Ready for the Touch: Exploring Users' Perceived Transparency of Robot Pre-Touch CuesThe emergence of embodied intelligence is expending the landscape of human-robot interaction (HRI) to include more direct and physical contact. While robot touch can provide assistance or comfort, a lack of Perceived Transparency before the touch, meaning limited clarity of the robot’s intentions, can lead to user confusion and anxiety. Despite its importance for user experience, perceived transparency towards robot's pre-touch conveyance method remains underexplored. This study systematically investigates how touch information conveyance affects perceived transparency and safety. Informed by a 340-person survey, we conducted a video-based study with 41 participants, comparing nine different robot pre-touch cues. Our mixed-methods approach combined subjective ratings and interviews with objective measures such as eye-tracking. We found that greater perceived transparency significantly enhances perceived safety. Video Displays were most effective at improving clarity, while task-oriented touch was more readily accepted than emotion-oriented touch. Based on these findings, we propose evidence-based design guidelines for safer and more effective robot touch interaction.2026RZRan Zhao et al.Beihang UniversitySocial Robot InteractionHuman-Robot Collaboration (HRC)Affective Feedback & Emotion Regulation InterfacesCHI
“Who moved my heart brush?” Realms of the Heart: A Human-AI Collaborative Gamified Adjuvant Treatment Application for Depressed Teens Based on Painting TherapyPainting therapy has become an important approach to the treatment of depression in adolescents. However, depressed adolescents face low self-confidence, low engagement, and self-absorption when undergoing painting therapy. Informed by need-finding investigations, We developed Realms of the Heart, a human-AI collaborative gamified complementary treatment system for depressed adolescents. Realms of the Heart includes two AI-driven strategies: the transformation of natural objects collected by teens’ exploration into brushes, and the generation of sketches corresponding to natural objects by using brushes based on ControlNet. We conducted a randomized controlled trial. Participants were randomized into an experimental group and a control group. The results showed that Realms of the Heart increased users’ motivation and significantly alleviated their level of depression. These findings not only shed light on the role of human-AI co-created painting therapy in the treatment of adolescent depression but also pave the way for more informed design strategies for art therapy.2026NWNasi Wang et al.School of Design, Shanghai Jiaotong UniversitySerious & Functional GamesAffective Feedback & Emotion Regulation InterfacesGenerative AI (Text, Image, Music, Video)CHI
All Futures at Once: Supporting Speculative Design for Placemaking with Multi-Agent Social SimulationPlacemaking transforms physical spaces into socially meaningful places, with long-term impacts depending on how future communities inhabit and interact with them. Speculative design helps envision such futures, yet existing approaches often produce static representations that emphasize spatial form over evolving activity. We present ParaScape, a design support system that facilitates speculative design for placemaking by generating dynamic speculative objects through an underlying LLM-based multi-agent social simulation framework. The framework models heterogeneous agents with group-specific preferences and sensitivities, simulating context-sensitive behaviors and interactions that produce evolving scenarios. These scenarios are visualized as image sequences, where each scenario depicts multiple activities unfolding within a place at a given moment. ParaScape builds on this framework to allow designers to explore scenarios, analyze activity diversity and evolvability, and reflect on trade-offs among stakeholder needs. Evaluations through two experiments, a user study, and two case studies show that ParaScape supports critical reasoning and inclusive placemaking.2026JLJiayang Li et al.Tongji UniversityParticipatory DesignDesign FictionSmart Cities & Urban SensingCHI
Obscuring Undesirable Individuals to Alleviate Social Discomfort Using Diminished RealityIn 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".2026JZJun Zhang et al.Hubei Institute of Fine ArtsImmersion & Presence ResearchIdentity & Avatars in XREmpathy & Emotional DesignCHI
Behind the Meme: Understanding User Experiences with Memes on Social MediaWhile memes enhance social interaction on social media, they can raise privacy and security concerns. Despite research on overtly toxic or unsafe memes, little attention has been given to users' experiences with seemingly safe memes and how contextual factors trigger privacy concerns. This study explores users’ comfort levels, influencing factors, underlying reasons for discomfort, and unmet needs when engaging with such memes. We first collected and analyzed 2,317 Reddit posts describing real-world meme experiences, then conducted an online survey with 324 participants to evaluate comfort across curated scenarios. Our findings reveal that perceived-safe memes can cause harm when shared inappropriately, with comfort shaped by content and context. Privacy concerns intensify with deeper involvement, strangers, and sensitive meme topics. We identified users' desire for consent and control in meme interactions. Based on our study, we make recommendations for users, developers of social media platforms and policymakers to address meme-related privacy and contextual concerns.2026YNYuqi Niu et al.Shanghai Jiao Tong UniversityPrivacy Perception & Decision-MakingSocial Platform Design & User BehaviorContent Moderation & Platform GovernanceCHI
LubDubDecoder: Bringing Micro-Mechanical Cardiac Monitoring to HearablesWe present LubDubDecoder, a system that enables fine-grained monitoring of micro-cardiac vibrations associated with the opening and closing of heart valves across a range of hearables. Our system transforms the built-in speaker, the only transducer common to all hearables, into an acoustic sensor that captures the coarse "lub-dub" heart sounds, leverages their shared temporal and spectral structure to reconstruct the subtle seismocardiography (SCG) and gyrocardiography (GCG) waveforms, and extract the timing of key micro-cardiac events. In an IRB-approved feasibility study with 25 users, our system achieves correlations of 0.88-0.95 compared to chest-mounted reference measurements in within-user and cross-user evaluations, and generalizes to unseen hearables using a zero-effort adaptation scheme with a correlation of 0.91. Our system is robust across remounting sessions and music playback.2026SZSiqi Zhang et al.Carnegie Mellon UniversityBiosensors & Physiological MonitoringEmotion-Sensing WearablesFitness Tracking & Physical Activity MonitoringCHI
CoBRA: Programming Cognitive Bias in Social Agents Using Classic Social Science ExperimentsThis paper introduces CoBRA, a novel toolkit for systematically specifying agent behavior in LLM-based social simulation. We found that conventional approaches that specify agent behavior through implicit natural-language descriptions often do not yield consistent behavior across models, and the resulting behavior does not capture the nuances of the descriptions. In contrast, CoBRA introduces a model-agnostic way to control agent behavior that lets researchers explicitly specify desired nuances and obtain consistent behavior across models. At the heart of CoBRA is a novel closed-loop system primitive with two components:(1) Cognitive Bias Index that measures the demonstrated cognitive bias of a social agent, by quantifying the agent’s reactions in a set of validated classic social science experiments; (2) Behavioral Regulation Engine that aligns the agent’s behavior to exhibit controlled cognitive bias. Through CoBRA, we show how to operationalize validated social science knowledge (i.e., classical experiments) as reusable “gym” environments for AI—an approach that may generalize to richer social and affective simulations beyond bias alone.2026XLXuan Liu et al.University of California San DiegoHuman-LLM CollaborationExplainable AI (XAI)Brain-Computer Interface (BCI) & NeurofeedbackCHI
RageSense: Leveraging Acoustic Sensing and LLM-Based Intervention for Emotion Regulation in Mobile GamingRageSense introduces a novel system for detecting and regulating player frustration during mobile gaming. Instead of relying on coarse emotion labels, RageSense estimates users’ valence and arousal levels in real time using near-ultrasonic acoustic sensing. By analyzing facial muscle movements via built-in smartphone speakers and microphones, our approach enables emotion sensing without requiring cameras or wearables, constituting a more unobtrusive, environment-resilient, and privacy-friendly approach than traditional emotion recognition. To transform detection into action, we integrate a large language model (LLM) that generates empathetic, context-aware interventions based on gameplay screenshots, behavioral signals, and emotional trajectories. These interventions are delivered in real time, tailored to the user’s emotional state, and designed to mitigate rage while enhancing player well-being. In a 53-participant field study, our system improved emotional state immediately after triggers and was preferred over random or template-based messages. To our knowledge, this is the first demonstration of near-ultrasonic, on-phone valence-arousal regression during mobile gameplay that directly drives real-time, context-aware interventions.2026CLCong Liu et al.South China University of TechnologyEmotion Recognition & DetectionAffective Feedback & Emotion Regulation InterfacesGenerative AI (Text, Image, Music, Video)CHI
Prosocial AI Apologies on the Road: Emotional Compensation for Other Drivers' MisbehaviorAggressive 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.2026JZJun Zhang et al.Hubei Institute of Fine ArtsHead-Up Display (HUD) & Advanced Driver Assistance Systems (ADAS)In-Vehicle Haptic, Audio & Multimodal FeedbackEmotion Recognition & DetectionCHI
"Our Secret Language": Co-Creating and Ritualizing Affective Haptics in Long-Distance RelationshipsLong-distance relationships (LDRs) struggle to sustain intimacy without physical touch. Existing mediated social touch systems rely on designer-authored haptic patterns, which limit opportunities for personalization and shared meaning-making. We present Onni, a haptic interface that lets couples collaboratively define and experience a shared library of haptic interactions. In Study 1, we conducted co-creation workshops (n=20) to examine how couples negotiate and align meanings in haptic interactions. In Study 2, we deployed Onni in everyday routines (n=6) to explore how these interactions are adopted, adapted, and ritualized. Our findings illustrate that couples co-create and personalize haptic interactions through continuous exploration, negotiation, and situational adaptation. By integrating a dyadic co-design approach, an end-user authoring interface for a shared action–feedback haptic repertoire, and a longitudinal view of how meanings evolve in everyday LDR routines, this work advances understanding of haptic meaning-making as a collaboratively constructed and ritualized process. It offers concrete design implications for building personalized, evolving haptic systems that support intimacy in LDRs.2026MYMengshi Yang et al.Tongji UniversityHaptic WearablesLong-Distance Relationship TechnologyAffective Human-Computer DialogueCHI
Tell Me What I Missed: Interacting with GPT during Recalling of One-Time Witnessed EventsLLM-assisted technologies are increasingly used to support cognitive processing and information interpretation, yet their role in aiding memory recall—and how people choose to engage with them—remains underexplored. We studied participants who watched a short robbery video (approximating a one-time eyewitness scenario) and composed recall statements using either a default GPT or a guided GPT prompted with a standardized eyewitness protocol. Results show that default-condition participants who believed they had a clearer understanding of the event were more likely to trust GPT’s output, whereas guided-condition participants showed stronger alignment between subjective clarity and actual recall. Additionally, participants evaluated the legitimacy of the individuals in the incident differently across conditions. Interaction analysis further revealed that default-GPT users spontaneously developed diverse strategies, including building on existing recollections, requesting potentially missing details, and treating GPT as a recall coach. This work shows how GPT–user interplay subconsciously affects beliefs and perceptions of remembered events.2026SZSuifang Zhou et al.City University of Hong KongHuman-LLM CollaborationExplainable AI (XAI)Empathy & Emotional DesignCHI
How They Type: Eye and Finger Movement Strategies in Typing of Individuals with Cerebral PalsyTyping is essential for communication, yet the input behavior of individuals with cerebral palsy (CP) remains underexplored. We investigated 31 CP typists and 31 non-disabled controls using keystroke logging, eye tracking, and motion capture. Our study found that CP typists were slower and less rhythmically stable, but by prioritizing accuracy, their overall keyboard efficiency was comparable to controls. They adopted compensatory visual strategies such as shorter and more frequent fixations, greater reliance on the keyboard, and more gaze shifts, and displayed diverse finger usage strategies from single-finger to multi-finger input. We found that using more fingers did not necessarily result in faster typing. Subtype analysis showed spastic CP typists followed a "slow but steady" rhythm with consistent inter-key intervals, whereas athetoid CP typists exhibited a "fast but unstable" rhythm with greater variability, highlighting distinct mechanisms of typing in CP and providing insights for personalized assistive technologies.2026TSTingting Song et al.Institute of Psychology, Chinese Academy of SciencesMotor Impairment Assistive Input TechnologiesEye/Head-Controlled TypingHealth Self-TrackingCHI
Collab: Fostering Critical Identification of Deepfake Videos on Social Media via Synergistic AnnotationIdentifying deepfake videos on social media platforms is challenged by dynamic spatio-temporal artifacts and inadequate user tools. This hinders both critical viewing by users and scalable moderation on platforms. Here, we present Collab, a web plugin enabling users to collaboratively annotate deepfake videos. Collab integrates three key components: (i) an intuitive interface for spatio-temporal labeling where users provide confidence scores and rationales, facilitating detailed input even from non-experts, (ii) a novel confidence-weighted spatio-temporal Intersection-over-Union (IoU) algorithm to aggregate diverse user annotations into accurate aggregations, and (iii) a hierarchical demonstration strategy presenting aggregated results to guide attention toward contentious regions and foster critical evaluation. A seven-day online study (N=90), where participants annotated suspicious videos when viewing an online experimental platforms, compared Collab against two conditions without aggregation or demonstration respectively. Collab significantly improved identification accuracy and enhanced reflection compared to non-demonstration condition, while outperforming non-aggregation condition for its novelty and effectiveness.2026SZShuning Zhang et al.Tsinghua UniversityDeepfake & Synthetic Media DetectionContent Moderation & Platform GovernanceMisinformation & Fact-CheckingCHI
A-MIrror: Augmented Reality Mirror System for Enhanced Visual Illusion in Post-Stroke Upper-Limb RehabilitationMirror Therapy (MT) effectively supports post-stroke upper-limb rehabilitation but requires professional supervision and often leads to low patient motivation. While digital MT systems address these limitations, they typically compromise the embodiment benefits of traditional mirrors. Through informant-driven design with interdisciplinary experts, we developed A-MIrror, an augmented reality mirror system that preserves the view of both mirrored hand and overlays digital guidance. Using dual cameras for eye and hand tracking, the system creates a visual illusion where both real and mirrored hands appear to naturally interact with virtual 3D objects on screen. Our evaluation with 14 post-stroke patients and 8 therapists demonstrates that A-MIrror significantly enhances motivation compared to traditional MT (p < .01) while achieving comparable embodiment experiences and even superior illusion latency (30.47% faster, p < .001) to traditional mirrors. This study presents a promising approach for independent post-stroke rehabilitation that integrates the strengths of both traditional and digital MT, offering insights for enhancing future digital rehabilitation applications.2026LCLeheng Chen et al.Tongji UniversityVR Medical Training & RehabilitationEye Tracking & Gaze InteractionTelemedicine & Remote Patient MonitoringCHI
Decentralized Web3 Non-Fungible Token Community for Societal Prosperity? A Social Capital PerspectiveIn the rapidly evolving Web3 world, non-fungible token (NFT) communities are reshaping the formation, distribution, and activation of social capital in ways distinct from traditional models. However, despite their growing impact on societal prosperity, a comprehensive understanding of social capital dynamics within Web3 NFT communities remains limited. This study explores the Mfers community, a key example within Web3 NFT ecosystems. By analyzing social media and blockchain data and using a Delphi method-based human-large language model (LLM) collaboration, we uncovered unique social capital patterns across six dimensions. Our findings highlight a compelling blend of decentralization, inclusion, trust, and empowerment but also raise critical questions about wealth inequality, content quality, and ethical challenges. Based on the findings, we discussed the uniqueness of social capital in Web3 NFT communities, the tension between technical and power decentralization, and the multidimensional nature of societal prosperity. We also suggested directions for future research on decentralized online communities in the CSCW field. This study provides a systematic perspective on social capital in Web3 NFT communities and introduces an innovative human-LLM collaborative analysis, offering insights into the design and governance of benign decentralized online communities.2025HCHongzhou Chen et al.Getting Things Done With AICSCW
"If I were in Space": Understanding and Adapting to Social Isolation through Designing Collaborative StorytellingSocial isolation can lead to pervasive health issues like anxiety and loneliness. Previous work focused on physical interventions like exercise and teleconferencing, but overlooked the narrative potential of adaptive strategies. To address this, we designed a collaborative online storytelling experience in social VR, enabling participants in isolation to design an imaginary space journey as a metaphor for quarantine, in order to learn about their isolation adaptation strategies in the process. Eighteen individuals participated during real quarantine undertaken a virtual role-play experience, designing their own spaceship rooms and engaging in collaborative activities that revealed creative adaptative strategies. Qualitative analyses of participant designs, transcripts, and interactions revealed how they coped with isolation, and how the engagement unexpectedly influenced their adaptation process. This study shows how designing playful narrative experiences, rather than solution-driven approaches, can serve as probes to surface how people navigate social isolation.2025QGQi Gong et al.Social & Collaborative VRIdentity & Avatars in XRSTEM Education & Science CommunicationDIS
``I am not the primary focus" - Understanding the Perspectives of Bystanders in Photos Shared OnlineWhen taking photos in a crowd, unintended individuals, such as bystanders, are often captured alongside the main subject(s). In an effort to protect bystanders' privacy, existing methods have been developed to automatically detect bystanders. However, inconsistent definitions of who qualifies as a bystander limit their effectiveness. To better understand bystanders' perceptions, we conducted an online survey with 486 participants, analyzing their responses to 864 image-based scenarios and their comfort with sharing these images online. Our results revealed no significant correlation between comfort with public photo sharing and bystander status. We identified limitations in current bystander detection methodologies, as they often fail to recognize bystanders who are not clearly in the background, hence missing individuals with privacy concerns. Moreover, comfort with public sharing varied significantly depending on the image context. Our findings highlight the importance of considering the context of captured images to address privacy concerns in image sharing.2025YNYuqi Niu et al.Shanghai Jiao Tong University; The University of Edinburgh, School of InformaticsPrivacy by Design & User ControlPrivacy Perception & Decision-MakingMisinformation & Fact-CheckingCHI
Crossmodal Interactions in Human-Robot Communication: Exploring the Influences of Scent and Voice Congruence on User Perceptions of Social RobotsOlfactory stimuli have demonstrated the potential to evoke emotional depth and enhance user experiences in HCI. Yet, their role in shaping perceptions of social robots remains largely untapped. This study investigates how olfactory (scent) and auditory (voice) stimuli influence user perceptions of social robots. Using a 2x2 between-subjects design, participants interacted with a social robot under conditions with pleasant/unpleasant scents and friendly/unfriendly voices. The study measured perceived trust, friendliness, competence, and engagement. Our findings show that pleasant scents can enhance the perceptions of friendliness and engagement, while friendly voices can improve trust, friendliness, and engagement. The congruent combination of scents and voices affects friendliness and engagement but does not influence trust and competence. This study contributes to the growing work on multi-sensory Human-Robot Interaction (HRI) design, offering implications for creating more socially interactive robots.2025FCFangyuan Chang et al.Shanghai Jiao Tong University, School of DesignMid-Air Haptics (Ultrasonic)Social Robot InteractionCHI
M^2Silent: Enabling Multi-user Silent Speech Interactions via Multi-directional Speakers in Shared SpacesWe introduce M^2Silent, which enables multi-user silent speech interactions in shared spaces using multi-directional speakers. Ensuring privacy during interactions with voice-controlled systems presents significant challenges, particularly in environments with multiple individuals, such as libraries, offices, or vehicles. M^2Silent addresses this by allowing users to communicate silently, without producing audible speech, using acoustic sensing integrated into directional speakers. We leverage FMCW signals as audio carriers, simultaneously playing audio and sensing the user's silent speech. To handle the challenge of multiple users interacting simultaneously, we propose time-shifted FMCW signals and blind source separation algorithms, which help isolate and accurately recognize the speech features of each user. We also present a deep-learning model for real-time silent speech recognition. M^2Silent achieves Word Error Rate (WER) of 6.5% and Sequence Error Rate (SER) of 12.8% in multi-user silent speech recognition while maintaining high audio quality, offering a novel solution for privacy-preserving, multi-user silent interactions in shared spaces.2025JZJuntao Zhou et al.Shanghai Jiao Tong University, Department of Computer Science and EngineeringEye Tracking & Gaze InteractionVoice User Interface (VUI) DesignPrivacy by Design & User ControlCHI
How the Algorithmic Transparency of Search Engines Influences Health Anxiety: The Mediating Effects of Trust in Online Health Information SearchAdvancements in artificial intelligence-powered search engines have enhanced the efficiency of online health information searches by generating direct answers to queries using top-ranked featured snippets (FS). However, such functionalities may contribute to health anxiety, particularly when the displayed results are distressing. This study investigated the effect of algorithmic transparency (AT) explanations (absence vs. presence) on mitigating FS-triggered health anxiety. The results of an online experiment (N = 206) yielded two key findings: First, participants exposed to AT explanations detailing the selection process of FS experienced reduced trust in the search engine and distressing results, which subsequently alleviated health anxiety. Second, the moderating effect of pre-existing cyberchondria on the relationship between AT explanations and trust was observed, but only within a limited threshold. Overall, the findings empirically validate AT explanations as an effective approach to mitigate FS-induced health anxiety. Theoretical and practical implications are discussed.2025YWYuheng Wu et al.Shanghai Jiao Tong University, School of Media and Communication; City University of Hong Kong, Department of Media and CommunicationExplainable AI (XAI)Algorithmic Transparency & AuditabilityPrivacy Perception & Decision-MakingCHI