Adaptive Bounded-Rationality Modeling of Early-Stage Takeover in Shared-Control DrivingHuman drivers’ control quality in the first seconds after a handover is critical to shared-driving safety; potentially unsafe steering or pedal inputs therefore require detection and correction by the automated vehicle’s safety-fallback system. Yet performance in this window is vulnerable because cognitive states fluctuate rapidly, causing purely rationality-driven, cognition-unaware models to miss early control dynamics. We present an interpretable driver model grounded in bounded rationality with online adaptation that predicts early-stage control quality. We encode boundedness by embedding cognitive constraints in reinforcement learning and adapt latent cognitive parameters in real time via particle filtering from observations of driver actions. In a vehicle-in-the-loop study (n=41), we evaluated predictive performance and physiological validity. The adaptive model not only anticipated hazardous takeovers with higher coverage and longer lead times than non-adaptive baselines but also demonstrated strong alignment between inferred cognitive parameters and real-time eye-tracking metrics. These results confirm that the model captures genuine fluctuations in driver risk perception, enabling timely and cognitively grounded assistance.2026JSJian Sun et al.Tongji UniversityAutomated Driving Interface & Takeover DesignHead-Up Display (HUD) & Advanced Driver Assistance Systems (ADAS)Eye Tracking & Gaze InteractionCHI
Mental Health Impacts of AI Companions: Triangulating Social Media Quasi-Experiments, User Perspectives, and Relational LensAI-powered companion chatbots (AICCs) such as Replika are increasingly popular, offering empathetic interactions, yet their psychosocial impacts remain unclear. We examined how engaging with AICCs shaped wellbeing and how users perceived these experiences. First, we conducted a large-scale quasi-experimental study of longitudinal Reddit data, applying stratified propensity score matching and Difference-in-Differences regression. Findings revealed mixed effects—greater grief expression and interpersonal focus, alongside increases in language about loneliness, depression, and suicidal ideation. Second, we complemented these results with 18 semi-structured interviews, which we thematically analyzed and contextualized using Knapp’s relationship development model. We identified trajectories of initiation, escalation, and bonding, wherein AICCs provided emotional validation and social rehearsal but also carried risks of over-reliance and withdrawal. Triangulating across methods, we offer design implications for AI companions that scaffold healthy boundaries, support mindful engagement, support disclosure without dependency, and surface relationship stages—maximizing psychosocial benefits while mitigating risks.2026YYYunhao Yuan et al.Aalto UniversityAffective Human-Computer DialogueMental Health Apps & Online Support CommunitiesEmpathy & Emotional DesignCHI
HyperMOOC: Augmenting MOOC Videos with Concept-based Embedded VisualizationsMassive Open Online Courses (MOOCs) have become increasingly popular worldwide. However, learners primarily rely on watching videos, easily losing knowledge context and reducing learning effectiveness. We propose HyperMOOC, a novel approach augmenting MOOC videos with concept-based embedded visualizations to help learners maintain knowledge context. Informed by expert interviews and literature review, HyperMOOC employs multi-glyph designs for different knowledge types and multi-stage interactions for deeper understanding. Using a timeline-based radial visualization, learners can grasp cognitive paths of concepts and navigate courses through hyperlink-based interactions. We evaluated HyperMOOC through a user study with 36 MOOC learners and interviews with two instructors. Results demonstrate that HyperMOOC enhances learners' learning effect and efficiency on MOOCs, with participants showing higher satisfaction and improved course understanding compared to traditional video-based learning approaches.2026LYLi Ye et al.Hangzhou Dianzi UniversityInteractive Data VisualizationOnline Learning & MOOC PlatformsIntelligent Tutoring Systems & Learning AnalyticsCHI
"It Seems to Understand My Heart": An Empirical Study of Persona-Driven Persuasive AI Agent for Aging-in-Place in Singapore Persona-based, empathetic approaches can foster sustainable long-term user-agent engagement in aging-in-place contexts. We present PersonaBot, a persona-driven persuasive agent built on a Dual-Persona framework that constructs user personas and generates culturally diverse, gender- and personality-varied agent personas, pairing users with preferred agent personas and adapting them over time. In an eight-week field deployment (8 participants; 1005 participant messages; 2432 agent messages), PersonaBot significantly increased perceived empathy, slowed engagement decline relative to a non-persona baseline, and elicited more elaborative interactions. Effectiveness varied with users’ technological self-efficacy, autonomy preferences, cultural identity, and social patterns, underscoring heterogeneous persona needs. Contrary to our initial assumptions, participants sometimes chose cross-cultural agents for perceived professionalism (over demographic similarity) and favored teacher-like personas balancing authority and warmth. Many framed the agent as a co-pilot rather than a caregiver replacement and engaged selectively, indicating agent personas should respect autonomy and invite—rather than demand—interaction.2026BGBO GAO et al.LILY(Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly)Agent Personality & AnthropomorphismElderly Care & Dementia SupportAging-in-Place Assistance SystemsCHI
Designing Computational Tools for Exploring Causal Relationships in Qualitative DataExploring causal relationships for qualitative data analysis in HCI and social science research enables the understanding of user needs and theory building. However, current computational tools primarily characterize and categorize qualitative data; the few systems that analyze causal relationships either inadequately consider context, lack credibility, or produce overly complex outputs. We first conducted a formative study with 15 participants interested in using computational tools for exploring causal relationships in qualitative data to understand their needs and derive design guidelines. Based on these findings, we designed and implemented QualCausal, a system that extracts and illustrates causal relationships through interactive causal network construction and multi-view visualization. A feedback study (n=15) revealed that participants valued our system for reducing the analytical burden and providing cognitive scaffolding, yet navigated how such systems fit within their established research paradigms, practices, and habits. We discuss broader implications for designing computational tools that support qualitative data analysis.2026HMHan Meng et al.National University of SingaporeComputational Methods in HCIInteractive Data VisualizationTime-Series & Network Graph VisualizationCHI
BAIT: Visual-illusion-inspired Privacy Preservation for Mobile Data VisualizationWith the prevalence of mobile data visualizations, there have been growing concerns about their privacy risks, especially shoulder surfing attacks. Inspired by prior research on visual illusion, we propose BAIT, a novel approach to automatically generate privacy-preserving visualizations by stacking a decoy visualization over a given visualization. It allows visualization owners at proximity to clearly discern the original visualization and makes shoulder surfers at a distance be misled by the decoy visualization, by adjusting different visual channels of a decoy visualization (e.g., shape, position, tilt, size, color and spatial frequency). We explicitly model human perception effect at different viewing distances to optimize the decoy visualization design. Privacy-preserving examples and two in-depth user studies demonstrate the effectiveness of BAIT in both controlled lab study and real-world scenarios.2026SCSizhe Cheng et al.Nanyang Technological UniversityUncertainty VisualizationPrivacy by Design & User ControlPrivacy Perception & Decision-MakingCHI
Hesitation and Tolerance in Recommender SystemsUsers' interactions with recommender systems often involve more than simple acceptance or rejection. We highlight two overlooked states: hesitation, when people deliberate without certainty, and tolerance, when this hesitation escalates into unwanted engagement before ending in disinterest. Across two large-scale surveys (N=6,644 and N=3,864), hesitation was nearly universal, and tolerance emerged as a recurring source of wasted time, frustration, and diminished trust. Analyses of e-commerce and short-video platforms confirm that tolerance behaviors, such as clicking without purchase or shallow viewing, correlate with decreased activity. Finally, an online field study at scale shows that even lightweight strategies treating tolerance as distinct from interest can improve retention while reducing wasted effort. By surfacing hesitation and tolerance as consequential states, this work reframes how recommender systems should interpret feedback, moving beyond clicks and dwell time toward designs that respect user value, reduce hidden costs, and sustain engagement.2026KZKuan Zou et al.Nanyang Technological UniversityRecommender System UXRecommender System InteractionCHI
Motor-Mediated Creativity: Bridging Embodied Skill Training and Digital ExpressionExpressive digital drawing requires nuanced motor control, subtle variations in pressure, velocity, and rhythm that convey affect and style. While experts develop this embodied fluency through years of practice, novices struggle to produce marks that match their intentions, creating a gap between vision and execution. We propose motor-mediated creativity: treating motor training as integral to digital expression. Our system, {\system}, instantiates this through structured practice of expressive primitives, expert-referenced feedback, and ideation prompts that encourage exploration. We report a two-stage investigation. A formative study characterized: (a) novice challenges in motor fluency, (b) examined how different feedback types, including corrective feedback, helped participants understand their mistakes, (c) how prompts, generic or embodied, support engagement with abstract expressive content. A controlled evaluation then linked fluency gains to subjective and expert ratings of expressiveness. Together, our findings show that scaffolding motor skills is a viable strategy for enhancing expressive agency in digital drawing.2026PBPasindu Bolonghege et al.University of MoratuwaHaptic WearablesShape-Changing Interfaces & Soft Robotic MaterialsPrototyping & User TestingCHI
Seeing, Hearing, and Knowing Together: Multimodal Strategies in Deepfake Videos DetectionAs deepfake videos become increasingly difficult for people to recognise, understanding the strategies humans use is key to designing effective media literacy interventions. We conducted a study with 195 participants between the ages of 21 and 40, who judged real and deepfake videos, rated their confidence, and reported the cues they relied on across visual, audio, and knowledge strategies. Participants were more accurate with real videos than with deepfakes and showed lower expected calibration error for real content. Through association rule mining, we identified cue combinations that shaped performance. Visual appearance, vocal, and intuition often co-occurred for successful identifications, which highlights the importance of multimodal approaches in human detection. Our findings show which cues help or hinder detection and suggest directions for designing media literacy tools that guide effective cue use. Building on these insights can help people improve their identification skills and become more resilient to deceptive digital media.2026CCChen Chen et al.Nanyang Technological UniversityDeepfake & Synthetic Media DetectionExplainable AI (XAI)CHI
Outline and Detail: A Semantic-Driven Framework for Layered 2D Character Generation2D cartoon-style digital characters represent an important art form in games, animation, and virtual live streaming. However, traditional 2D character creation workflows involve tedious manual layering, complex skeleton rigging, and professional animation skills, posing challenges for independent studios and non-professional users. While existing AI generation technologies can quickly create visual content, they typically produce non-layered, difficult-to-edit composite images that cannot be integrated into current workflows. This paper presents Spiritus, a semantic-driven 2D character generation framework. Unlike existing text-based AI animation workflows, Spiritus integrates mixed text and sketch inputs, achieving character image generation and automatic component segmentation through an open mask library and semantic matching. We validated the system's effectiveness in character generation freedom, character animation quality, and technical barrier reduction through comparative evaluation of user experiment results. Finally, we explored the possibilities of applying generated characters to various workflows and scenarios, including game development, animation production, and interactive illustrations.2025QSQirui Sun et al.Music Composition & Sound Design ToolsVideo Production & Editing3D Modeling & AnimationUIST
Conversational Explanations: Discussing Explainable AI with Non-AI ExpertsExplainable AI (XAI) aims to provide insights into the decisions made by AI models. To date, most XAI approaches provide only one-time, static explanations, which cannot cater to users' diverse knowledge levels and information needs. Conversational explanations have been proposed as an effective method to customize XAI explanations. However, building conversational explanation systems is hindered by the scarcity of training data. Training with synthetic data faces two main challenges: lack of data diversity and hallucination in the generated data. To alleviate these issues, we introduce a repetition penalty to promote data diversity and exploit a hallucination detector to filter out untruthful synthetic conversation turns. We conducted both automatic and human evaluations on the proposed system, fEw-shot Multi-round ConvErsational Explanation (EMCEE). For automatic evaluation, EMCEE achieves relative improvements of 81.6% in BLEU and 80.5% in ROUGE compared to the baselines. EMCEE also mitigates the degeneration of data quality caused by training on synthetic data. In human evaluations (N=60), EMCEE outperforms baseline models and the control group in improving users' comprehension, acceptance, trust, and collaboration with static explanations by large margins. Through a fine-grained analysis of model responses, we further demonstrate that training on self-generated synthetic data improves the model’s ability to generate more truthful and understandable answers, leading to better user interactions. To the best of our knowledge, this is the first conversational explanation method that can answer free-form user questions following static explanations.2025TZTong Zhang et al.Explainable AI (XAI)IUI
AppAgent: Multimodal Agents as Smartphone UsersRecent advancements in large language models (LLMs) have led to the creation of intelligent agents capable of performing complex tasks. This paper introduces a novel LLM-based multimodal agent framework designed to operate smartphone applications. Our framework allows the agent to mimic human-like interactions such as tapping and swiping through a simplified action space, eliminating the need for system back-end access and enhancing its versatility across various apps. Central to the agent's functionality is an innovative in-context learning method, where it either autonomously explores or learns from human demonstrations, creating a knowledge base used to execute complex tasks across diverse applications. We conducted extensive testing with our agent on over 50 tasks spanning 10 applications, ranging from social media to sophisticated image editing tools. Additionally, a user study confirmed the agent's superior performance and practicality in handling a diverse array of high-level tasks, demonstrating its effectiveness in real-world settings. Our project page is available at \url{https://appagent-official.github.io/}.2025CZChi Zhang et al.Westlake University, School of EngineeringHuman-LLM CollaborationCHI
Bridging Simulation and Reality: Augmented Virtuality for Mass Casualty Triage Training - From Landscape Analysis to Empirical InsightsLive drills are the gold standard for mass casualty incident (MCI) training but are often too resource-intensive for widespread implementation. Immersive technologies offer a promising alternative, but can they deliver comparable fidelity and effectiveness? Working with a local disaster response academy, this paper investigated the potential of Augmented Virtuality (AV) in MCI training through two phases. First, we conducted a landscape analysis of 126 papers across the virtuality continuum, revealing trends in population, training focus, and evaluation metrics. Second, we empirically evaluated an AV system for mass casualty triage training against traditional role-playing and Virtual Reality (VR) approaches, involving 60 trainees in an operational curriculum. Results indicated that both AV and VR surpassed traditional simulations, with AV's tactile integration significantly enhancing physical engagement, satisfaction, and triage accuracy. Through the lens of triage, we discussed the broader practical implications of integrating immersive technologies like AV into real-world MCI education.2025YCYang Chen et al.National University of Singapore, College of Design and EngineeringSocial & Collaborative VRVR Medical Training & RehabilitationCHI
Exploring the Design of Human Speech Indicators to Enhance Waiting Experience in Voice User InterfaceWaiting for system loading is a common scenario that often diminishes user experience, leading to dissatisfaction. Well-established visual indicators like progress bars can not directly apply to the interactions with voice assistants (VAs) like Siri. As VAs continue to rise in popularity, this research aims to explore the design of auditory indicators, particularly human speech, for optimizing waiting experiences in Voice User Interfaces (VUIs). We first organized focus groups (N=35) to identify design considerations for speech indicators, uncovering design opportunities in integrating explanations and humor. Subsequently, we conducted an empirical study (N=30) to evaluate the effects of speech indicators with two levels of explanation and humor on the waiting experience, measured by attention, perceived time, pleasure, and overall satisfaction, during both short and long loading durations. Our findings suggest significant potential for incorporating explanations and humor into VUIs, offering actionable insights for designing effective speech indicators that improve waiting experiences.2025WLWenan Li et al.Information Hub, The Hong Kong University of Science and Technology (GuangZhou)Voice User Interface (VUI) DesignAgent Personality & AnthropomorphismCHI
Enhancing UX Evaluation Through Collaboration with Conversational AI Assistants: Effects of Proactive Dialogue and TimingUsability testing is vital for enhancing the user experience (UX) of interactive systems. However, analyzing test videos is complex and resource-intensive. Recent AI advancements have spurred exploration into human-AI collaboration for UX analysis, particularly through natural language. Unlike user-initiated dialogue, our study investigated the potential of proactive conversational assistants to aid UX evaluators through automatic suggestions at three distinct times: before, in sync with, and after potential usability problems. We conducted a hybrid Wizard-of-Oz study involving 24 UX evaluators, using ChatGPT to generate automatic problem suggestions and a human actor to respond to impromptu questions. While timing did not significantly impact analytic performance, suggestions appearing after potential problems were preferred, enhancing trust and efficiency. Participants found the automatic suggestions useful, but they collectively identified more than twice as many problems, underscoring the irreplaceable role of human expertise. Our findings also offer insights into future human-AI collaborative tools for UX evaluation.2024EKEmily Kuang et al.Rochester Institute of TechnologyHuman-LLM CollaborationPrototyping & User TestingComputational Methods in HCICHI
SoK: An Exhaustive Taxonomy of Display Issues for Mobile ApplicationsDisplay issues, often arising from design inconsistencies or software problems, can have a significant impact on both user experience and system functionality. This study focuses on three primary challenges in the field of display issues: the absence of a standardized classification system, the limitations of existing detection tools, and the inadequacy of available data. To systematically address these challenges, we introduce a Comprehensive Display Issue Analysis Framework (DIS). Utilizing this framework, we construct a comprehensive and industry-validated taxonomy for display issues. When evaluating the capabilities of existing detection tools and the completeness of available data against this taxonomy, we find that current mainstream tools can identify only 77\% of the cataloged display issues. This finding suggests that, although the field has received some attention from the industry, there is still room for further improvement and research. This study not only deepens our understanding of the classification of display issues and the capabilities of detection tools, but also provides valuable insights for future research and applications in this domain.2024LNLiming Nie et al.Interactive Data VisualizationPrototyping & User TestingIUI
AI and Disaster Risk: A Practitioner PerspectiveEmerging techniques developed by AI researchers promise to offer the capacity to support disaster risk management (DRM), through making data collection or analysis practices faster, less costly, or more accurate. However, in every socially consequential domain in which AI tools have been applied, these technologies have been demonstrated to have some degree of negative consequences. This paper explores an attempt to convene technical experts in the area of DRM to discuss potential negative impacts, their approaches toward mitigating these impacts as well as identifying some of the overarching challenges. In doing so, we contribute new findings about a domain that has received relatively little attention from critical and ethical AI researchers, and the opportunities and limitations that are presented by working with domain experts to evaluate the social consequences of emerging technologies.2022AMAparna Moitra et al.Disaster Response; Disaster ResponseCSCW
Becoming Interdisciplinary: Fostering Critical Engagement With Disaster DataInformation systems such as mapping platforms, algorithms, and databases are a central component of how society responds to the threats posed by disasters. However, these systems has come under increasing criticism in recent years for prioritizing technical disciplines over insights from the humanities and social science and failing to adequately incorporate the perspectives of at-risk or affected communities. This paper describes a unique month-long workshop that convened interdisciplinary experts to collaborate on a projects related to flood data. In addition to findings about the practical accomplishment of interdisciplinary collaboration, we offer three interrelated contributions. First, we position interdisciplinarity as a critical practice and offer a detailed example of how we staged this process. We then discuss the benefits to interdisciplinarity of expanding the range of temporal logics normally deployed in design workshops. Finally, we reflect on approaches to evaluating the event’s contributions toward sustained critique and reform of expert practice.2021RSDavid Lallemant et al.Data Work Across Contexts and DisciplinesCSCW
Real Differences between OT and CRDT in Correctness and Complexity for Consistency Maintenance in Co-EditorsOT (Operational Transformation) was invented for supporting real-time co-editors in the late 1980s and has evolved to become core techniques widely used in today’s working co-editors and adopted in industrial products. CRDT (Commutative Replicated Data Type) for co-editors was first proposed around 2006, under the name of WOOT (WithOut Operational Transformation). Follow-up CRDT variations are commonly labeled as "post-OT" techniques capable of making concurrent operations natively commutative in co-editors. On top of that, CRDT solutions have made broad claims of superiority over OT solutions, and often portrayed OT as an incorrect and inefficient technique. Over one decade later, however, CRDT is rarely found in working co-editors; OT remains the choice for building the vast majority of today’s co-editors. Contradictions between the reality and CRDT’s purported advantages have been the source of much confusion and debate in co-editing researcher and developer communities. To seek truth from facts, we set out to conduct a comprehensive and critical review on representative OT and CRDT solutions and working co-editors based on them. From this work, we have made important discoveries about OT and CRDT, and revealed facts and evidences that refute CRDT claims over OT on all accounts. These discoveries help explain the underlying reasons for the choice between OT and CRDT in the real world. We report these results in a series of three articles. In this article (the second in the series), we reveal the differences between OT and CRDT in their basic approaches to realizing the same general transformation and how such differences had resulted in different technical challenges and consequential correctness and complexity issues. Moreover, we reveal hidden complexity and algorithmic flaws with representative CRDT solutions, and discuss common myths and facts related to correctness and complexity of OT and CRDT. We hope the discoveries from this work help clear up common myths and confusions surrounding OT and CRDT, and accelerate progress in co-editing technology for real world applications.2020DSDavid Q. Sun et al.Collaboration: Creating and Writing TogetherCSCW
The Disaster and Climate Change Artathon: Staging Art/Science Collaborations in Crisis InformaticsInformation systems increasingly shape our knowledge of crises such as disasters and climate change. While these tools improve our capacity to understand, prepare for, and mitigate such challenges, critical questions are being raised about how their design shapes public imagination of these problems and delimits potential solutions. Prior work in human-computer interaction (HCI) has pointed to art/science collaboration as one approach for helping to explore such questions. As an attempt to draw on this potential, our team designed and facilitated a 2-day “artathon” that brought together artists and scientists to create new works of art based on disaster and climate data. Reflecting on the artathon and its outcomes, we contribute two sets of findings. First, we articulate opportunities, suggested by the artwork, for expanding research and design in crisis informatics. Second, we offer suggestions for HCI researchers seeking to stage successful art/science collaborations or similar inter-disciplinary events.2020RSRobert Soden et al.Sustainable HCIClimate Change Communication ToolsHuman-Nature Relationships (More-than-Human Design)DIS