Vistoryteller: Designing Data Stories with LLM Agent-Based Generation and Interactive User ControlData stories that combine data, visualizations, and prose are widely used for communication, decision making, and persuasion, but producing them typically requires coordinated effort across specialized roles such as analysts, scripters, and designers, which is time consuming and difficult to manage. Existing AI-assisted methods generally treat storytelling as a single-agent task and offer only coarse, global controls, limiting an author’s ability to preserve and shape their communication intention over the course of a narrative. In this work, we present Vistoryteller, a multi-agent authoring system that models the division of labor found in human teams by assigning specialized large language model agents to complementary roles and orchestrating their interactions to generate cohesive, intention-aligned data stories. Vistoryteller supports fine-grained authorial control through two complementary mechanisms: a sketch-based tension-flow control for specifying how thematic emphasis and narrative tension should evolve, and a conversational interface for issuing localized directives to individual agents or to the team. We evaluate Vistoryteller with two controlled experiments and a qualitative user study. Results show that Vistoryteller generates narratives that align more closely with user intentions, preserve coherence across agent contributions, and surface diverse and expressive insights.2026YSYang Shi et al.College of Design and Innovation, Tongji UniversityGenerative AI (Text, Image, Music, Video)Data StorytellingCreative Collaboration & Feedback SystemsIUI
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
HCI for Agroecology: Agri-Tech between Grassroots and CapitalismDigital technologies in agriculture are typically portrayed as enabling more sustainable production while increasing productivity. Yet, commercial solutions rarely address the root causes of unsustainable farming, limiting the uptake of more radical solutions such as agroecology. We conducted fieldwork on 11 UK small-scale agroecological farms investigating their adoption of digital technologies. Far from being anti-technological, agroecological farmers are currently poorly supported by appropriate digital tools. Further, the collaborative nature of agroecological farming, market productivity pressures, and regulatory requirements necessitate complex data practices for coordination, planning, monitoring, and learning. These data practices require labour that is often hidden and causes tension within farms. We develop these insights into a set of guiding principles for designing digital technologies appropriate for agroecology and suggest concrete design opportunities. We present a call to action for HCI to reimagine digital agriculture beyond capitalism and work with existing farmer-led grassroots networks towards technological sovereignty.2026SPSebastian Prost et al.City St George's, University of LondonSustainable HCIEcological Design & Green ComputingCitizen Science & Crowdsourced DataCHI
AI Personalization Paradox: Reading Highlights for Personalized AI-Assisted Writing Increases Engagement but Undermines Autonomy and OwnershipAI-assisted writing raises concerns about autonomy and ownership when benefiting writers. Personalization has been proposed as an effective solution while also risking writers' reliance on AI and behavior shifting. For better personalization design, existing studies rely on interaction and information solely within the writing phase; however, few studies have examined how reading behaviors can inform personalized writing. This study investigates the effects of integrating reading highlights for personalization on AI-assisted writing. A between-subjects study with 46 participants revealed that the personalization condition encouraged participants to produce more highlights. However, highlighting unexpectedly shifted from a sense-making strategy to an instrumental act of "feeding the AI," leading to significant reliance on AI and declines in writers' sense of autonomy, ownership, and self-credit. These findings indicate personalization risks in AI-assisted writing, emphasize the importance of personalization strategies, and provide design implications.2026PQPeinuan Qin et al.National University of SingaporeHuman-LLM CollaborationAI-Assisted Writing & Text GenerationBehavior Change & Reflection TechnologyCHI
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
All Futures at Once: Supporting Speculative Design for Placemaking with Multi-Agent Social SimulationPlacemaking transforms physical spaces into socially meaningful places, with long-term impacts depending on how future communities inhabit and interact with them. Speculative design helps envision such futures, yet existing approaches often produce static representations that emphasize spatial form over evolving activity. We present ParaScape, a design support system that facilitates speculative design for placemaking by generating dynamic speculative objects through an underlying LLM-based multi-agent social simulation framework. The framework models heterogeneous agents with group-specific preferences and sensitivities, simulating context-sensitive behaviors and interactions that produce evolving scenarios. These scenarios are visualized as image sequences, where each scenario depicts multiple activities unfolding within a place at a given moment. ParaScape builds on this framework to allow designers to explore scenarios, analyze activity diversity and evolvability, and reflect on trade-offs among stakeholder needs. Evaluations through two experiments, a user study, and two case studies show that ParaScape supports critical reasoning and inclusive placemaking.2026JLJiayang Li et al.Tongji UniversityParticipatory DesignDesign FictionSmart Cities & Urban SensingCHI
ChatLearn: Leveraging Non-Native Speaker Communication Challenges as Language Learning OpportunitiesNon-native speakers (NNSs) face significant language barriers in multilingual communication with native speakers (NSs). While AI-mediated communication (AIMC) tools offer efficient one-time assistance, they often overlook opportunities for NNSs' continuous language acquisition. We introduce ChatLearn, an enhanced AIMC system that leverages NNSs' communication difficulties as learning opportunities. Beyond comprehension and expression assistance, ChatLearn simultaneously captures NNSs' language challenges, and subsequently provides them with spaced review as the conversation progresses. We conducted a mixed-methods study using a communication task with 43 NNS-NS pairs, after which ChatLearn NNSs recalled significantly more expressions than the baseline group, while there was no substantial decline in communication experience. Our findings highlight the value of contextual learning in NNS-NS communication, providing a new direction for AIMC systems that foster both immediate collaboration and continuous language development.2026PQPeinuan Qin et al.National University of SingaporeMultilingual & Cross-Cultural Voice InteractionHuman-LLM CollaborationIntelligent Tutoring Systems & Learning AnalyticsCHI
From Touch to Change: Understanding Public Engagement in Data Physicalization for Social GoodData physicalization, which encodes data in physical form, has been increasingly used to engage the public with issues of social good. While public engagement is often invoked as a motivation or expected outcome, it has not been systematically examined as a design objective. This gap raises two key challenges: what characterizes engagement in data physicalization for social good (Phys4Good), and how it can be effectively designed. In this work, we address these challenges by first curating a corpus of 45 Phys4Good projects and deriving a design space structured around a modified three-act framework comprising Stage, Encounter, and Impact. We then conducted semi-structured interviews with designers of eight projects to identify recurring challenges and strategies for fostering engagement. Finally, we demonstrated the effectiveness of our design space and strategies through a case study, which showed that they can guide designers in structuring engagement, anticipating barriers, and creating more impactful Phys4Good experiences.2026YPYechun Peng et al.Tongji UniversityData PhysicalizationCitizen Science & Crowdsourced DataCHI
When LLMs Enter Everyday Feminism on Chinese Social Media: Opportunities and Risks for Women’s EmpowermentEveryday digital feminism refers to the ordinary, often pragmatic ways women articulate lived experiences and cultivate solidarity in online spaces. In China, such practices flourish on RedNote through discussions under hashtags like ''women's growth''. Recently, DeepSeek-generated content has been taken up as a new voice in these conversations. Given widely recognized gender biases in LLMs, this raises critical concerns about how LLMs interact with everyday feminist practices. Through an analysis of 430 RedNote posts, 139 shared DeepSeek responses, and 3211 comments, we found that users predominantly welcomed DeepSeek's advice. Yet feminist critical discourse analysis revealed that these responses primarily encouraged women to self-optimize and pursue achievements within prevailing norms rather than challenge them. By interpreting this case, we discuss the opportunities and risks that LLMs introduce for everyday feminism as a pathway toward women's empowerment, and offer design implications for leveraging LLMs to better support such practices.2026RZRunhua ZHANG et al.The Hong Kong University of Science and TechnologyAI Ethics, Fairness & AccountabilityAlgorithmic Fairness & BiasGender & Race Issues in HCICHI
Beyond Input–Output: Rethinking Creativity through Design-by-Analogy in Human–AI CollaborationWhile the proliferation of foundation models has significantly boosted individual productivity, it also introduces a potential challenge: the homogenization of creative content. In response, we revisit Design-by-Analogy (DbA), a cognitively grounded approach that fosters novel solutions by mapping inspiration across domains. However, prevailing perspectives often restrict DbA to early ideation or specific data modalities, while reducing AI-driven design to simplified input–output pipelines. Such conceptual limitations inadvertently foster widespread design fixation. To address this, we expand the understanding of DbA by embedding it into the entire creative process, thereby demonstrating its capacity to mitigate such fixation. Through a systematic review of 85 studies, we identify six forms of representation and classify techniques across seven stages of the creative process. We further discuss three major application domains: creative industries, intelligent manufacturing, and education and services, demonstrating DbA’s practical relevance. Building on this synthesis, we frame DbA as a mediating technology for human-AI collaboration and outline the potential opportunities and inherent risks for advancing creativity support in HCI and design research.2026XLXuechen Li et al.Tongji UniversityGenerative AI (Text, Image, Music, Video)Human-LLM CollaborationCreative Collaboration & Feedback SystemsCHI
Depictions of Privacy Invasion and Surveillance in Artworks and Potential Lessons For Privacy CommunicationUser-facing communication about privacy (e.g., privacy policies, privacy tools' user interfaces) is frequently ignored and often ineffective. In contrast to these arguably staid interfaces, artworks often focus on provocation, engagement, and critical interpretation. For decades, artists have created privacy art—artistic media in galleries relating to the surveillance and privacy of individuals. What are artists saying about privacy, and how? Crucially, what lessons might they have for designing privacy-focused user interfaces? To this end, we compiled over 800 privacy artworks, qualitatively analyzing a sample. Common topics spanned artistic media (from paintings to immersive installations) and eras. Artworks built upon familiar concepts (e.g., cameras, homes) to speculate on society's future and present personal information (e.g., artist, viewer, public). We discuss lessons for making non-artistic privacy communication more engaging and powerful through directing attention (e.g., lighting, collage) and setting a tone (e.g., unsettling, fun, mundane).2026TETess Eschebach et al.University of ChicagoPrivacy by Design & User ControlDigital Art Installations & Interactive PerformanceTechnology Ethics & Critical HCICHI
Toward Natural and Companionable Virtual Agents via Cross-Temporal Emotional ModelingRecent advances in foundation models have enabled conversational agents that aim for sustained companionship rather than mere task completion. Yet most still remain unable to support natural, long-term companion-like interactions, resulting in experiences that feel episodic and inauthentic. We argue that current agents overlooked cross-temporal modeling of agents’ social behaviors and internal emotions: generated behaviors rarely influence an agent’s emotional state, and emotional states seldom shape subsequent behaviors. We present Cross-Temporal Emotion Modeling (CTEM), a framework that links long-term behavioral history to moment-to-moment emotional expression. CTEM establishes a closed loop where past experiences update an evolving emotional state; this state conditions immediate interactions; and user feedback continually revises both memory and emotional state, enabling reflection and anticipation. We instantiate CTEM as \textit{Auri}, a companion agent on an instant-messaging platform, and report a 21-day in-the-wild study showing that CTEM shows improvements in perceived naturalness, coherence, and emotional harmony.2026YZYi Zheng et al.Communication University of ChinaAgent Personality & AnthropomorphismAffective Human-Computer DialogueHuman-LLM CollaborationCHI
Continuous Measurement Methods for Transient Physiological Discomfort in VR LocomotionMotion sickness, in addition to its persistent long-term effects, also exhibits short-term effects characterized as transient physiological discomfort, which changes rapidly with variations in locomotion. However, such discomforts are challenging to assess using current subjective scales and objective physiological measurements. To tackle this issue, this paper suggests continuous measurement methods designed specifically for evaluating transient physiological discomfort during VR locomotion. Through a user-elicitation study, three preferred measurement methods—'squeezing ball', 'sliding thumb', and 'rubbing thigh'—were identified. These techniques were then evaluated for reliability, validity, attention, presence, and workload, with 'sliding thumb' identified as the most effective option. The paper expands traditional measurement methods to capture users' physiological experiences in VR interactions, offering practical choices for researchers in this field along with an in-depth discussion of design considerations, detailed implementation guidelines, and potential ways to optimize the VR experiences utilizing the measurement data.2026TLTianren Luo et al.Institute of SoftwareMotion Sickness & Passenger ExperienceImmersion & Presence ResearchCHI
The Wetland Quest: Fostering Empathy and Literacy for Urban Herpetofauna Through VR Wetland ExplorationThis paper investigates how virtual reality (VR) can foster empathy and ecological literacy for urban herpetofauna—reptiles and amphibians often overlooked in conservation. We present The Wetland Quest (TWQ), an immersive VR experience set in a Shanghai wetland that employs embodiment and scale-shift mechanics to situate users in the world of local species. In a mixed-methods study with 62 participants, TWQ significantly improved species literacy and attitudes toward herpetofauna, supported by large quantitative gains and qualitative themes of immersion, empathy, and reduced aversion. This work contributes to HCI and environmental communication by: (1) introducing TWQ as a design case of VR for underrepresented species; (2) providing empirical evidence that immersive perspective-taking can enhance literacy and pro-environmental attitudes; and (3) demonstrating a methodological protocol that combines knowledge tests, validated attitude scales, observations, and interviews, offering a transferable approach for future VR conservation research.2026LXLei Xia et al.Tongji UniversityImmersion & Presence ResearchHuman-Nature Relationships (More-than-Human Design)Sustainable HCICHI
Prosocial AI Apologies on the Road: Emotional Compensation for Other Drivers' MisbehaviorAggressive driving often triggers anger and retaliatory behaviors, posing threats to traffic safety. This paper proposes an AI-driven apology mechanism based on an Augmented Reality Head-Up Display (AR-HUD), which delivers immediate apologies on behalf of offending drivers during traffic conflicts and repairs damaged social relations through prosocial lies. We conducted a 2 (scenario risk: high vs. low) × 5 (apology depth) mixed-design experiment (N = 40) to evaluate its effectiveness. Results show that AI apologies enhanced positive emotions and forgiveness intentions while reducing anger, with participants also perceiving psychological benefits. These effects were consistent across both high- and low-risk scenarios. Our findings offer a practical design pathway for human-AI emotional regulation in traffic contexts.2026JZJun Zhang et al.Hubei Institute of Fine ArtsHead-Up Display (HUD) & Advanced Driver Assistance Systems (ADAS)In-Vehicle Haptic, Audio & Multimodal FeedbackEmotion Recognition & DetectionCHI
Vistoria: A Multimodal System to Support Fictional Story Writing through Instrumental Image-Text Co-EditingHumans think visually—we remember in images, dream in pictures, and use visual metaphors to communicate. Yet, most creative writing tools remain text-centric, limiting how writers plan and translate ideas. We present Vistoria, a system for synchronized image-text co-editing in fictional story writing. A formative Wizard-of-Oz co-design study with 10 story writers revealed how sketches, images, and text serve as essential elements for ideation and organization. Drawing on theories of Instrumental Interaction, Vistoria introduces instrumental operations—Lasso, Collage, Perspective Shift, and Filter that enable seamless narrative exploration across modalities. A controlled study with 12 participants shows that co-editing enhances expressiveness, immersion, and collaboration, opening space for writers to follow divergent story directions and craft more vivid, detailed narratives. While multimodality increased cognitive demand, participants reported stronger senses of ownership and agency. These findings demonstrate how multimodal co-editing expands creative potential by balancing abstraction and concreteness in narrative development.2026KFKexue Fu et al.City University of Hong KongAI-Assisted Creative WritingCreative Collaboration & Feedback SystemsCHI
"Our Secret Language": Co-Creating and Ritualizing Affective Haptics in Long-Distance RelationshipsLong-distance relationships (LDRs) struggle to sustain intimacy without physical touch. Existing mediated social touch systems rely on designer-authored haptic patterns, which limit opportunities for personalization and shared meaning-making. We present Onni, a haptic interface that lets couples collaboratively define and experience a shared library of haptic interactions. In Study 1, we conducted co-creation workshops (n=20) to examine how couples negotiate and align meanings in haptic interactions. In Study 2, we deployed Onni in everyday routines (n=6) to explore how these interactions are adopted, adapted, and ritualized. Our findings illustrate that couples co-create and personalize haptic interactions through continuous exploration, negotiation, and situational adaptation. By integrating a dyadic co-design approach, an end-user authoring interface for a shared action–feedback haptic repertoire, and a longitudinal view of how meanings evolve in everyday LDR routines, this work advances understanding of haptic meaning-making as a collaboratively constructed and ritualized process. It offers concrete design implications for building personalized, evolving haptic systems that support intimacy in LDRs.2026MYMengshi Yang et al.Tongji UniversityHaptic WearablesLong-Distance Relationship TechnologyAffective Human-Computer DialogueCHI
CoBreath: Designing a VR-Based Dyadic Biofeedback System to Support Breathing Exercise for Breast Cancer SurvivorsChronic stress and anxiety severely affect breast cancer survivors’ (BCSs) mental health and well-being. Peer support has been shown to enhance psychological empowerment, while biofeedback offers a promising approach to improve physiological relaxation through self-regulation. However, few studies explored combining both for BCSs. We conducted a formative study with clinicians and BCSs to identify requirements and preferences for VR biofeedback. Informed by the findings, we proposed a VR-based dyadic biofeedback system, Cobreath, which integrates breathing and heart rate variability (HRV) feedback into a calming virtual environment, allowing two users to practice breathing-focused relaxation simultaneously. Through a clinical user study with ten clinicians and a between-subjects study with 32 BCSs, we demonstrated that Cobreath’s dyadic mode improved biofeedback effectiveness and provided a better user experience compared to the individual mode. We further discuss insights and design considerations for developing dyadic VR-biofeedback applications to support the mental well-being of BCSs and potential applications.2026QWQi Wang et al.Tongji UniversityVR Medical Training & RehabilitationAffective Feedback & Emotion Regulation InterfacesMental Health Apps & Online Support CommunitiesCHI
ATRU: A Stage-based Framework for Designing Ethology-Inspired Social RobotsAnimal behavior (ethology) has emerged as a promising source of inspiration for social robot design. However, existing efforts have commonly resulted in isolated design instances. Our high-level understanding of the design processes for integrating ethological insights into social robot design and evaluation remains limited. To address this gap, we conducted a two-step investigation. First, we developed a stage-based framework through a systematic review, identifying six core design stages along with their descriptive dimensions. Using this framework as an analytic lens, we then analyzed design cases drawn from academic, commercial, and public contexts, deriving stage-specific considerations and actionable strategies to support designers in navigating the process. Our findings provide a conceptual scaffold for operationalizing ethology as a design resource, enabling more systematic, reflective, and transferable practices, while also surfacing new opportunities for future social robot interaction design.2026XSXiaoqing Sun et al.Beijing Institute of TechnologySocial Robot InteractionHuman-Robot Collaboration (HRC)CHI
Exploring Aggressors’ In‑Match Cognitive and Emotional Formation and Toxic Behavior Trajectories in MOBA GamesToxic behavior in Multiplayer Online Battle Arena (MOBA) games has become a major issue. While previous studies have examined factors influencing toxic behavior, few have captured the cognitive and emotional states of the aggressors at the point of emergence of toxic behavior, or traced its evolution across an entire match. To fill the gap, we conducted replay-based semi-structured interviews with 18 players who recently initiated toxic behavior during matches. With adapted retrospective think-aloud protocols and players' emotional journey maps, we collected their subjective perceptions and dynamic changes of emotion. Through thematic analysis, we identified a multi-dimensional criterion for evaluating toxicity severity and a three-layer cognition–emotion association structure, and described recurring persistent and single-instance patterns of toxic behavior observed in our matches. Based on our findings, we contribute to understanding the internal evolution of player toxicity and discuss implications for preventive intervention strategies and designs aiming at mitigating toxic behavior2026KYKangyu Yuan et al.Hong Kong University of Science and TechnologyGame UX & Player BehaviorMultiplayer & Social GamesEmpathy & Emotional DesignCHI