approach that utilizes implicit user actions to reconfigure haptic interfaces to extend the gamut of VR haptic experiences. Modular 3D-printed cells are assembled into dynamic interfaces that express diverse haptic properties such as softness and weight. By masking physical reconfigurations with visual (re)mapping, user actions unnoticeably change haptic properties, resulting in user-driven, dynamic haptic experiences. User studies show that our design can provide distinguishable haptic experiences and is perceived as realistic and enjoyable in a VR task. We further showcase four applications: a fishing rod that changes weight and flexibility, a dynamic desktop of pressable buttons, a glove with adjustable squeezing, and a crossbow with variable pulling resistance.

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.

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.

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.

Foldable phones naturally support displaying two applications side by side on adjacent screens. However, one-handed interaction with this dual-screen configuration faces two challenges: first, users must change grip to reach each screen; second, menus displayed on each screen take valuable screen real estate and can be difficult to reach. To address this, we investigated how the hinge of foldable phones can enrich tactile interaction and provide access to hinge menus, unifying interaction across both screens. In a first experiment, we examined how users hold a foldable phone at different fold angles and identified the thumb-accessible screen areas, validating that a hinge-based grip enhances reachability. In two subsequent experiments, we evaluated the feasibility and performance of hinge gestures, defined as touch inputs (tap, tap-tap, or swipe) executed fully or partially on the hinge. Building on these findings, we designed hinge menus that combine hinge gestures for menu activation and item selection. Our final experiment identifies different hinge menus that outperform linear menus on adjacent screens. Our findings provide practical guidelines that can immediately inform and improve interaction on current foldable phones.

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.