From Text to Movement: LLM-driven Swarm User Interfaces for Embodied and Interactive StorytellingThis paper introduces PuppetLine, an interactive storytelling system that translates natural language narratives into coordinated performances using tabletop robots. The system combines large language models with a constrained set of action and emotion primitives to generate physically executable and interpretable multi-robot motions. We describe the system design and report findings from a designer-centered user study examining how people interpret and reflect on robot enactments. The results provide formative insights into mapping narrative intent to embodied interaction and inform the design of narrative Swarm User Interfaces.2026RWRuhan Wang et al.Tsinghua University3D Modeling & AnimationInteractive Narrative & Immersive StorytellingHuman-Robot Collaboration (HRC)IUI
DuoMorph: Synergistic Integration of FDM Printing and Pneumatic Actuation for Shape-Changing InterfacesWe introduce DuoMorph, a design and fabrication method that synergistically integrates Fused Deposition Modeling(FDM) printing and pneumatic actuation to create novel shape-changing interfaces. In DuoMorph, the printed structures and heat-sealed pneumatic elements are mutually designed to actuate and constrain each other, enabling functions that are difficult for either component to achieve in isolation. Moreover, the entire hybrid structure can be fabricated through a single, seamless process using only a standard FDM printer—including both heat-sealing and 3D/4D printing. In this paper, we define a design space including four primitive categories that capture the fundamental ways in which printed and pneumatic components can interact. To support this process, we present a fabrication method and an accompanying design tool. Finally, we demonstrate the potential of DuoMorph through example applications and performance demonstrations.2026XLXueqing Li et al.Tsinghua universityShape-Changing Interfaces & Soft Robotic MaterialsShape-Changing Materials & 4D PrintingCHI
OpenCD: Empowering Diagnosis of Children's Mathematical Cognition through Open-ended Multimodal TasksAssessing children’s cognitive development in early mathematics is vital for effective teaching. Compared to closed-ended questions, which may fail to capture nuanced developmental spectrum, open-ended elicitation tasks (e.g., asking students to manipulate objects or draw to represent numbers) serve as a promising approach to reveal deeper cognitive processes. However, their diverse and unstructured nature makes systematic analysis challenging for teachers. We present OpenCD, a teacher-facing system that automatically analyzes multimodal student responses to capture individualized insights. Based on Evidence-Centered Design, it combines Vision-Language Models (VLMs) and expert models to generate interactive diagnostic graphs and reports with traceability back to behavioral evidence. In our two-part evaluation, a validation study found 90.3% of the system’s diagnoses “completely reasonable,” and a user study showed that OpenCD reduced teachers’ analysis burden and enhanced their insights into student thinking. Our work contributes to scalable process-based assessment for mathematical literacy.2026ZZZhi Zheng et al.Tsinghua UniversityIntelligent Tutoring Systems & Learning AnalyticsProgramming Education & Computational ThinkingUser Research Methods (Interviews, Surveys, Observation)CHI
PrivWeb: Unobtrusive and Content-aware Privacy Protection For Web AgentsWhile 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.2026SZShuning Zhang et al.Tsinghua UniversityPrivacy by Design & User ControlPrivacy Perception & Decision-MakingHuman-LLM CollaborationCHI
HiSync: Spatio-Temporally Aligning Hand Motion from Wearable IMU and On-Robot Camera for Command Source Identification in Long-Range HRILong-range Human-Robot Interaction (HRI) remains underexplored. Within it, Command Source Identification (CSI) – determining who issued a command – is especially challenging due to multi-user and distance-induced sensor ambiguity. We introduce HiSync, an optical-inertial fusion framework that treats hand motion as binding cues by aligning robot-mounted camera optical flow with hand-worn IMU signals. We first elicit a user-defined (N=12) gesture set and collect a multimodal command gesture dataset (N=38) in long-range multi-user HRI scenarios. Next, HiSync extracts frequency-domain hand motion features from both camera and IMU data, and a learned CSINet denoises IMU readings, temporally aligns modalities, and performs distance-aware multi-window fusion to compute cross-modal similarity of subtle, natural gestures, enabling robust CSI. In three-person scenes up to 34m, HiSync achieves 92.32% CSI accuracy, outperforming the prior SOTA by 48.44%. HiSync is also validated on real-robot deployment. By making CSI reliable and natural, HiSync provides a practical primitive and design guidance for public-space HRI.2026CZChengwen Zhang et al.Tsinghua UniversityTeleoperation & TelepresenceHuman Pose & Activity RecognitionHand Gesture RecognitionCHI
ActivitySeeker: Towards Collaborative Personalized Human Activity Discovery and Recognition on SmartphonesSmartphones provide an attractive yet challenging platform for human activity recognition (HAR). They are ubiquitous, but also limit the input of HAR systems to a single IMU. These systems are also challenged by the inherent diversity of human activities and varying phone placement on the user's body. This results in traditional smartphone HAR systems having limited personalization potential or imposing a high user burden. We propose ActivitySeeker, a personalized smartphone HAR system that combines self-supervised activity discovery and low-burden user interaction to collaboratively label IMU data and adapt HAR models to individual users on-device through transfer learning. We evaluated ActivitySeeker through simulated online learning and in-the-wild user experiments, where it discovered 95.5% of personal activity types and achieved high recognition accuracy (93.3%) while maintaining a positive user experience. Leveraging the synergy between user and smartphone, ActivitySeeker opens up new possibilities for HAR-based applications like fitness, health and personalized recommendation.2026ZYZhoutong Ye et al.Tsinghua UniversityHuman Pose & Activity RecognitionFitness Tracking & Physical Activity MonitoringBehavior Change & Reflection TechnologyCHI
DancingBox: A Lightweight MoCap System for Character Animation from Physical ProxiesCreating compelling 3D character animations typically requires either expert use of professional software or expensive motion capture systems operated by skilled actors. We present DancingBox, a lightweight, vision-based system that makes motion capture accessible to novices by reimagining the process as digital puppetry. Instead of tracking precise human motions, DancingBox captures the approximate movements of everyday objects manipulated by users with a single webcam. These coarse proxy motions are then refined into realistic character animations by conditioning a generative motion model on bounding-box representations, enriched with human motion priors learned from large-scale datasets. To overcome the lack of paired proxy–animation data, we synthesize training pairs by converting existing motion capture sequences into proxy representations. A user study demonstrates that DancingBox enables intuitive and creative character animation using diverse proxies, from plush toys to bananas, lowering the barrier to entry for novice animators.2026HYHaocheng Yuan et al.University of Edinburgh3D Modeling & AnimationTangible Programming & Physical ComputingCHI
TraceRing: Touchpad-like Pointing with a Single IMU Ring through Personalized LearningAchieving touchpad-like pointing with a single IMU ring is highly desirable for portable and wearable interaction, yet challenging due to incomplete motion data and significant user variability. We present TraceRing, a finger-worn IMU system that enables precise two-dimensional cursor control. To address the limitations of generic end-to-end models, we propose a personalized training framework that learns user-specific representations through joint multi-task and contrastive learning, while dynamically selecting the most suitable expert model. This approach enables personalization without requiring per-user fine-tuning, and reduces velocity prediction error by 33.9% over state-of-the-art baselines. Furthermore, a real-time study shows it delivers speed and accuracy far exceeding those of AirMouse (2.26s v.s. 3.01s in average task completion time). These results demonstrate TraceRing as a portable and comfortable alternative for mobile computing and AR interaction applications.2026ZHZhe He et al.Tsinghua UniversityHaptic WearablesHand Gesture RecognitionMobile Augmented RealityCHI
Audience in the Loop: Viewer Feedback-Driven Content Creation in Micro-drama Production on Social MediaThe popularization of social media has led to increasing consumption of narrative content in byte-sized formats. Such micro-dramas contain fast-pace action and emotional cliffs, particularly attractive to emerging Chinese markets in platforms like Douyin and Kuaishou. Content writers for micro-dramas must adapt to fast-pace, audience-directed workflows, but previous research has focused instead on examining writers’ experiences of platform affordances or their perceptions of platform bias, rather than the step-by-step processes through which they actually write and iterative content. In 28 semi-structured interviews with scriptwriters and writers specialized in micro-dramas, we found that the short-turn-around workflow leads to writers taking on multiple roles simultaneously, iteratively adapting to storylines in response to real-time audience feedback in the form of comments, reposts, and memes. We identified unique narrative styles such as AI-generated micro-dramas and audience-responsive micro-dramas. This work reveals audience interaction as a new paradigm for collaborative creative processes on social media.2026GCGengchen Cao et al.Tsinghua - Anta Joint Research CenterCreative Collaboration & Feedback SystemsSocial Platform Design & User BehaviorLive Streaming & Content CreatorsCHI
Division of Labor and Collaboration Between Parents in Family Education: The Case of Homework Involvement in Chinese FamiliesHomework tutoring work is a demanding and often conflict-prone practice in family life, and parents often lack targeted support for managing its cognitive and emotional burdens. Through interviews with 18 parents of children in grades 1–3, we examine how homework-related labor is divided and coordinated between parents, and where AI might meaningfully intervene. We found three key insights: (1) Homework labor encompasses distinct dimensions: physical, cognitive, and emotional, with the latter two often remaining invisible. (2) We identified father-mother-child triadic dynamics in labor division, with children’s feedback as the primary factor shaping parental labor adjustments. (3) Building on prior HCI research, we propose an AI design that prioritizes relationship maintenance over task automation or broad labor mitigation. By employing labor as a lens that integrates care work, we explore the complexities of labor within family contexts, contributing to feminist and care-oriented HCI and to the development of context-sensitive coparenting practices.2026ZWZiyi Wang et al.Beijing University of Civil Engineering and ArchitectureParticipatory DesignInclusive DesignEmpowerment of Marginalized GroupsCHI
AI-generated AR Reassembly Guidance from Disassembly Videos to Scaffold Everyday RepairRepair is a valuable yet challenging activity, especially when product manuals are missing or outdated. Augmented Reality (AR) has been widely explored for repair tasks, but most systems rely on CAD models or pre-constructed assets, which escalate authoring costs and constrain scalability. We introduce RePairAR, a system that leverages multimodal large language models (MLLMs) to generate interactive AR reassembly guidance derived directly from user-recorded egocentric disassembly videos. RePairAR deduces step-part-relation structures, reverses these for reassembly planning, and delivers the guidance through mixed-media AR visualizations. In a user study with repair novices, RePairAR significantly reduced perceived temporal demand compared to traditional how-to videos. Both media improved self-efficacy, with RePairAR providing greater gains. Follow-up interviews revealed the mechanisms behind these effects. We contribute a validated MLLM-driven pipeline and highlight design implications for scalable, situated support in everyday repair practices.2026WDWenjing Deng et al.Tsinghua UniversityAR Navigation & Context AwarenessMixed Reality WorkspacesHuman-LLM CollaborationCHI
SituFont: A Just-in-Time Adaptive Intervention Interface for Enhancing Mobile Readability in Situational Visual ImpairmentsSituational visual impairments (SVIs) hinder mobile readability, causing discomfort and limiting information access. Building on prior work in adaptive typography and accessibility, this paper presents SituFont, a context-aware and human-in-the-loop adaptive typography adjustment approach that enhances smartphone mobile readability by dynamically adjusting font parameters based on real-time contextual changes. Using smartphone sensors and a human-in-the-loop approach, SituFont personalizes text presentation to accommodate personal factors (e.g., fatigue, distraction) and environmental conditions (e.g., lighting, motion, location). To inform its design, we conducted formative interviews (N=15) to identify key SVI factors and controlled experiments (N=18) to quantify their impact on optimal text parameters. A comparative user study (N=12) across eight simulated SVI scenarios demonstrated SituFont's effectiveness in improving smartphone mobile readability in terms of improved efficiency and reduced workload compared with a non-trivial manual adjustment baseline.2026JCJingruo Chen et al.Cornell UniversityMobile Accessibility DesignBehavior Change & Reflection TechnologyContext-Aware ComputingCHI
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
GestuProp: 3D Virtual Reality Prop Generation with Co-Speech GesturesVirtual Reality (VR) has been widely adopted in domains such as gaming, education, and healthcare, where 3D props play a central role in enabling immersive interaction. With the advancement of generative AI, 3D props can now be created rapidly; however, little research has explored how gestures and speech can be integrated to support prop generation. To address this gap, we introduce GestuProp, a VR prop generation system driven by co-speech gestures. Building on a formative study with 30 participants, we proposed a gesture design space and developed the VR system GestuProp. We then conducted a user study with 14 participants, which showed that GestuProp demonstrates good usability and favorable user experiences, while also revealing how object categories influence gesture use and interaction. These findings highlight the potential of gesture–speech synergy to advance prop generation in VR.2026ZYZhihao Yao et al.Tsinghua UniversitySocial & Collaborative VR3D Modeling & AnimationHand Gesture RecognitionCHI
Does Personalized Nudging Wear Off? A Longitudinal Study of AI Self-Modeling for Behavioral EngagementSustaining the effectiveness of behavior change technologies remains a key challenge. AI self-modeling, which generates personalized portrayals of one’s ideal self, has shown promise for motivating behavior change, yet prior work largely examines short-term effects. We present one of the first longitudinal evaluations of AI self-modeling in fitness engagement through a two-stage empirical study. A 1-week, three-arm experiment (visual self-modeling (VSM), auditory self-modeling (ASM), Control; N=28) revealed that VSM drove initial performance gains, while ASM showed no significant effects. A subsequent 4-week study (VSM vs. Control; N=31) demonstrated that VSM sustained higher performance levels but exhibited diminishing improvement rates after two weeks. Interviews uncovered a catalyst effect that fostered early motivation through clear, attainable goals, followed by habituation and internalization which stabilized performance. These findings highlight the temporal dynamics of personalized nudging and inform the design of behavior change technologies for long-term engagement.2026QHQing He et al.University of PennsylvaniaBehavior Change & Reflection TechnologyHealth Self-TrackingEmotion-Sensing WearablesCHI
Caring about Care: A Meta-Narrative Review of HCI Research on CareThe number of HCI papers on care has grown rapidly in recent years. Despite growing interest in care both as an application domain for technology and as an ethical stance in research and design, our integrated understanding of the concept is limited. It remains unclear how various application areas of care relate to one another, to what extent their underlying assumptions align or contradict, and how they collectively shape HCI discourse on care. To address this, we present a meta-narrative review of 317 SIGCHI papers on care. We first outline the landscape of care in HCI. We then present six paradigmatic framings of care, and a conceptual map that positions these framings in relation to each other, their representative care–tech relations, and the temporal development of the field. We conclude by discussing the implications from the review, as well as gaps in the field and future directions.2026ZWZixuan Wang et al.University of EdinburghMental Health Apps & Online Support CommunitiesEmpowerment of Marginalized GroupsTechnology Ethics & Critical HCICHI
AI for Creativity: A GenAI-Based Approach for Early Concept Design and Its Impact on Senior ArchitectsSenior architects are pivotal in shaping architectural projects, yet integrating Generative AI (GenAI) into their workflows presents notable challenges. A formative study (N=11) identified key pain points in their early concept design process. To address these, we developed EarlyArchi, a GenAI-driven system supporting automated concept generation and evaluation. In a within-subject study (N=13), participants used EarlyArchi for early-stage design tasks. Results showed enhanced perceived creativity, improved design competency, and more efficient ideation. However, concerns emerged regarding controllability and domain-specific accuracy, highlighting the need for features that preserve professional autonomy and trust. Further analysis revealed three GenAI involvement modes—fully AI-driven, GenAI-led, and human-led—emphasizing the importance of adaptive role allocation in balancing creative exploration with expert leadership. These findings offer insights into supporting senior architects through GenAI while identifying key considerations for designing future human–AI co-creation systems.2026JLJiajuan LI et al.The Hong Kong Polytechnic UniversityGenerative AI (Text, Image, Music, Video)Human-LLM CollaborationAI-Assisted Decision-Making & AutomationCHI
From Human Pragmatic Language Skills to Conversational Agent Design: A Systematic Review of Transfer StrategiesWhile conversational agents’ (CAs) semantic and syntactic capabilities have advanced, their pragmatic skills, using language appropriately in context, have emerged as a critical focus in practical applications. Hence, scholars integrate conversational skills derived from human-human interaction into CA designs. However, existing research mainly adopts an empirical approach and focuses on specific CA deployment, making it challenging to identify overarching patterns or develop a comprehensive methodology for transferring human pragmatic skills to CA design. Thus, we conducted a systematic review of 85 studies from primary databases (e.g., ACM, IEEE, etc.), focusing on designing CAs with human-derived conversational skills. We identified skill categories (verbal, paralinguistic, nonverbal), transfer strategies (from dialog data, theories, and via co-design), implementations, and evaluation metrics. We consolidated these insights into a four-stage design process: human skill exploration, definition, transfer, and iterative evaluation. Future research can leverage this to design CAs that achieve conversational goals through contextually appropriate language use.2026JHJiaxiong Hu et al.The Hong Kong University of Science and TechnologyAgent Personality & AnthropomorphismConversational ChatbotsUser Research Methods (Interviews, Surveys, Observation)CHI
VisGuardian: A Lightweight Group-based Visual Privacy Control Technique For Smart Glasses in Home EnvironmentsAlways-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.2026SZShuning Zhang et al.Tsinghua UniversitySmart Home Privacy & SecurityPrivacy by Design & User ControlAR Navigation & Context AwarenessCHI
BuyMate: Making AI Interventions Effective in Promoting Rational Consumption in Live CommerceLive 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.2026SWShiyi Wang et al.Tsinghua universityAI-Assisted Decision-Making & AutomationAI Ethics, Fairness & AccountabilityRecommender System UXCHI