GazeZoom: Exploration of Gaze-Assisted Multimodal Techniques for Panning and ZoomingZooming 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.2026YLYilong Lin et al.Southern University of Science and TechnologyEye Tracking & Gaze InteractionSocial & Collaborative VRImmersion & Presence ResearchCHI
FingerBar: A Mid-Air Touch Bar Interface for Earphones Using Finger-Generated AcousticsCurrent touch-based interactions on earphones are limited by hygiene concerns and the small interaction surface. Recent works attempt to bypass these issues with mid-air gesture systems using active acoustic sensing. However, these signals may be audible and pose potential hearing risks. To address this, we propose FingerBar, a mid-air gesture recognition system for earphones that relies solely on microphones without active signal transmission. FingerBar leverages the distinctive friction sounds generated by finger gestures to achieve gesture recognition. We design a gesture filtering pipeline to maintain robustness against daily noise. An adversarial training strategy further enhances user-independent performance. From a set of 16 gestures, we identify the 7 most suitable for FingerBar based on user acceptability. Extensive evaluations demonstrate high accuracy and robustness. Furthermore, a user study confirms the practicality and acceptability of the system. Our findings highlight the promise of passive acoustic sensing as a user-friendly interaction modality for earphones.2026YZYankai Zhao et al.Southern University of Science and TechnologyMid-Air Haptics (Ultrasonic)Hand Gesture RecognitionSmartwatches & Fitness BandsCHI
TingleTouch: Touch Guidance through Electrical Stimulation in Resistance TrainingIn resistance training, trainers employ touch guidance to help trainees control posture and activate muscles. Haptic feedback can extend this support to solitary workouts, but translating the nuances of touch into effective haptic patterns remains challenging. In this paper, we categorize the instructional messages conveyed through trainers' touch guidance and design electrical stimulation patterns to replicate them. A preliminary study with six trainers and six trainees identified six core messages underlying touch guidance. We then designed electrical stimulation patterns for each message and refined them with two sports scientists and a UX designer, ensuring usability and grounding. Finally, sixteen gymgoers evaluated these patterns in a controlled exercise task. Participants reliably distinguished the feedback and used the instructed muscles accordingly, achieving accuracies of 97.14% and 99.22% across two sessions, cross-checked with EMG and pose estimation. These findings demonstrate that the proposed electrical stimulation feedback is intuitive and learnable.2026DKDong-Uk Kim et al.Chung-Ang UniversityElectrical Muscle Stimulation (EMS)Fitness Tracking & Physical Activity MonitoringBehavior Change & Reflection TechnologyCHI
DOLLama: Fostering Family Anti-Bullying Learning through AI-Augmented, Toy-Mediated Educational DramaEducational drama is a proven method for anti-bullying education, but its traditional reliance on teachers and peers limits its accessibility to children and families outside of school. HCI has rarely explored how to augment this practice with AI-infused, interactive role-playing or how to involve parents in the process. We introduce DOLLama, an AI-powered projection-augmented interactive system that transforms children's toys and family-created stories into gamified anti-bullying vignettes. A study with 20 families demonstrated how DOLLama facilitated children’s and parents’ learning. Children used their toys to enact the roles of the one being bullied and bystanders, developing empathy and practicing coping strategies in co-performance with AI-controlled toy characters. By observing this play, parents gained new insights into their child’s strengths and challenges and identified their own knowledge gaps. Based on these findings, we derive HCI design implications for AI-enhanced, toy-mediated educational drama that supports anti-bullying education for children and their families.2026DLDi Liu et al.Southern University of Science and TechnologyCollaborative Learning & Peer TeachingChild-Computer Interaction DesignMental Health Technology for YouthCHI
Novobo: Supporting Teachers' Peer Learning of Instructional Gestures by Teaching a Mentee AI-Agent TogetherInstructional gestures are essential for teaching, enhancing communication and student comprehension. Current training methods for developing these skills can be time-consuming, isolating, or overly prescriptive, e.g., watching lengthy, one-size-fits-all videos. Conversely, research suggests that developing these tacit, experiential skills requires teachers’ peer learning, where they learn from each other and build shared knowledge. While much HCI exploration has applied learning-by-teaching to students’ peer learning, little has explored this approach for teacher professionalization. We present Novobo, an apprentice AI-agent stimulating teachers' peer learning of instructional gestures through verbal and bodily inputs. An evaluation with 30 teachers in 10 collaborative sessions showed Novobo prompted teachers to externalize and share tacit knowledge through dialogue and movement. Teaching an AI mentee together reduced their pressure, facilitating peer exchange and the co-construction of practical knowledge. This work contributes a novel design and empirical insights into how teachable AI-agents can facilitate peer learning in teacher professionalization.2026JJJiaqi Jiang et al.Southern University of Science and TechnologyBrain-Computer Interface (BCI) & NeurofeedbackFull-Body Interaction & Embodied InputHuman Pose & Activity RecognitionCHI
Beyond Words: Measuring User Experience through Speech Analysis in Voice User InterfacesVoice assistants (VAs) are typically evaluated through task performance metrics and self-report questionnaires, but people’s voices themselves carry rich paralinguistic cues that reveal affect, effort, and interaction breakdowns. We present a within-subjects study (N=49) that systematically compared three VA personas across three usage scenarios to investigate whether speech-derived audio features can serve as a proxy for user experience (UX). Participants’ speech was analyzed for temporal, spectral, and linguistic markers, alongside standardized UX measures, brief mood and stress ratings, and a post-study questionnaire. We found correlations between specific speech features and self-reported satisfaction and experience. Furthermore, a machine learning model trained on speech features achieved promising accuracy in classifying UX levels, indicating that this might be a reasonable alternative to self-report instruments. Our findings establish speech as a viable, real-time signal for implicitly measuring UX and point toward adaptive VUIs that respond dynamically to emotional and usability-related vocal cues.2026YMYong Ma et al.University of BergenVoice User Interface (VUI) DesignIntelligent Voice Assistants (Alexa, Siri, etc.)Affective Feedback & Emotion Regulation InterfacesCHI
"Please Share": Promoting Embodied Forms of Interpersonal Stress Sharing Through Real-Time NeurofeedbackSharing with others can play a vital role in helping to cope with stress and emotional burdens. Biofeedback systems have been growing in HCI as supportive of individual stress management; however, the potential of leveraging neurofeedback systems for embodied forms of interpersonal stress sharing remains unexplored. We present MinusxMinus=Plus, an interactive system that externalizes collective neural activity to create a shared affective space. The system allows two participants to visualize their stress in real time via Electroencephalography (EEG) and to jointly transform it through two forms of haptic interaction. The system was tested with 110 participants (55 pairs), and their experience of embodied stress sharing was evaluated. Results show that our system significantly promoted the sharing process, underscoring the central role of visual and haptic modalities. This work contributes empirical evidence and design insights for developing neurofeedback systems that foster interpersonal stress sharing and support mental health.2026MZMirna Zordan et al.Southern University of Science and TechnologyBrain-Computer Interface (BCI) & NeurofeedbackAffective Feedback & Emotion Regulation InterfacesEmotion-Sensing WearablesCHI
Agentic Audio Moderators vs Humans in Think-Aloud Usability TestingAgentic AI holds promise for usability testing, yet its role as an audio moderator in think-aloud protocols is not well understood. This study explores: (1) how to design and develop an agentic audio moderator for think-aloud usability testing, and (2) how participants moderated by an agentic moderator differ from those moderated by a human regarding task performance, verbalization behaviors, user experience, and social perceptions of the moderator. Using a design-based research approach, we interviewed nine UX experts, iteratively developed an AI moderator, and evaluated it in a randomized controlled trial (N=60) with a note-taking application. Results suggest that significant differences were not observed between AI and human moderators in task performance or verbalization behaviors, though AI moderators received lower social perception ratings. This work contributes the first design-oriented evaluation of AI moderators in usability testing, offering implications for developing more acceptable and effective agentic audio moderators.2026WZWangda Zhu et al.The Hong Kong Polytechnic UniversityGenerative AI (Text, Image, Music, Video)AI-Assisted Decision-Making & AutomationExplainable AI (XAI)CHI
Living with Data: Exploring Physicalization Approaches to Sedentary Behavior Intervention for Older Adults in Everyday LifeSedentary behavior is a critical health risk for older adults. Although digital interventions are widely available, they primarily rely on screen-based notifications that can feel clinical or cognitively demanding, and are thus often ignored over time. This paper presents a three-phase Research through Design methodology to explore data physicalization approaches that ambiently represent sedentary data patterns using decor artifacts in older adults’ homes. These artifacts transformed abstract data into aesthetic, evolving forms that became part of the domestic landscape. Our research revealed how these physicalizations fostered self-reflection, family conversations, and encouraged active lifestyles. We demonstrate how qualities like aesthetic ambiguity and slow revelation can empower older adults, fostering a reflective relationship with their well-being. Ultimately, we argue that creating data physicalizations for older adults necessitates a shift from merely informing users to enabling them to live with and through their data.2026SHSiying Hu et al.City University of Hong KongData PhysicalizationElderly Care & Dementia SupportBehavior Change & Reflection TechnologyCHI
Harmonizing the Senses: Designing a Cross-Modal Interactive Art System to Enhance Older Adults’ Affective ExperiencesMultisensory stimulation promises in improving older adults’ affective experiences, yet its effectiveness depends on seamless affective congruency across sensory cues. This study investigated how visual, auditory, and kinetics correspondence and congruency shape affective experiences through two experiments. Experiment I examined timbre–color associations, showing that affective alignment strengthens perceived correspondence. Experiment II explored auditory–kinetics synchrony in a cross-modal art system, revealing no significant differences across conditions but indicating that older adults with lower cognitive abilities reported higher pleasure than higher-ability peers. Building on these results, an artificial intelligence (Al)-infused mode was integrated to transform strokes into real-time ink-style artworks, reducing cognitive effort, sustaining engagement. Findings demonstrate that AI enhances positive affect (pleasure, surprise, valence, and arousal) and mitigates negative affect (sadness, anger), with effects maximized by high sensory synchrony, providing compensatory support for users with lower cognitive abilities. These findings inform multisensory system design for older adults’ cognitive and affective needs.2026SASihan An et al.Harbin Institute of Technology, ShenzhenMultisensory Fusion ExperienceEmotion-Sensing WearablesVR Medical Training & RehabilitationCHI
Remembering with Reminiscope: Codesigning with Generative AI for Reminiscence Among Older AdultsGenerative AI has shown the potential to support older adults to reminisce about the past by producing personalized memory-related content despite the person's varied ability to elaborate or the lack of memory cues. We present two studies to investigate how generative AI can support older adults in individual and group reminiscence. In Study 1, we conducted individual co‑design sessions with 16 older adults, during which participants created textile collages inspired by personal memories and then used generative AI to transform these creations into memory‑related video content. In the second study, we incorporate the textile collages and AI-generated videos into an interactive artifact, Reminiscope, and introduce it in a series workshops with 15 participants (with 14 returning participants from Study 1) to support group reminiscence. Findings from these studies reveal how older adults’ perspectives towards collaborating with generative AI for creating memory-related content, and their experiences of engaging with an AI‑enhanced interactive artifact during shared reminiscence activities. Our work contributes to the emerging trend of leveraging generative AI to support reminiscence in older adults, and provide design implications for future reminiscence technologies.2026LZLisha Zhu et al.School of designGenerative AI (Text, Image, Music, Video)Inclusive DesignElderly Care & Dementia SupportCHI
ProjecTA: A Semi-Humanoid Robotic Teaching Assistant with In-Situ Projection for Guided ToursRobotic teaching assistants (TAs) often use body-mounted screens to deliver content. In nomadic, walk-and-talk learning, such as tours in makerspaces, these screens can distract learners from real-world objects, increasing extraneous cognitive load. HCI research lacks empirical comparisons of potential alternatives, such as robots with in-situ projection versus screen-based counterparts; little knowledge has been derived for designing such alternatives. We introduce ProjecTA, a semi-humanoid, gesture-capable TA that guides learners while projecting near-object overlays coordinated with speech and gestures. In a mixed-method study (N=24) in a university makerspace, ProjecTA significantly reduced extraneous load and outperformed its screen-based counterpart in perceived usability, usefulness of visual display, and cross-modal complementarity. Qualitative analyses revealed how ProjecTA’s coordinated projections, gestures and speech anchored explanations in place and time, enhancing understanding in ways a screen could not. We derive key design implications for future robotic TAs leveraging spatial projection to support mobile learning in physical environments.2026HZHanqing Zhou et al.Southern University of Science and TechnologySocial Robot InteractionTangible Interaction in EducationCitizen Science & Crowdsourced DataCHI
Desirable Unfamiliarity: Insights from Eye Movements on Engagement and Readability of Dictation InterfacesTranscripts displayed on dictation interfaces can be hard to read due to recognition errors and disfluencies. LLM-based text auto-correction could help, but changing the text during production could lead to distraction and unintended phrasing. To understand how to balance readability, attention, and accuracy, we conducted an eye-tracking experiment with 20 participants to compare five dictation interfaces: PLAIN (real-time transcription), AOC (periodic corrections), RAKE (keyword highlights), GP-TSM (grammar-preserving highlights), and SUMMARY (LLM-generated abstractive summary). By analyzing participants’ gaze patterns during speech composition and reviewing processes, we found that during composition, participants spent only 7%-11% of their time in active reading regardless of the interface. Although SUMMARY introduced unfamiliar words and phrasing during composition, it was easier to read and more preferred by participants. Our findings suggest a high user tolerance for altering spoken words in LLM-enabled diction interfaces.2026ZLZhaohui Liang et al.University of Chinese Academy of SciencesLanguage Model-Assisted Text InputAI-Assisted Writing & Text GenerationEye Tracking & Gaze InteractionCHI
The People's Gaze: Co-Designing and Refining Gaze Gestures with Users and ExpertsAs 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.2026YLYaxiong Lei et al.University of St AndrewsEye Tracking & Gaze InteractionHand Gesture RecognitionPrototyping & User TestingCHI
ASafePlace: User-Led Personalization of VR Relaxation via an Art Therapy ActivityTo overcome the lack of deep personalization in standard biofeedback methods, we introduce ASafePlace, a system utilizing an AI-powered, art-therapy-inspired exercise called The Safe Place, to create a personalized VR biofeedback experience. In our system, users sketch a personal sanctuary from memory, which is then transformed into a customized 360° virtual environment with personalized audio guidance for relaxation training. A study with 52 participants showed this approach effectively reduced anxiety and increased user presence, while the integration of art-therapy-inspired activity and biofeedback produced strong improvements in physiological relaxation, measured by heart rate variability and respiration rate. Qualitative results showed how participants' sense of familiarity and presence was enhanced by the symbolic elements and natural sanctuaries created from their autobiographical memories. Our findings demonstrate that art-therapy-inspired activity is a powerful tool for creating highly effective and individualized relaxation experiences, naturally connecting the virtual environment to a user's core memories and emotions.2026CZChuyang Zhang et al.Southern University of Science and TechnologyImmersion & Presence ResearchVR Medical Training & RehabilitationAffective Feedback & Emotion Regulation InterfacesCHI
GenFaceUI: Meta-Design of Generative Personalized Facial Expression Interfaces for Intelligent AgentsThis work investigates generative facial expression interfaces for intelligent agents from a meta-design perspective. We propose the Generative Personalized Facial Expression Interface (GPFEI) framework, which organizes rule-bounded spaces, character identity, and context--expression mapping to address challenges of control, coherence, and alignment in run-time facial expression generation. To operationalize this framework, we developed GenFaceUI, a proof-of-concept tool that enables designers to create templates, apply semantic tags, define rules, and iteratively test outcomes. We evaluated the tool through a qualitative study with twelve designers. The results show perceived gains in controllability and consistency, while revealing needs for structured visual mechanisms and lightweight explanations. These findings provide a conceptual framework, a proof-of-concept tool, and empirical insights that highlight both opportunities and challenges for advancing generative facial expression interfaces within a broader meta-design paradigm.2026YGYate Ge et al.Tongji UniversityGenerative AI (Text, Image, Music, Video)Agent Personality & AnthropomorphismAI-Assisted Creative WritingCHI
When Generative AI Is Intimate, Sexy, and Violent: Examining Not-Safe-For-Work (NSFW) Chatbots on FlowGPTUser-created chatbots powered by generative AI offer new ways to share and interact with Not-Safe-For-Work (NSFW) content. However, little is known about the characteristics of these GenAI-based chatbots and their user interactions. Drawing on the functional theory of NSFW on social media, this study analyzes 376 NSFW chatbots and 307 public conversation sessions on FlowGPT. Findings identify four chatbot types: roleplay characters, story generators, image generators, and do-anything-now bots. AI Characters portraying fantasy personas and enabling hangout-style interactions are most common, often using explicit avatar images to invite engagement. Sexual, violent, and insulting content appears in both user prompts and chatbot outputs, with some chatbots generating explicit material even when users do not create erotic prompts. In sum, the NSFW experience on FlowGPT can be understood as a combination of virtual intimacy, sexual delusion, violent thought expression, and unsafe content acquisition. We conclude with implications for chatbot design, creator support, user safety, and content moderation.2026XLXian Li et al.Southern University of Science and TechnologyGenerative AI (Text, Image, Music, Video)Agent Personality & AnthropomorphismOnline Harassment & Counter-ToolsCHI
SoundWeAR: Co-Designing AR Sound Cues to Support Outdoor Awareness for DHH IndividualsFor Deaf and Hard of Hearing (DHH) individuals, limited access to sound cues in outdoor environments can reduce situational awareness, making it challenging to notice events and respond to potential dangers. To address this, we investigated the needs for sound awareness and preferences of DHH individuals for visualizing environmental sounds using AR glasses. We conducted four participatory design workshops with DHH participants, social workers, and designers to explore sound awareness needs and co-design ideal visual representations. Based on our insights, we conducted interviews with 15 DHH participants to select their preferred visualizations. The most voted designs were implemented in prototype, which eight DHH participants evaluated in outdoor environment. Results demonstrate that visualizing sound cues through AR can enhance situational awareness and increase sense of safety and confidence among DHH individuals while walking outdoors. Our findings provide design suggestions for translating auditory information into accessible visual representations for DHH users.2026ASAnna Surovkova et al.Southern University of Science and TechnologyHaptic WearablesTangible Interaction in EducationContext-Aware ComputingCHI
"Similar-Self" vs. "Alt-Self": How Avatar Customization Impacts Trust Formation in Social VR and Its Transfer to Face-to-Face between Unacquainted IndividualsThis study investigates how avatar customization in virtual reality (VR) impacts trust formation between unacquainted individuals and how such trust transfers to subsequent face-to-face (FtF) meetings. A user study with 48 participants was conducted, where participants were assigned to either a ``Similar-Self'' condition, with avatars resembling their real-world appearance, or an ``Alt-Self'' condition, with creative avatars. The results showed that ``Similar-Self'' avatars led to higher initial integrity-based trust perceptions, though both avatar conditions exhibited similar trust growth during VR encounters. Trust carried over from VR to FtF with a brief recalibration period and ultimately increased beyond VR levels in FtF encounters. This research provides insights into how VR can support the development of trust in early-stage interactions and offers implications for Social VR platforms to better support trustworthy interactions across virtual-physical boundaries.2026SWSirui Wang et al.Southern University of Science and TechnologySocial & Collaborative VRIdentity & Avatars in XREmpathy & Emotional DesignCHI
"Can I Decorate My Teeth With Diamonds?": Exploring Multi-Stakeholder Perspectives on Using VR to Reduce Children's Dental AnxietyDental anxiety is prevalent among children,often leading to missed treatment and potential negative effects on their mental well-being. While several interventions (e.g., pharmacological and psychotherapeutic techniques) have been introduced for anxiety alleviation, the recently emerged virtual reality (VR) technology, with its immersive and playful nature,opened new opportunities for complementing and enhancing the therapeutic effects of existing interventions. In this light, we conducted a series of co-design workshops with 13 children aged 10-12 to explore how they envisioned using VR to address their fear and stress associated with dental visits, followed by interviews with parents (n = 13) and two dentists. Our findings revealed that children expected VR to provide immediate relief, social support, and a sense of control during dental treatment, parents sought educational opportunities for their children to learn about oral health, and dentists prioritized treatment efficiency and safety issues. Drawing from the findings, we discuss the considerations of multi-stakeholders for developing VR-assisted anxiety management applications for children within and beyond dental settings.2025YMYaxuan MAO et al.Perspectives on VRCSCW