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
DensityBars: A Space-Efficient Visualization for Event Temporal DistributionEvent temporal distribution analysis aims to capture both global (e.g., rises and peaks) and local patterns (e.g., frequent occurrences and sudden absences). Traditional charts typically rely on adjusting binning granularities to reveal such patterns. However, this strategy forces a trade-off between global clarity and local detail and may require considerably more screen space as the number of bins increases, which limits its applicability in space-constrained visual interface design. In this paper, we propose DensityBars, a space-efficient visualization that embeds fine-grained density heatmaps of event occurrences into the coarse-grained bar chart to convey both global and local patterns simultaneously. Two real-world use cases and two formal user studies demonstrate its effectiveness and usability. Insights from studies provide valuable implications for the visual design of temporal distribution visualizations.2026MLMingwei Lin et al.South China University of TechnologyInteractive Data VisualizationTime-Series & Network Graph VisualizationUncertainty VisualizationCHI
Open-ended Structured Question Assessment with Human-LLM CollaborationOpen-ended Structured Questions (OSQs) assess not only students’ knowledge but also their reasoning and expression. However, grading OSQ requires fine-grained, scoring point–level analysis, which is labor-intensive and difficult to scale. Although recent LLM-based and human–AI collaborative grading systems improve efficiency, they mainly operate at the whole-response level and lack support for point-level inspection, correction, and feedback integration. We present VeriGrader, a novel human–AI collaborative system for OSQ grading. It combines chain-of-thought prompting with scoring point– and response-level in-context learning to enable interpretable LLM grading and iterative refinement from instructor feedback. A coordinated multi-view interface supports efficient verification of response segments, matched scoring points, and rationales. We evaluate VeriGrader using real course data and a user study with 12 participants. Results show that VeriGrader improves both grading efficiency, accuracy, and consistency over the baselines, demonstrating the effectiveness of VeriGrader and promoting human–AI collaboration in educational assessment.2026FLFengyan Lin et al.South China University of TechnologyHuman-LLM CollaborationIntelligent Tutoring Systems & Learning AnalyticsParticipatory DesignCHI
TSEditor: Interactive Time Series Editing for Privacy PreservationPublishing time series datasets raises substantial privacy concerns, as the underlying patterns (e.g., trends, values) can lead to the disclosure of individual identification. Mitigating these concerns remains challenging due to difficulties in pinpointing specific privacy-leaking patterns and protecting them without significantly compromising the analytical utility of the published data. Existing methods remain vulnerable to identity attacks utilizing diverse temporal patterns and may compromise data utility for subsequent analytical tasks. To address these limitations, we collaborated with domain experts to summarize a taxonomy of privacy risks in time series data and developed TSEditor, an interactive editing system. TSEditor integrates coordinated views for multi-perspective analysis of privacy risks and introduces six editing operations for targeted modifications, providing visual feedback. We demonstrate the effectiveness and usability of TSEditor through two case studies, an expert interview, a model evaluation, and a user study.2026ZXZihan Xu et al.Zhejiang UniversityPrivacy Perception & Decision-MakingInteractive Data VisualizationExplainable AI (XAI)CHI
Does Sycophancy Change Decisions? Effect of LLM Sycophancy on AI-Assisted Decision-MakingLarge language models are increasingly integrated into everyday and professional decision making, yet often exhibit sycophantic behavior by aligning with users’ views or preferences. While sycophancy can enhance interaction, its influence on users' decisions remain unclear given different styles and task risks. We examine three forms of sycophancy—opinion agreement, direct praise, and self-deprecation—in two contrasting contexts: a low-risk speed-dating prediction task and a high-risk ETF investment task. In a 4×2 mixed-design online study (\textit{N} = 106), we compare non-sycophantic AI with sycophantic variants on decision outcomes and confidence changes. Results show that sycophancy influences decision patterns in type-dependent ways. Specifically, opinion agreement reinforces initial decisions and self-deprecation boosts confidence. Interviews further indicate that users value supportive AI but question its objectivity when praise becomes excessive. These findings reveal the multifaceted effects of AI sycophancy and offer design implications for balancing support and credibility in human–AI interaction.2026ZLZejian Li et al.Zhejiang UniversityAI-Assisted Decision-Making & AutomationAI Ethics, Fairness & AccountabilityHuman-LLM CollaborationCHI
Toward Equitable ASL Education: Egocentric Stereo Sensing with LLM Feedback for Error-Aware LearningAmerican Sign Language (ASL) is the primary language of many Deaf and Hard of Hearing (DHH) individuals. However, existing learning resources often lack timely, individualized feedback, leaving learners uncertain about signing accuracy. We introduce a novel egocentric ASL learning system that integrates stereo vision, error detection across four manual ASL parameters (handshape, orientation, location, movement), and large language model (LLM)–driven natural language feedback. To our knowledge, this is the first system to deliver error-aware, pedagogically grounded feedback for ASL learners. A formative study with 15 ASL teachers and 30 learners (both Deaf and hearing backgrounds) supports the motivation and design goals, while a system evaluation with 13 Deaf ASL participants (novice to advanced) practicing 230 signs provides initial evidence of system feasibility and short-term, pedagogically promising behavior within the primary user community. Across two complementary studies, we identify key design principles: prioritizing reliability over sensitivity, stratifying feedback by error severity, and leveraging egocentric alignment for natural practice. Collectively, these contributions establish a foundation for scalable ASL education and provide generalizable insights for designing AI-mediated feedback in Human-Computer Interaction (HCI).2026YCYongxiang Cai et al.Binghamton UniversityDeaf & Hard-of-Hearing Support (Captions, Sign Language, Vibration)Motor Impairment Assistive Input TechnologiesVoice AccessibilityCHI
Wrist2Finger: Sensing Fingertip Force for Force-Aware Hand Interaction with a Ring-Watch WearableHand pose tracking is essential for advancing applications in human-computer interaction. Current approaches, such as vision-based systems and wearable devices, face limitations in portability, usability, and practicality. We present a novel wearable system that reconstructs 3D hand pose and estimates per-finger forces using a minimal ring-watch sensor setup. A ring worn on the finger integrates an inertial measurement unit (IMU) to capture finger motion, while a smartwatch-based single-channel electromyography (EMG) sensor on the wrist detects muscle activations. By leveraging the complementary strengths of motion sensing and muscle signals, our approach achieves accurate hand pose tracking and grip force estimation in a compact wearable form factor. We develop a dual-branch transformer network that fuses IMU and EMG data with cross-modal attention to predict finger joint positions and forces simultaneously. A custom loss function imposes kinematic constraints for smooth force variation and realistic force saturation. Evaluation with 20 participants performing daily object interaction gestures demonstrates an average Mean Per Joint Position Error (MPJPE) of 0.57 cm and a fingertip force estimation (RMSE: 0.213 N, r=0.76). We showcase our system in a real-time Unity application, enabling virtual hand interactions that respond to user-applied forces. This minimal, force-aware tracking system has broad implications for VR/AR, assistive prosthetics, and ergonomic monitoring.2025YXYingjing Xiao et al.Force Feedback & Pseudo-Haptic WeightHand Gesture RecognitionUIST
RidgeBuilder: Interactive Authoring of Expressive Ridgeline PlotsRidgeline plots are frequently employed to visualize the evolution or distributions of multiple series with a pile of overlapping line, area, or bar charts, highlighting the peak patterns. While traditionally viewed as small multiple visualizations, their ridge-like patterns have increasingly attracted graphic designers to create appealing customized ridgeline plots. However, many tools only support creating basic ridgeline plots and overlook their diverse layouts and styles. This paper introduces a comprehensive design space for ridgeline plots, focusing on their varied layouts and expressive styles. We present RidgeBuilder, an intuitive tool for creating expressive ridgeline plots with customizable layouts and styles. In particular, we summarize three goals for refining the layout of ridgeline plots and propose an optimization method. We assess RidgeBuilder's usability and usefulness through a reproduction study and evaluate the layout optimization algorithm through anonymized questionnaires. The effectiveness is demonstrated with a gallery of ridgeline plots created by RidgeBuilder.2025SLShuhan Liu et al.State Key Lab of CAD & CG, Zhejiang UniversityInteractive Data VisualizationData StorytellingCHI
Get Your Hands Dirty? A Comparative Study of Tool Usage and Perceptual Engagement in Physical and Digital SculptingThe creation of 3D content, crucial in various applications, is often challenging and time-intensive. While digital tools are prevalent for 3D content creation, traditional clay sculpting offers an embodied experience that fosters artists' perceptual engagement with physical space, enhancing their interactive and cognitive connection with the creation process. We conducted an eight-day live sculpting session at an art academy, systematically comparing the creative workflows of eight professional artists in both physical and digital mediums. Our qualitative and quantitative analysis include artists' differences in tool usage between physical and digital sculpting, variations in visual and tactile perceptual engagement, and the potential for future integration of the two modalities. Our study provides insights into the benefits of physical and digital sculpting and may inform future design of hybrid interfaces for 3D content creation.2024XMXinyu Ma et al.Shape-Changing Interfaces & Soft Robotic Materials3D Modeling & AnimationC&C
VAID: Indexing View Designs in Visual Analytics SystemVisual analytics (VA) systems have been widely used in various application domains. However, VA systems are complex in design, which imposes a serious problem: although the academic community constantly designs and implements new designs, the designs are difficult to query, understand, and refer to by subsequent designers. To mark a major step forward in tackling this problem, we index VA designs in an expressive and accessible way, transforming the designs into a structured format. We first conducted a workshop study with VA designers to learn user requirements for understanding and retrieving professional designs in VA systems. Thereafter, we came up with an index structure VAID to describe advanced and composited visualization designs with comprehensive labels about their analytical tasks and visual designs. The usefulness of VAID was validated through user studies. Our work opens new perspectives for enhancing the accessibility and reusability of professional visualization designs.2024LYLu Ying et al.Zhejiang UniversityInteractive Data VisualizationVisualization Perception & CognitionCHI
RingVKB: A Ring-Shaped Virtual Keyboard Using Low-Cost IMUWearable devices have been important components for ubiquitous computing. However, text input remains challenging on wearables due to the lack of a physical keyboard. In this paper, we propose a novel ring-shaped virtual keyboard system named RingVKB for convenient text input using low-cost IMUs available on any wearables. At the core of RingVKB are two novel designs: 1) A circular keyboard layout with 12 equal sectors, which assembles all common keys on classical keyboards while allowing users to type with only one finger effectively, and 2) an error control algorithm that calculates the relative displacement of keystrokes from the noisy IMU sensor data. The two components, coupled together, enable high-accuracy and efficient text input for ubiquitous scenarios. We implement RingVKB using a small device consisting of a microcontroller and a MEMS sensor, which can be attached to the user's index finger. Experimental results show that RingVKB can effectively improve the relative displacement estimation accuracy, and achieves an overall keystroke recognition accuracy of 93% for 25 key positions. A user study also shows that RingVKB is easy to learn and use. Using only low-cost IMU sensors, RingVKB provides a virtual keyboard solution that can be widely adopted on wearables.2023ZLZhenjiang Li et al.Haptic WearablesFoot & Wrist InteractionUbiquitous ComputingMobileHCI