注目の論文一覧

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.

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.

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.

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.

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.

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.

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.

Urbanization has limited people’s daily exposure to nature, reducing nature connection and overall well-being. Although virtual reality (VR) offers an alternative access to nature-like environments, it often lacks authenticity and connection to physical experiences. We present NatureCapture, a multi-user system that bridges real-world and virtual nature engagement by allowing users to capture real-world natural elements and collaboratively integrate them as interactive 3D models into a shared VR nature space. A two-week within-subjects study indicated that NatureCapture promoted nature connectedness, emotional regulation, and social closeness. Interviews further revealed that NatureCapture supported immersive and extended interactions with nature, promoted reciprocal engagement in virtual and real environments, and strengthened nature interaction through socially mediated engagement. Our work highlights the potential of user-driven real-to-3D transformation to preserve physical encounters with nature, combining the spatial flexibility and interactivity of VR. Such cross-environment paradigm offers a novel approach for sustaining human-nature connection in technologically mediated life.

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

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.

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.

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.

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.

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.

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.

Most shelving relies on rectangular compartments that ignore the contours of the objects they hold. We present Uniquely Shaped Spaces, an object-driven algorithmic tool for custom shelving generation. The workflow arranges users' object silhouettes with simulated annealing, grows walls via cellular automata to carve fitted voids, and outputs fabrication files with joinery for laser cutting. We designed the system so that objects, our algorithm, and users share authorship, and studied how this configuration played out with five participants as they designed shelves in guided workshops and then lived with the fabricated pieces. Our findings show how participants navigated object geometry, algorithmic search, and fabrication limits by curating, tweaking, and appropriating algorithmic proposals, and how the resulting shelves supported reflection and storytelling. These results point toward object-driven fabrication systems that foreground objects as generative constraints and explicitly support negotiation within constraint-driven workflows.

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.

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.

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.

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.

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.

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.

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.

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.

Extended Reality (XR) systems for physical skill training have largely emphasized simulation rather than real-time in-situ instruction. We present WeldAR, an Augmented Reality (AR) system with five learning modules that overlays real-time guidance during live welding using a headset integrated into a welding helmet and a torch attachment. We conducted an in-situ within-subjects study with 24 novices, comparing AR guidance to video instruction for live welding across practice and unassisted tests. AR improved performance in both assisted practice and unassisted tests, primarily driven by gains in travel speed and work angle. By offering real-time feedback on four performance measures, AR supported novices in carrying embodied knowledge into independent tasks. Our contributions include: (1) WeldAR for in-situ physical skill training; (2) empirical evidence that AR enhances composite welding performance and key physical skills; and (3) implications for the development of AR systems that support in-situ, embodied skill training in welding and related trades.

This paper investigates associations, explicit representations of relations between multiple views in Mixed Reality (MR). While research on 2D desktop environments offers extensive recommendations for communicating relations between multiple views, MR environments lack such systematic guidance, necessitating adapted solutions that consider their spatial affordances. To address this gap, we systematically explored association techniques in existing research. Building on established 2D multi-view literature and refining insights from prior design principles, we developed a codebook to describe view relations and their representations. Applying it to a corpus of 44 immersive multi-view approaches, we identified recurring design strategies and synthesized them into a design space of visual association techniques adapted for immersive contexts. Based on a lightweight prototyping framework, we validate the utility of the design space through three envisioning scenarios, demonstrating how associations can support exploration, coordination, and sensemaking in MR applications. Our results inform the design of MR multi-view environments.

Generative AI's function as a frictionless "answer engine" creates a paradox in educational HCI: the very tools that can enhance intellect may also weaken it by allowing users to circumvent crucial cognitive processes. This risks creating a "hollowed mind"---knowledge that is broad but superficial, and a user experience that diminishes learner agency. The convenience of cognitive offloading introduces a motivational challenge that traditional cognitive scaffolding cannot address. We argue that designing genuine human-AI partnerships in learning requires moving beyond cognitive support to motivation-aware scaffolding. This paper provides a toolkit for building motivation-aware AI systems. At its core is the Dual Zone of Proximal Development (DZPD), a conceptual framework building on foundational work in educational psychology. We introduce an overarching design principle, concrete design principles, illustrative archetypes, and examples of measurable indicators. These conceptual tools offer essential guidance for the next wave of empirical HCI research in education.

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.

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.

Perceiving material properties such as elasticity, flexibility, and torsion is inherently bimanual, as we rely on the relative motion of our hands to form a unified sense of materiality. Yet, most vibrotactile material rendering approaches are limited to a single hand or finger. While prior work has explored bimanual haptic interfaces, most depend on specialized hardware for specific interactions. In this paper, we demonstrate design strategies to support bimanual material exploration through motion-coupled vibrotactile feedback. Our technique introduces variable crosstalk between the controllers' vibration to evoke connectedness, making two unconnected devices feel as though they manipulate a single object. The technique generalizes motion-coupled feedback approaches beyond previous single-point explorations. Through two user studies, we show that this approach (1) significantly enhances perceived connectedness and (2) conveys distinct material qualities such as elasticity and torsion. Finally, we present \textit{Dvihastīya}, an authoring tool for designing connected bimanual experiences in virtual reality.

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.

This study explores how nonverbal, sensory cues—olfactory and wind—can serve as subtle channels for behavioral guidance in mobile human-robot interaction. As multimodal interaction becomes increasingly integral to HRI, implicit communication remains underexplored, particularly through non-visual and non-auditory modalities. To address this gap, we conducted a Wizard-of-Oz study with 35 participants who experienced three types of stimuli—strong wind, weak wind, and olfactory cues—across six contextual scenarios. Our findings show that such sensory cues can induce affective interpretations ranging from support to surveillance, depending on the context. Olfactory cues generally evoked more positive impressions and a greater sense of care than wind, while wind cues were perceived as more directive and intrusive in comparison. These results suggest that scent and wind offer promising potential as ambient, affective, and non-intrusive notification channels for future human-robot interaction systems.

Co-located collaboration often raises challenges related to physical constraints because of , e.g., stationary display setups or limited possibilities of movement. We introduce DoubleMe, an Augmented Reality system that generates virtual duplicates of collaborators' workspaces, comprising both their displays and avatars. With DoubleMe, users maintain the layout of their own physical workspace and can position the duplicate of their collaborator's workspace nearby. This approach alleviates spatial constraints by enabling one user to join another's workspace without leaving their own. We report on two experiments examining the effectiveness of this approach. The first experiment investigates how avatar appearance and interaction influence user comfort and relationship dynamics. The second experiment assesses the performance benefits of duplicates over traditional co-located setups for collaborative tasks. Our findings suggest that the addition of duplicates to physical presence can enhance co-located collaboration while improving comfort.

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.

Long-range Human-Robot Interaction (HRI) remains underexplored. Within it, Command Source Identification (CSI) – determining who issued a command – is especially challenging due to multi-user and distance-induced sensor ambiguity. We introduce HiSync, an optical-inertial fusion framework that treats hand motion as binding cues by aligning robot-mounted camera optical flow with hand-worn IMU signals. We first elicit a user-defined (N=12) gesture set and collect a multimodal command gesture dataset (N=38) in long-range multi-user HRI scenarios. Next, HiSync extracts frequency-domain hand motion features from both camera and IMU data, and a learned CSINet denoises IMU readings, temporally aligns modalities, and performs distance-aware multi-window fusion to compute cross-modal similarity of subtle, natural gestures, enabling robust CSI. In three-person scenes up to 34m, HiSync achieves 92.32% CSI accuracy, outperforming the prior SOTA by 48.44%. HiSync is also validated on real-robot deployment. By making CSI reliable and natural, HiSync provides a practical primitive and design guidance for public-space HRI.

Data visualization benefits from non-visual cues to enable people to understand information by engaging with it through multimodality, yet most approaches rely on cumbersome technologies or large scale artifacts, making them difficult to adapt to dynamic or complex datasets. In this paper, we explore the use of cutaneous haptics as a lightweight quantitative channel for visualization tasks, allowing users to feel data and interact with it dynamically. We present Haptixel, an open-source DIY encountered-type wearable providing force-feedback on the users' fingertips' pulp. We propose an interaction framework illustrating how Haptixel can be used to complement visualization tasks through combinations of force levels and contact types. We evaluate our approach in a pixel-art-like VR user study (n=16) where pixels color/height are associated to forces as a univariate value mapping. Results show that participants can retrieve information with Haptixel, and significantly discriminate 3D-data with at least four levels of forces; suggesting that cutaneous force-feedback can function for quantitative distinctions in visualization tasks.

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.

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.

Multimodal interaction has long promised to make interfaces more intuitive and effective by combining complementary inputs. Among these, gaze and speech form a compelling pairing: gaze provides rapid spatial grounding, while speech conveys rich semantic information. Together, they offer rich cues for understanding user behaviour and intent. Yet despite decades of exploration, the research remains fragmented, making this synthesis timely as these inputs mature and are integrated into consumer-ready devices. This scoping review examined 103 studies published between 1991 and 2025, organised into \emph{explicit}, where users intentionally provide gaze and speech, and \emph{implicit}, where systems leverage users' natural behaviours to support interaction. Across both, we identified recurring ways for combining gaze and speech to resolve ambiguity, ground references, and support adaptivity. We contribute a synthesis of research on their combined use while highlighting challenges of temporal alignment, fusion and privacy, offering guidance for future research toward richer multimodal human-computer interaction.

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.

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.

While autonomous vehicles (AVs) continue to advance and reshape modern transportation, they remain unable to navigate all traffic conditions without human input, highlighting the need for remote human intervention in edge-case scenarios. Two major teleoperation paradigms have emerged to address this need: tele-driving and tele-assistance. In tele-driving, remote operators (ROs) continuously control the AV through direct access to its actuators. In tele-assistance, ROs provide high-level instructions through a specialized interface, with low-level maneuvers delegated to the AV. We conducted a quantitative comparison of these paradigms, examining four edge-case scenarios: one uses a steering wheel and pedals, the other employs discrete high-level commands through a Wizard-of-Oz methodology. We measured mental workload, situation awareness, time completion, and overall user experience (UX). Results indicate that the tele-assistance interface reduced operators’ mental workload and improved situation awareness, suggesting the need for further development of tele-assistance interfaces.

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.

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.

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.

AI-generated future selves have been explored for self-reflection and counseling, yet most remain confined to chat, voice, or diary formats. We introduce RoF (Reminiscences of Futures), a system that enables users to encounter and converse with embodied future selves. It synthesizes voice and appearance from personal data and generates three projections of the user ten years ahead, situated within a tunnel-like installation that evokes a time-slip experience. We report the design process, stepwise judgements, and a formative study with six parent–child pairs that informed design strategies. We also present a week-long user study with fifteen participants. Participants perceived RoF as an emotionally rich experience, reporting shifts in their attitudes toward the future and deeper reflection on their present identity and life. We discuss the implications of interacting with future selves for fostering reflection and preparing positively for the future, as well as ethical issues, such as self-replication using AI.

This paper presents LLooM, a probe designed to capture situated, temporal, and contradictory experiences with language technologies such as voice assistants, chatbots, and LLMs. The design of LLooM draws on work in probes, feminist HCI, and storytelling to invite participants to write stories about their encounters with language technologies on fabric strips and weave them into looms. Through a public, researcher-facilitated, and collective participant deployment with 56 participants, LLooM enabled participants to share diverse perspectives on language technologies. This methodological approach makes two contributions to probe design in HCI: enabling participants to reshape the methodological assumptions underlying research and allowing participants' visible contributions to become provocations that support collaborative meaning-making across diverse experiences.

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.

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.

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.