注目の論文一覧

Responsiveness in large language model (LLM) applications is widely assumed to be critical, yet the impact of latency on user behavior and perception of output quality has not been systematically explored. We report a controlled experiment varying time-to-first-token latency (2, 9, 20 seconds) across two taxonomy-driven knowledge task types (Creation and Advice). Log analyses reveal that user interaction behaviors were robust to latency, yet varied by task type: Creation tasks elicited more frequent prompting than Advice tasks. In contrast, participants who experienced 2-second latencies rated the LLM’s outputs less thoughtful and useful than those who experienced 9- or 20-second latencies. Participants attributed delays to AI deliberation, though long waits occasionally shifted this interpretation toward frustration or concerns about reliability. Overall, this work demonstrates that latency is not simply a cost to reduce but a tunable design variable with ethical implications. We offer design strategies for enhancing human-LLM interaction.

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.

Despite their ubiquity, wireless earbuds remain audio-centric due to size and power constraints. We present VueBuds, the first camera-integrated wireless earbuds for egocentric vision, capable of operating within stringent power and form-factor limits. Each VueBud embeds a camera into a Sony WF-1000XM3 to stream visual data over Bluetooth to a host device for on-device vision language model (VLM) processing. We show analytically and empirically that while each camera's field of view is partially occluded by the face, the combined binocular perspective provides comprehensive forward coverage. By integrating VueBuds with VLMs, we build an end-to-end system for real-time scene understanding, translation, visual reasoning, and text reading; all from low-resolution monochrome cameras drawing under 5mW through on-demand activation. Through online and in-person user studies with 90 participants, we compare VueBuds against smart glasses across 17 visual question-answering tasks, and show that our system achieves response quality on par with Ray-Ban Meta. Our work establishes low-power camera-equipped earbuds as a compelling platform for visual intelligence, bringing rapidly advancing VLM capabilities to one of the most ubiquitous wearable form factors.

Mobile phone numbers function as single keys to banking, government, and commerce, making the Subscriber Identity Module (SIM) a critical element of security. In April 2025, South Korea’s largest carrier experienced a SIM breach that compromised authentication keys and exposed nearly 27 million subscriber identifiers. We conducted semi-structured interviews with mental-model elicitation (N=33) to examine user awareness, responses, and understanding of SIM-based authentication. Results reveal a pronounced awareness–action gap: participants recognized the breach yet held incomplete mental models, perceived little personal risk, and rarely acted protectively, even when affected. Learned helplessness, reliance on carriers, and the invisibility of SIM shaped these passive responses. Brief educational interventions improved conceptual understanding but seldom produced lasting behavioral change. Our findings demonstrate how technical opacity and psychological factors jointly inhibit protective action and offer design implications for usable security, emphasizing interventions that realign users’ mental models with system risks to foster sustainable practices.

Clinician skepticism toward opaque AI hinders adoption in high-stakes healthcare. We present AICare, an interactive and interpretable AI copilot for collaborative clinical decision-making. By analyzing longitudinal electronic health records, AICare grounds dynamic risk predictions in scrutable visualizations and LLM-driven diagnostic recommendations. Through a within-subjects counterbalanced study with 16 clinicians across nephrology and obstetrics, we comprehensively evaluated AICare using objective measures (task completion time and error rate), subjective assessments (NASA-TLX, SUS, and confidence ratings), and semi-structured interviews. Our findings indicate AICare's reduced cognitive workload. Beyond performance metrics, qualitative analysis reveals that trust is actively constructed through verification, with interaction strategies diverging by expertise: junior clinicians used the system as cognitive scaffolding to structure their analysis, while experts engaged in adversarial verification to challenge the AI's logic. This work offers design implications for creating AI systems that function as transparent partners, accommodating diverse reasoning styles to augment rather than replace clinical judgment.

In interpersonal interactions, individuals often exhibit avoidance behaviors toward others they find unpleasant, which can undermine the comfort of everyday social experiences. Existing human-computer interaction (HCI) research has primarily focused on promoting social connections, while support for avoidance-oriented social situations remains underexplored. To address this gap, we propose leveraging Diminished Reality (DR) technology to obscure perceptual cues of undesirable individuals. We designed and implemented a mixed reality prototype system and conducted experiments manipulating both the occlusion method and social distance. Results indicate that DR significantly reduces users' social anxiety and sense of social presence. Moreover, participants generally expressed positive attitudes toward usage intention and ethical considerations. This work extends HCI research on social comfort, shifting the focus from "facilitating connection" to "supporting avoidance".

Systems' latency — the time between user input and system response — slows down the human-computer interaction loop. Several studies revealed negative objective and subjective effects of high latency, typically treating latency as a constant delay. Because latency varies significantly in practice, recent work also assessed the effects of large and sudden latency changes. In practice, however, latency variations are small but frequent. As the effects of such variations are unclear, we investigate how small latency variations (+/- 50 ms) affect users' performance and perceived task load for 2D target selection tasks with static and moving targets. For static targets, we found that latency variation causes significantly higher completion times and less efficient trajectories, however with small effect sizes. In contrast, we found no significant effects on any performance measure for moving targets. Our findings indicate that the effect of latency variation is generally very small and quickly disappears for non-trivial tasks.

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.

End-to-end verifiable Internet voting promises that voters can remotely check whether their ballot was recorded correctly and that all ballots were tallied as cast. However, in order to achieve an adequate level of security, voters actually need to perform the first check. Our research focuses on the cast-then-audit approach for this check. We use related work to improve this approach in particular by providing a step-by-step guide. We conducted a deceptive online user study (N=437) to compare our improved system with a baseline version from an actual election. We also measured the usability and participants confidence in using such systems. Our findings show that participants from the improved system perform significantly better than the baseline w.r.t. manipulation detecting and reporting capabilities. Furthermore, we show that it is important to distinguish between detection and reporting to understand how to further increase the overall security.

Privacy Policies are a cornerstone of informed consent, yet a persistent gap exists between their legal intent and practical efficacy. Despite decades of research proposing various visualizations, user comprehension remains low, and designs rarely see widespread adoption. To understand this landscape and chart a path forward, we synthesized 65 top-tier papers using a framework adapted from user-centered design lifecycles. Our analysis presented four findings of the field's evolution: (1) trade-off between information load and decision efficacy, which shows a shift from augmenting disclosures to cognitive load management, (2) co-evolutionary dynamic of design and automation, revealing that designs such as context-awareness drove automation needs, while LLM breakthroughs enable the semantic interpretation required to realize those designs, (3) tension between generality and specificity, highlighting the divergence between standardized solutions and the increasing necessity for specialized interaction in IoT and immersive environments, and (4) balancing stakeholder opinions, where visualization efficacy is constrained by the interplay of regulatory mandates, developer capabilities and provider incentives.

Urban cycling benefits personal wellbeing, public health, and global sustainability. While current tools such as Google and Apple Maps provide bike route recommendations, they do not account for a person’s dynamic context (e.g., commuting, recreation). We introduce BikeButler, a personalized, context-sensitive bicycle route generation tool that enables users to generate, compare, virtually preview, and iteratively customize bike routes via custom profiles that encode seven bikeability features, including bike lane existence, slope, vegetation, and surface quality—fusing data from OpenStreetMap, open government data, and a custom VLM-based analysis of Street View images. To design BikeButler, we employed a human-centered, iterative approach starting with formative interviews and culminating in a user study (N=16). Our findings demonstrate that bike routing preferences change as a function of context, that BikeButler enables users to quickly create and iterate context-sensitive routes, and that generated routes differ significantly from Google Maps bike routing, reinforcing the importance of personalization.

Configuring security and privacy (S&P) settings can be challenging for non-expert users, resulting in excessive dependence on persuasive cues, such as social proofs or expert suggestions. Although such suggestions can promote protective user choices, they can be misused as deceptive patterns that steer users toward less-protective settings. This study examines (1) how source-based suggestions (public vs. experts), when combined with logical persuasive statements, influence decision-making in S&P settings under honest or deceptive conditions and (2) how users evaluate these approaches once deception is revealed. An online experiment with 1,433 U.S. participants utilizing a 2×2×2 factorial design revealed that persuasive statements amplified the effect of social proof- and authority-based cues, which persisted even when promoting less-protective settings. These findings demonstrate the importance of persuasive S&P interfaces that follow transparent and rational design, as well as complementary interventions that foster users' critical assessment and resilience against manipulation.

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.

We present SensoryBlox, a modular, multi-sensory user interface designed for integration with cardboard-based virtual reality (VR) head-mounted displays (HMDs). SensoryBlox features interchangeable sensory modules—vibration, temperature, wind, and olfactory—that enable users to assemble customized multi-sensory configurations tailored to diverse VR contexts. The system includes in-VR interfaces for module scanning, spatial tracking, and real-time customization of feedback patterns. To inform SensoryBlox design, we conducted three user studies. The initial study explored application scenarios and associated sensory modalities to identify design requirements for a modular multi-sensory VR system. Based on these findings, we developed the hardware modules and in-VR software interfaces. In the second study, we evaluated the usability and interaction experience of SensoryBlox across all functionalities. Finally, a comparison study examined the impact of multi-sensory feedback on user experience. Our findings demonstrate the potential of a modular multi-sensory system in enriching immersion and engaging interactions within low-cost VR environments.

Hands are the chief appendage with which we manipulate the world around us, creating sounds as they go. As such, they are a rich source of information that computers can leverage for input and context sensing. Indeed, many prior works in HCI have explored this idea by instrumenting users' hands with a microphone, often integrated into a ring, wristband, or watch. In this work, we explore an alternative bare-hands approach --- by using a microphone array integrated into a user's headset/glasses, we can use beamforming to create a virtual microphone that tracks with the user's fingers in 3D space. We show this method can capture even the subtle noise of a finger translating across surfaces, including skin-to-skin contact for micro-gestures, as well as passive widget interactions.

Haptic perception on touchscreens varies across fingers, yet little is known about how finger identity and multi-finger use shape tactile discrimination and user experience. We conducted two experiments with four haptic feedback. In Experiment 1, right-handed participants explored each of the ten fingers individually under stationary and moving conditions. Experiment 2 examined two-finger sequences with same participants. Results showed that moving exploration enhanced accuracy, confidence, and enjoyment, while stationary touch increased cognitive and physical load, especially for weaker fingers such as the left ring and pinky. The right thumb and index consistently performed best. In dual-finger trials, moving exploration improved second-finger performance, and adjacent same-hand pairs (e.g., Left Index–Left Thumb, Right Thumb–Right Index) yielded higher synergy. These findings highlight the role of finger anatomy, motion, and coordination, and provide concrete guidelines on which fingers (or combinations) and exploration modes to assign for haptic surfaces that optimize accuracy, comfort, and engagement.

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.

Augmented reality (AR) games hold promise for rehabilitation, yet most remain confined to laboratory studies with limited clinical uptake. Recent advances in spatial computing, especially lightweight, glasses-form-factor AR, create a timely opportunity to embed rehabilitative play into clinical practice and daily contexts. To investigate this potential, we systematically reviewed 132 applications and conducted playtesting with 14 licensed physical therapists. Our analysis revealed three ways therapists re-authored AR games: co-authored play (reshaping movements, progressions, and difficulty), situated play (adapting across specialties, conditions, and contexts), and dual play (mediating both physical recovery and psychological support). We reframe therapists’ frequent phrase—“It depends”—as a generative design principle. This study contributes a clinical reasoning–based framework and design principles and guidelines for creating personalized, situated forms of play that align with therapists’ everyday workflows and inform future lab-to-clinic translation.

Exergames combine motivating game elements with bodily movement to encourage physical activity. However, onboarding players to perform correct movements remains a challenge, especially in virtual reality (VR) environments where safety and performance are critical. Drawing inspiration from sports training and learning sciences, we contrast two onboarding approaches: (i) trial-and-error and (ii) observational learning via a novel self-model tutorial. In this tutorial, players temporarily lose agency and observe their own avatar performing the movements, leveraging VR’s unique affordances for embodied experiences. To explore which of these two approaches yields a better performance and player experience, we conducted a between-participants study (N=60), comparing them against a baseline condition without a tutorial. Our findings show that the self-model tutorial not only improves players' performance but also increases the perceived ease of control and progress feedback. We discuss tradeoffs and implications for the design of future onboarding experiences in VR exergames.

Parenting while serving as a frontline leader is uniquely stressful, yet little is known about how family responsibilities shape physiological stress in these roles. We followed emergency physicians and tactical police leaders, comparing parents of young children with non-parents across four days: one critical mission day, two standard workdays, and one non-workday. Using wearable sensing, expert activity labeling, and daily debriefs, we inferred stress only in sedentary epochs via a normalized-heart-rate method, with an HRV-based index as benchmark. Parents showed higher stress on workdays and non-workdays, but not on critical mission days, where attentional narrowing and strict device policies appear to suppress parenting-related differences. We contribute: (i) in-the-wild physiological evidence that parenthood amplifies stress mainly under permeable boundaries, (ii) a pragmatic stress-labeling pipeline for safety-critical settings, (iii) a configuration-based account linking boundaries, attention, and parenting, and (iv) design implications for stress-aware boundary management systems, supported by an open analysis repository.

Everyday object-based interactions (EOIs) and mid-air gesture interactions (MAIs) have been widely explored, yet prior work on their integration often targets narrow use cases or specific technologies, leaving designers and developers with limited guidance that generalizes across diverse EOIs and MAIs. We introduce Objestures (“Obj” + “Gestures”)—five interaction types spanning EOIs and MAIs, forming a design space for expressive uni- and bimanual interaction. To evaluate the usefulness of Objestures, we conducted an exploratory user study (N=12) on basic 3D tasks (rotation and scaling), which showed performance comparable to the headset's native freehand manipulation. To understand the user experience, we conducted case studies with the same participants across three applications (Sound, Draw, and Shadow), where participants found the interactions intuitive, engaging, and expressive, and indicated interest in everyday use. We further demonstrate the potential of Objestures across diverse contexts through 30 examples, and discuss limitations and implications.

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.

Data powering AI is often opaque. Researchers, NGOs, and law and policy leaders have called for greater transparency about how data is used for training, fine-tuning, and evaluation. While data transparency is often championed as crucial, what it concretely enables is largely implicit. Similarly, the concerns developers seem to have about transparency go unstated. This lack of clarity has led some researchers to critique transparency demands as disconnected from the actual benefits—or risks—to specific stakeholders. We analyze documentation from four stakeholder groups to create a taxonomy of the risks and benefits of dataset transparency. Data transparency is perceived as either a risk or a benefit given a stakeholder's position, rather than wholesale. We also propose data availability and data documentation as two lenses through which to consider transparency. We discuss how best to strategically promote situational data transparency that takes into account the relationship between stakeholder position, transparency modality, and benefits/risks.

Nudges are subtle interventions designed to influence user behavior without restricting choice. Responsible gambling messages (RGMs) exemplify such nudges by encouraging safer decision-making in gambling environments. Prior research has examined how pop-up messages influence gambling behavior in experimental settings and has explored the design of effective slogan messages. However, little is known about how different types of RGMs shape users’ real-world gambling behavior and safety. To address this gap, we apply a nudging perspective to examine how RGMs support gambling safety throughout gamblers’ decision-making journey. We conducted semi-structured interviews with 22 gamblers and found that participants were generally aware of RGMs, yet some misunderstood their intended purpose. Participants perceived the safety impact of RGMs as reflected in both attitudinal and behavioral dimensions. We further discuss users’ message reception practices and the effectiveness of RGMs as nudges, and conclude with design implications for promoting gambling safety.

While regulatory frameworks call for the implementation of AI certifications, empirical knowledge about how such certifications affect interactions is still scarce. In this work, we examined how AI certifications affect users' trust and reliance. In addition, we examined whether certifications elevate user expectations and whether unmet expectations subsequently reduce trust. In a 2 (certification vs no certification) x 2 (reliability: high vs low) between-subjects online study, N = 644 participants had to identify bacterial infestation in pictures with the help of an AI. Our results show that, before interacting with the AI, participants trusted the certified system more and showed reduced vigilance. However, these effects disappeared post-interaction, where, instead of the certification, system reliability significantly affected trust and vigilance. Notably, certifications did not raise expectations per se, but instead amplified the impact of system reliability on user trust. Additional exploratory results showed that the certification supported appropriate reliance.

Live commerce platforms frequently employ algorithmic recommendations and time-limited promotions to trigger impulsive purchases, challenging rational consumer decision-making. While existing research has identified manipulative design patterns in live commerce, significant gaps remain in understanding consumer psychological motivations and developing counter-persuasion interventions. We conducted a multi-stage formative study involving surveys (N = 116), interviews (N = 21), and co-design workshops (N = 16) to explore user preferences for rational consumption support systems. Informed by these insights, we designed BuyMate, which provides gentle, real-time rational interventions through product comparison and persuasive speech reframing. A user evaluation (N = 35) demonstrates that the system effectively reduces impulsive purchases, enhances decision autonomy, and promotes sustainable consumption. This work contributes an AI-driven counter-persuasion approach, identifies user-centered principles for adaptive interventions, and offers practical guidance for responsible AI in digital commerce.

While Personal Informatics (PI) systems support behavior change, everyday well-being involves more than achieving individual target behaviors. It is shaped by cultural narratives that give actions meaning. In South Korea, the God-Saeng (God生) phenomenon—encompassing disciplined, collective, and publicly documented self-improvement practices—offers a lens into how well-being is negotiated in daily life. We conducted a 10-day probe (N=24) with bite-sized missions to examine how young adults engaged in God-Saeng. Participants relied on planning practices, accountability infrastructures, and datafication to stabilize themselves, yet these same routines also intensified pressures toward self-monitoring and performance. They navigated tensions between consistency and flexibility, authenticity and visibility, and productivity and broader values such as relationships, and reinterpreted ordinary activities through sociocultural contexts. These insights suggest design opportunities for PI systems that move beyond tracking, toward digital instruments that help users negotiate tensions, make meaning, and reflexively understand how technologies participate in their culturally and existentially situated well-being.

Technology offers new opportunities to support healthier food choices, particularly for individuals in low-income communities who face systemic barriers to obtaining nutritious, affordable groceries. We introduce a novel conceptual model of grocery planning that frames food purchasing as a multi-objective optimization problem that considers cost, nutrition components, and a consumer's personal dietary goals. Guided by Zimmerman’s model of Self-Regulated Learning and prior research on food agency, we designed the Food Information System, a planning tool that provides optimized product recommendations aligned with users’ goals by integrating store inventory, prices, and nutritional data. We evaluated our system in an eight-week within-subjects intervention with 55 participants from a food-insecure community, followed by focus group sessions. While overall Healthy Eating Index scores remained largely stable, participants reported improved nutritional awareness and greater perceived agency in planning and purchasing groceries. We discuss design implications to support food agency by promoting long-term food literacy and by enhancing autonomy in making food choices.

Community engagement processes often shape policies that affect people’s daily lives, yet they frequently struggle to build transparency, understanding, and agency. Civic technologies aim to address this gap by making connections between voices and decisions visible, but rarely evaluate impact on democratic participants. This study examines the effects of varying levels and types of transparency, including personalization, in technology-enabled civic decision-making on perceptions of agency, vertical and horizontal transparency, and community connection. We conducted an experiment with 266 participants who advocated for a local skate park or tennis court, and then received a decision for or against their position under varying transparency conditions. Results show that increased transparency improved perceptions of agency, vertical transparency, and horizontal transparency, but personalization had limited effects. Qualitative reflections highlighted horizontal transparency as particularly valuable for opening perspectives and enhancing participant experience. We discuss key design implications for civic technologies.

Environmental sounds like footsteps, keyboard typing, or dog barking carry rich information and emotional context, making them valuable for designing haptics in user applications. Existing audio-to-vibration methods, however, rely on signal-processing rules tuned for music or games and often fail to generalize across diverse sounds. To address this, we first investigated user perception of four existing audio-to-haptic algorithms, then created a data-driven model for environmental sounds. In Study 1, 34 participants rated vibrations generated by the four algorithms for 1,000 sounds, revealing no consistent algorithm preferences. Using this dataset, we trained Sound2Hap, a CNN-based autoencoder, to generate perceptually meaningful vibrations from diverse sounds with low latency. In Study 2, 15 participants rated its output higher than signal-processing baselines on both audio-vibration match and Haptic Experience Index (HXI), finding it more harmonious with diverse sounds. This work demonstrates a perceptually validated approach to audio-haptic translation, broadening the reach of sound-driven haptics.

For mobile developers to comply with privacy regulations, they must create privacy policies that accurately describe their apps' data practices. This requires a complete understanding of their apps' behaviors, including those of embedded third-party SDKs. Despite the complexity of this process, little is known about how privacy policies are created and validated. To investigate, we interviewed 20 developers from around the world about their processes, also observing them use a large language model (LLM) to prepare privacy policies for their apps. We found that developers struggle with collecting information about third-party SDKs, even when they use LLMs, and feel uncertain about the legal validity of LLM outputs. Many developers do not seek legal assistance and believe that, as long as app stores accept their privacy policies, they are protected. Our findings suggest that reliance on LLMs and developers' desire to externalize validation may result in increasingly unreliable privacy policies.

Algorithmic dating applications mediate romance through an "algorithmic mirror," subjecting users to data-driven classifications that shape their self-perception. However, the specific strategies users employ to interpret and strategically manage this reflection remain underexplored. Understanding this dynamic is critical, as navigating the algorithmic gaze demands significant emotional labor and has profound implications for user agency and well-being. Through semi-structured interviews with 15 OkCupid users, I investigated this process of sense-making. I contribute a novel typology of three knowledge forms, Folk, Personal, and Academic, that users construct to redefine themselves against the algorithm. Theoretically, this paper frames the "algorithmic other" as a statistical counterpart to Mead's "generalized other," revealing a core "dual-audience dilemma" where users perform for both humans and machines. These findings inform the design of more transparent and contestable systems that better support user agency.

Current co-op games keep collaboration virtual even when players are physically co-located in the same room, limiting embodied coordination in the shared space. We introduce MoveTogether, a novel physical co-op gameplay in which two players jointly operate a single, tracked prop, adding a shared physical communication channel on top of visual and audio cues. To explore the design space in mixed reality, we conducted a workshop with 10 professional designers, generating a physical co-op design space that encompasses prop and interaction design patterns, and how they relate to affordance and cooperative experience. In a within-subjects study of virtual vs. physical co-op experiences (n=16), we observed finer-grained task coordination, fewer collisions, and more strategy-focused communication. Players reported higher collaboration, sense of achievement, enjoyment, and overall preference for physical co-op. This work opens a new design space for co-located play and offers guidance for designing embodied co-op experiences.

Progressive digitisation and adoption of artificial intelligence (AI) are reshaping welfare services in ways that risk compounding inequalities for disabled people. Globally, many governments present these reforms as beneficial--streamlining processes, reducing costs and eliminating delays. Yet digitisation and automation of welfare decision-making can deepen exclusion and erode human accountability. In response, this paper foregrounds the lived experiences of people with the communication disability aphasia in navigating digitised welfare and their perspectives on AI-automated futures. We report findings from a four-stage participatory design study involving eight workshops with 42 recruited co-designers. Reflexive thematic analysis identified five challenges: the cost of performing disability, geographies of inequity, navigating digital bureaucracy, the accessibility paradox and hostile design. Co-designers voiced concerns about AI-automation but envisioned inclusive future alternatives: AI dialogues that are patient, multimodal and supportive; welfare systems that are compassionate, transparent and retain human recourse; and infrastructures that are open, publicly governed and truthful.

Pedestrian–automated vehicle(AV) encounters in shared spaces often involve hesitation and ambiguity. Vehicle-mounted external human–machine interfaces(eHMIs) can help, but obscured or poorly timed communications create significant challenges. To address this, we present a mobile smart pole interaction unit(SPIU) with integrated cameras and LED displays, designed as a pedestrian-side system to deliver explicit cues(``WALK,'' ``STOP''). An in-the-wild evaluation of the SPIU(N=21) using a four-factor analysis (CarBehavior, Mobility, eHMI, SPIU) showed that the SPIU improved understandability, trust, and perceived safety, and reduced workload compared with the baseline, with a combination(eHMI+SPIU) yielding the strongest results. Beyond these quantitative benefits, participants appreciated the mobility of the SPIU for its ``clear'' and ``easy to decide'' mediation. This work contributes to(1) a design and deployment framework for a mobile SPIU and(2) an in-the-wild evaluation protocol for pedestrian–AV interactions in nonsignalized spaces. Our work sparks discussions on real world evaluations involving detailed vehicle kinematics and accessible multimodality(e.g., audio), focusing on the role of personal robots as user-side eHMIs.

Decades of online fashion retail and investment in its usability have led to a seemingly refined user experience. Yet, our study shows that female online shoppers, who make up the largest user group, experience a conflicted love-hate relationship when shopping online. Adopting a feminist HCI perspective, we contribute insights from a multi-step qualitative approach involving probes, co-design, iterative prototyping and body maps. We demonstrate that even screen-based website designs are deeply entangled with users’ embodied experiences. Through our analysis, we identify where such designs contribute to heightened emotional labour and negative user experiences. Our work offers concrete design implications centred around inclusivity, the predictive user experience of wearing and caring for garments, and transparency of information. We embody these implications in an interactive prototype and use it to validate our recommendations for a body-centred approach to UX design.

Always-on sensing of AI applications on AR glasses makes traditional permission techniques inefficient for context-dependent private visual data within home environments. Home presents a challenging privacy context due to massive sensitive objects and the intimate nature of daily routines. We propose VisGuardian, a fine-grained content-based visual permission technique for AR glasses. VisGuardian features a group-based control mechanism that enables users to efficiently manage permissions for multiple private objects. VisGuardian detects objects using YOLO and adopts a pre-classified schema to group them. By selecting a single object, users can obscure groups of related objects based on criteria including privacy sensitivity, object category, or spatial proximity. A technical evaluation shows VisGuardian achieves mAP50 of 0.6704 with only 14.0 ms latency and a 1.7% increase in battery consumption per hour. Furthermore, a user study (N=24) comparing VisGuardian to slider-based and object-based baselines found it to be significantly faster for setting permissions and was preferred by users for its efficiency, effectiveness, and ease of use.

Human–animal interaction in virtual reality has been explored for stress relief, yet balancing practical ease of use with natural haptic experience for relaxation remains a key challenge. We investigated whether mid-air ultrasound haptics, rendering breathing and fur stroking cues without wearable haptic devices, could enhance relaxation with a virtual cat. We first conducted a perceptual study to design a tactile cue for a cat’s breathing. By synchronizing expansion–contraction of the ultrasound focal region with intensity modulation, we demonstrated the realism and expressivity of the breathing cue. Next, we conducted an application study in which participants engaged in a short relaxation session with a virtual cat. Physiological and subjective measures showed that ultrasound haptics enhanced relaxation compared to both non-haptic interaction and controller-based vibrotactile feedback. These findings suggest that ultrasound haptics can extend VR-based human–animal interaction by combining accessibility with psychological benefits, opening new opportunities for well-being and therapeutic applications.

Generative AI assistants are being rapidly adopted in universities, supporting students in coursework and faculty in academic tasks. To address privacy concerns, some institutions introduced institutional AI assistants, typically wrappers around commercial models (e.g., ChatGPT) with added governance and data protections. However, university-affiliated users appear to rely more on commercial tools (e.g., ChatGPT, Gemini). We conducted a survey (n=260) at one U.S. university to examine preferences, usage scenarios, and perceptions of trust, privacy, and experience with institutional and commercial AI. Participants trusted institutional tools more and considered them more privacy protective, nevertheless commercial tools were often favored for writing, programming, and learning due to their features and utility. Findings reveal a trade-off between privacy and trust versus utility, highlighting complementary adoption patterns and design opportunities for both institutional and commercial AI in higher education.

Non-custodial wallets (NCWs) grant users full control over their keys and crypto assets, whereas custodial wallets (CWs) rely on centralized exchanges. Security breaches at major exchanges are on the rise, exemplified by the 2022 FTX fraud, yet their influence on users' security perceptions and risk mitigation behaviors remains understudied. We conducted 22 semi-structured interviews and a follow-up survey with 430 participants to address this gap concerning the FTX incident. We find that learning about FTX reduced trust in CWs and increased perceived security of NCWs. However, most users who were using non-SEC-compliant (equally risky) CWs did not transfer crypto to mitigate potential threats, showing continued trust in current wallets. Those who did often moved all funds from CWs to traditional banks rather than adopting NCWs. Notably, only one-third of survey participants were aware that centralized exchanges hold their private keys, and many still used noncompliant exchanges.

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.

The rise in popularity and value of esports motivates the creation of computational training tools (CTTs) for learning, assessment, and skill gain. While some tools exist commercially, much of the work in the research literature is rarely used outside of a lab, resulting in a lack of knowledge on the challenges involved in real-world integration. In this work, we develop a bespoke CTT for League of Legends, MySkills, based on prior work and deploy it at a professional training academy for three months. Based on two rounds of stakeholder interviews, we uncover insights into users' perspectives on using CTTs in esports coaching and the challenges inherent in introducing a novel tool into an existing, real-world esports training context. From these results, we connect the domain of esports training technology to existing conversations on translational HCI, challenges in bridging research and practice, and present implications for future work.

Gathering information about AI systems is essential for contesting their use; it forms the basis of arguments about how AI is causing harm. Information thus plays a central role for advocates like lawyers, journalists, and auditors contesting harmful AI systems. However, there is little systematic understanding of how these actors, many of whom are newly encountering AI in their advocacy work, access and use information effectively in this process. Understanding this information work can offer valuable insights for supporting effective contestation of harmful AI systems. To better understand information work in AI contestation, we interviewed 18 advocates in the United States (US) who have contested the use of AI in high-stakes domains, such as public benefits and housing. We characterize advocates' strategies for accessing information that is useful for contestation, including a range of creative yet resource-intensive and risky workarounds that they use to overcome opacity. We discuss implications of our findings for the effectiveness of popular transparency policy strategies in the US and offer additional ways to support the social fabric that makes advocates' information work effective.

Health artificial intelligence (AI) is often developed in high-stakes, data-scarce contexts, where both clinical validity and patient comprehension are critical; however, rigorous, multi-level evaluation of explanations in real-world patient-facing settings remains challenging. To enhance patient understanding and trust, we propose a practical blueprint for designing and evaluating medically aligned, patient-centered explanation (MAP-X). We propose this blueprint through MAP-X, a system that employs a large language model (LLM) with retrieval-augmented generation (RAG) to translate clinical assessments into an understandable interface. We conducted a three-phase evaluation following a multi-level validation framework: a functional evaluation of faithfulness, a clinician evaluation of workflow suitability, and a patient evaluation of perceived understanding and trust. Our findings suggest that MAP-X may support clinical adoption. In the patient study, MAP-X showed higher reported trust and a positive trend in explanation satisfaction. Interviews suggested clearer understanding of assessment results. Overall, MAP-X produced clinically relevant explanations with reasonable faithfulness and usability. Clinician oversight remains necessary.

Fitness-tracking platforms, such as Strava and Garmin Connect, are increasingly popular and are reshaping how people monitor and share their physical activity. Given the sensitive nature of the data users share, these platforms implement a series of privacy features, including controls for profile visibility, activity sharing, and the specification of sensitive locations.In this paper, we present the first large-scale study aiming to quantify user adoption of privacy features on fitness-tracking platforms and to shed light on the reasoning behind identified trends.We apply a mixed-method.First, we provide a systematic categorization of the privacy features implemented across major fitness-tracking platforms.We then quantify their adoption, using the Strava and Garmin Connect platforms as our case studies, by analyzing 197,873 public activity records, revealing a gap between available controls and actual adoption.We complement our empirical evaluation by surveying 182 participants, confirming low adoption and identifying barriers.Our findings highlight limited use of privacy features and provide insights into the reasons for this trend, including a lack of awareness, perceived low necessity, concerns about functionality, and difficulties adjusting settings.We also discuss potential strategies to overcome these challenges.

Pet loss is a distressing experience often underappreciated by societal norms, leading to disenfranchised grief. We investigate how bereaved pet owners engage in online support groups, focusing on their motivations, interactions, and challenges. Through in-depth interviews with 18 participants, we identified key motivations for joining, including grief expression and validation, emotional and informational support, anonymity and accessibility. Engagement in these groups facilitated emotional expression, grief validation, memorialization practices, and the development of coping mechanisms, while also fostering shared rituals and collective identity. However, challenges like compulsory grief—where grievers feel pressured to remain in a constant state of mourning—and insufficient support for dynamic coping persisted. Drawing on the dual process model of bereavement, we propose the metaphor of oscillation design, balancing loss-oriented and restoration-oriented coping. Our findings show that current platforms overemphasize loss, underscoring the need for design interventions that rebalance asymmetric oscillation and enable more dynamic coping trajectories.

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.

ACM's transition to full open access (OA) may fundamentally reshape publication practices within the SIGCHI community. However, the current state of OA adoption and the potential impact of this shift remain underexplored, which limits both scholarly understanding and informed actions. To address this gap, we conduct a large-scale bibliometric analysis of SIGCHI publications from 2001 to 2024. We document the prevalence of OA in our community and author characteristics associated with OA uptake, and assess the projected impact of the transition regarding financial cost and scholarly visibility. Our results indicate that our community is well-positioned for this shift, with fewer than 10.1% of the papers expected to incur additional OA fees. This move to OA is likely to boost citations, especially cross-community citations, but risks further marginalizing under-resourced authors. We discuss the broader implications of these findings for fostering a sustainable future for our community.

Artificial intelligence (AI)-based decision support systems can be highly accurate yet still fail to support users or improve decisions. Existing theories of AI-assisted decision-making focus on calibrating reliance on AI advice, leaving it unclear how different system designs might influence the reasoning processes underneath. We address this gap by reconsidering AI interfaces as collections of intelligent reasoning cues: discrete pieces of AI information that can individually influence decision-making. We then explore the roles of eight types of reasoning cues in a high-stakes clinical decision (treating patients with sepsis in intensive care). Through contextual inquiries with six teams and a think-aloud study with 25 physicians, we find that reasoning cues have distinct patterns of influence that can directly inform design. Our results also suggest that reasoning cues should prioritize tasks with high variability and discretion, adapt to ensure compatibility with evolving decision needs, and provide complementary, rigorous insights on complex cases.

While web agents gained popularity by automating web interactions, their requirement for interface access introduces privacy risks that are understudied, particularly from users' perspective. Through a formative study (N=15), we found that users frequently misunderstand agent data practices, and desire unobtrusive, transparent data management. To achieve this, we developed PrivWeb, a trusted add-on on web agents that utilizes a localized LLM to anonymize private information on interfaces based on user preferences. It employs a tiered delegation to balance automation and intrusiveness, using ambient notifications for low-sensitivity data and enforces a mandatory pause for high-sensitivity data. The user study (N=14) across travel, information retrieval, shopping, and entertainment tasks showed that PrivWeb enhances perceived privacy protection and trust compared to transparency-only baselines, without increasing cognitive load. Crucially, we identified user delegation strategies: they prefer to manually execute sensitive steps for high-sensitivity data, while granting agent access to low-sensitivity data.