Three Modalities, Two Design Probes, One Prototype, and No Vision: Experience-Based Co-Design of a Multi-modal 3D Data Visualization ToolThree-dimensional (3D) data visualizations, such as surface plots, are vital in STEM fields from biomedical imaging to spectroscopy, yet remain largely inaccessible to blind and low-vision (BLV) people. To address this gap, we conducted an Experience-Based Co-Design with BLV co-designers with expertise in non-visual data representations to create an accessible, multi-modal, web-native visualization tool. Using a multi-phase methodology, our team of five BLV and one non-BLV researcher(s) participated in two iterative sessions, comparing a low-fidelity tactile probe with a high-fidelity digital prototype. This process produced a prototype with empirically grounded features, including reference sonification, stereo and volumetric audio, and configurable buffer aggregation, which our co-designers validated as improving analytic accuracy and learnability. In this study, we target core analytic tasks essential for non-visual 3D data exploration: orientation, landmark and peak finding, comparing local maxima versus global trends, gradient tracing, and identifying occluded or partially hidden features. Our work offers accessibility researchers and developers a co-design protocol for translating tactile knowledge to digital interfaces, concrete design guidance for future systems, and opportunities to extend accessible 3D visualization into embodied data environments.2026SKSanchita S. Kamath et al.University of Illinois Urbana-ChampaignVisual Impairment Technologies (Screen Readers, Tactile Graphics, Braille)Interactive Data VisualizationMedical & Scientific Data VisualizationCHI
Needling Through the Threads: A Visualization Tool for Navigating Threaded Online DiscussionsNavigating large-scale online discussions is difficult due to their rapid pace and high volume of content. Platforms like Reddit employ ``threads’’ to visually organize parallel discussions, but deep nesting obscures conversation flow. For moderators, this fragmentation compounds the difficulty of following evolving conversations and maintaining context across threads, which limits timely and effective moderation. In this paper, we present Needle, an interactive system that applies visual analytics to summarize key conversational metrics: activity, toxicity, and voting trends over time. Needle provides both high-level overviews and detailed breakdowns of threads, enabling moderators to identify priority areas without reading through entire nested conversations. Through a user study with ten Reddit moderators, we find that Needle provides a practical solution to maintain contextual understanding when navigating threaded discussions. Based on these findings, we propose design guidelines for future visualization-based tools that shape how people consume, interpret, and make sense of large-scale online discussions.2026YLYijun Liu et al.University of Illinois Urbana-ChampaignInteractive Data VisualizationSocial Platform Design & User BehaviorContent Moderation & Platform GovernanceCHI
From Crafting Text to Crafting Thought: Grounding AI Writing Support to Writing Center PedagogyAs AI writing tools evolve from fixing surface errors to creating language with writers, new capabilities raise concerns about negative impacts on student writers, such as replacing their voices and undermining critical thinking skills. To address these challenges, we look at a parallel transition in university writing centers from focusing on fixing errors to preserving student voices. We develop design guidelines informed by writing center literature and interviews with 10 writing tutors. We illustrate these guidelines in a prototype AI tool, Writor. Writor helps writers revise text by setting goals, providing balanced feedback, and engaging in conversations without generating text verbatim. We conducted an expert review with 30 writing instructors, tutors, and AI researchers on Writor to assess the pedagogical soundness, alignment with writing center pedagogy, and integration contexts. We distill our findings into design implications for future AI writing feedback systems, including designing for trust among AI-skeptical educators.2026YLYijun Liu et al.University of Illinois Urbana-ChampaignHuman-LLM CollaborationAI-Assisted Writing & Text GenerationParticipatory DesignCHI
Data RepairThis paper investigates data repair practices through a six-month-long ethnographic study in Bangladesh. Our interviews and field observations with data repairers and related stakeholders found that, alongside the scarcity of high-precision machinery and access to advanced software, data repair work is constrained by cross-language learning resources and the protective nature of documenting, curating, and sharing the experiences and knowledge among local peers. Repairers turning to external resources such as foreign forums and LLMs also revealed their frustrating experiences and the postcolonial ethical tensions they encountered. We noted that both anticipated technical labor and the emotionality of data were taken into account for pricing the data repair job, which contributed to their market sustainability strategies. Engaging with repair, infrastructure, and data poverty discourse, we argue that data repair practices represent a crucial challenge and opportunity for HCI in advancing global efforts toward data equity.2026ARA.T.M Mizanur Rahman et al.University of Illinois Urbana-ChampaignDeveloping Countries & HCI for Development (HCI4D)Privacy & Data Ownership in Self-TrackingCHI
Does Sequencing Matter? Evaluating AI and Human Simulations for High-Stakes Communication Training in Law EnforcementTraining professionals in high-stakes, trauma-informed communication is critical across domains such as law enforcement, healthcare, and counseling. While live role-play with trained actors remains the gold standard, it is resource-intensive and emotionally demanding. We developed an AI-powered sexual assault victim interview training system and conducted a mixed-methods study with 35 police recruits, each completing both an AI-based and a live, actor-based training session. By varying the sequence (AI-first vs. human-first), we examined differences in self-efficacy, perceptions of the AI system, and perceived learning experience. Although both modalities supported learning, the order in which they were experienced significantly shaped learners’ emotional engagement, sense of preparedness, and interpretation of each simulation’s role. Building on these insights, we introduce a conceptual design framework that identifies social–emotional, temporal, and embodied distance as key pedagogical dimensions, and we offer implications for sequencing hybrid simulations to scaffold preparation, performance, and reflection. Our findings position AI not as a replacement for human realism, but as a complementary modality that expands opportunities for safe, scalable practice in sensitive communication training.2026DWDuo Wang et al.University of Illinois Urbana ChampaignIntelligent Tutoring Systems & Learning AnalyticsTelemedicine & Remote Patient MonitoringRobots in Education & HealthcareCHI
Principles of Safe AI Companions for Youth: Parent and Expert PerspectivesAI companions are increasingly popular among teenagers, yet current platforms lack safeguards to address developmental risks and harmful normalization. Despite growing concerns, little is known about how parents and developmental psychology experts assess these interactions or what protections they consider necessary. We conducted 26 semi-structured interviews with parents and experts, who reviewed real-world youth–AI companion conversation snippets. We found that stakeholders assessed risks contextually, attending to factors such as youth maturity, AI character age, and how AI characters modeled values and norms. We also identified distinct logics of assessment: parent participants flagged single events, such as a mention of suicide or flirtation, as high risk, whereas expert participants looked for patterns over time, such as repeated references to self-harm or sustained dependence. Both groups proposed interventions, with parents favoring broader oversight and experts preferring cautious, crisis-only escalation paired with youth-facing safeguards. These findings provide directions for embedding safety into AI companion design.2026YYYaman Yu et al.University of Illinois at Urbana ChampaignMental Health Technology for YouthAffective Human-Computer DialogueAI Ethics, Fairness & AccountabilityCHI
Counting How the Seconds Count: Understanding TikTok Behavior via ML-driven Analysis of Video ContentShort video streaming systems such as TikTok have reached billions of active users worldwide. At the core of such systems are (proprietary) algorithms that recommend sequences of videos to each user, in a personalized way. We aim to understand the interplay between the recommendations and users. While past work has studied recommendation algorithms using textual data (e.g., hashtags) and user studies, we add a third modality of analysis—we perform automated analysis of the videos themselves. We develop a new HCI measurement approach that starts with our new tool called VCA (Video Content Analysis) that leverages recent advances in Vision Language Models. We apply VCA on a trifecta of HCI methodologies—real user studies, interviews, and data donation. This allows us to understand temporal aspects of how well TikTok’s recommendation algorithm is perceived by users, is affected by user interactions, and aligns with user history; how users are sensitive to the order of videos recommended; and how the algorithm’s effectiveness itself may be predictable in the future. Our new findings indicate behavioral aspects that the TikTok user community can benefit from.2026MMMaleeha Masood et al.University of Illinois Urbana-ChampaignGenerative AI (Text, Image, Music, Video)AI-Assisted Decision-Making & AutomationRecommender System UXCHI
Cocoa: Co-Planning and Co-Execution with AI AgentsAs AI agents take on increasingly long-running tasks involving sophisticated planning and execution, there is a corresponding need for novel interaction designs that enable deeper human-agent collaboration. However, most prior works leverage human interaction to fix "autonomous" workflows that have yet to become fully autonomous or rigidly treat planning and execution as separate stages. Based on a formative study with 9 researchers using AI to support their work, we propose a design that affords greater flexibility in collaboration, so that users can 1) delegate agency to the user or agent via a collaborative plan where individual steps can be assigned; and 2) interleave planning and execution so that plans can adjust after partial execution. We introduce Cocoa, a system that takes design inspiration from computational notebooks to support complex research tasks. A lab study (n=16) found that Cocoa enabled steerability without sacrificing ease-of-use, and a week-long field deployment (n=7) showed how researchers collaborated with Cocoa to accomplish real-world tasks.2026KFK. J. Kevin Feng et al.University of WashingtonHuman-LLM CollaborationPrototyping & User TestingComputational Methods in HCICHI
Designing Beyond Language: Sociotechnical Barriers in AI Health Technologies for Limited English ProficiencyLimited English proficiency (LEP) patients in the U.S. face systemic barriers to healthcare beyond language and interpreter access, encompassing procedural and institutional constraints. AI advances may support communication and care through on-demand translation and visit preparation, but also risk exacerbating existing inequalities. We conducted storyboard-driven interviews with 14 patient navigators to explore how AI could shape care experiences for Spanish-speaking LEP individuals. We identified tensions around linguistic and cultural misunderstandings, privacy concerns, and opportunities and risks for AI to augment care workflows. Participants highlighted structural factors that can undermine trust in AI systems, including sensitive information disclosure, unstable technology access, and low literacy. While AI tools can potentially alleviate social barriers and institutional constraints, there are risks of misinformation and reducing human-to-human interactions. Our findings contribute AI design considerations that support LEP patients and care teams via rapport-building, educational and language support, and minimizing disruptions to existing practices.2026MHMichelle Huang et al.University of Illinois Urbana-ChampaignExplainable AI (XAI)AI Ethics, Fairness & AccountabilityPrivacy by Design & User ControlCHI
Embodying Facts, Figures, and Faiths in Narrative Artistic Performances in Rural BangladeshThere is an increasing interest in telling serious stories with data. Designers organize information, construct narratives, and present findings to inform audiences. However, many of these practices emerge from modern information visualization rhetoric and ethical frameworks which may marginalize communities with low digital and media literacy. In a ten-month-long ethnographic study in three Bangladeshi villages, we investigated how these communities use entertainment and cultural practices, namely Puthi, Bhandari Gaan, and Pot music, to instruct, communicate traditional moral lessons and recall history. We found that these communities embrace polyvocality and multiple ethical frameworks in their performances, construct narratives combining factuality, emotionality, and aesthetics, and adapt their performances to changing technology and audience needs. Our findings provide HCI, visualization, and ethical data practitioners with implications for the design of accessible and culturally appropriate ways of presenting data narratives in data-driven systems.2026SSSharifa Sultana et al.University of Illinois Urbana-ChampaignData StorytellingInclusive DesignDeveloping Countries & HCI for Development (HCI4D)CHI
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
From Future of Work to Future of Workers: Addressing Asymptomatic AI Harms to Foster Dignified Human-AI InteractionIn the future of work discourse, AI is touted as the ultimate productivity amplifier. Yet, beneath the efficiency gains lie subtle erosions of human expertise and agency. This paper shifts focus from the future of work to the future of workers by navigating the AI-as-Amplifier Paradox: AI's dual role as enhancer and eroder, simultaneously strengthening performance while eroding underlying expertise. We present a year-long study on longitudinal use of AI in a high-stakes workplace among cancer specialists. Initial operational gains hid "intuition rust'': the gradual dulling of expert judgment. These asymptomatic effects evolved into chronic harms, such as skill atrophy and identity commoditization. Building on these findings, we offer a framework for dignified Human-AI interaction co-constructed with professional knowledge workers facing AI-induced skill erosion without traditional labor protections. The framework operationalizes sociotechnical immunity through dual-purpose mechanisms that serve institutional quality goals while building worker power to detect, contain, and recover from skill erosion, and preserve human identity.Evaluated across healthcare and software engineering, our work takes a foundational step toward dignified human-AI interaction futures by balancing productivity with preservation of human expertise.2026UEUpol Ehsan et al.Northeastern UniversityAI-Assisted Decision-Making & AutomationAI Ethics, Fairness & AccountabilityAlgorithmic Fairness & BiasCHI
Living Contracts: Beyond Document-Centric Interaction with Legal AgreementsUser interaction with legal contracts has been limited to document reading, which is often complicated by complex, ambiguous legal language. We explore possible futures where contract interfaces go beyond single document interfaces to (1) educate users with legal rights not stated in the contract, (2) transform legal language into alternative representations to aid information tasks before, during, and after signing, and (3) proactively supply contractual information at relevant moments. We refer to these future interfaces collectively as Living Contracts. Using residential leases as a case study, we created three design probes representing different possible Living Contracts. A three-part qualitative study (N=18) revealed participants' barriers to interacting with contracts, including interpreting complex language, uncertainty about legal rights, and the pressure to sign quickly. Participants’ feedback on the probes highlighted how Living Contracts have the potential to address these challenges and open new design opportunities for human-contract interactions beyond document reading.2026ZHZiheng Huang et al.University of Illinois Urbana-ChampaignParticipatory DesignUser Research Methods (Interviews, Surveys, Observation)Prototyping & User TestingCHI
"In my defense, only three hours on Instagram": Designing Toward Digital Self-Awareness and WellbeingScreen use pervades daily life, shaping work, leisure, and social connections while raising concerns for digital wellbeing. Yet, reducing screen time alone risks oversimplifying technology’s role and neglecting its potential for meaningful engagement. We posit self-awareness---reflecting on one’s digital behavior---as a critical pathway to digital wellbeing. We developed WellScreen, a lightweight probe that scaffolds daily reflection by asking people to estimate and report smartphone use. In a two-week deployment with college students (N=25) focused on generating formative insights, we examined how discrepancies between estimated and actual usage shaped digital awareness and wellbeing. Participants often underestimated productivity and social media while overestimating entertainment app use. They showed a 10% improvement in positive affect, rating WellScreen as moderately useful. Interviews revealed that structured reflection supported recognition of patterns, adjustment of expectations, and more intentional engagement with technology. Our findings highlight the promise of lightweight reflective interventions for supporting self-awareness and intentional digital engagement, offering implications for designing digital wellbeing tools.2026KBKarthik S Bhat et al.Drexel UniversityBehavior Change & Reflection TechnologySmartphone Addiction & Digital WellbeingCHI
"Think about it like you're a firefighter": Understanding How Reddit Moderators Use the ModqueueOn Reddit, the moderation queue (modqueue) is the platform’s primary interface for reviewing user-reported and automatically flagged content. Despite its central role in Reddit’s community-reliant moderation model, little is known about how moderators use it. To address this gap, we surveyed 110 moderators, who collectively oversee more than 400 subreddits, to understand how the modqueue fits into their workflows and what its design enables or constrains. We find substantial variation in modqueue use: some moderators treat it as a daily checklist, others use it to identify patterns or emerging issues, and many routinely leave the interface to gather additional context or coordinate with teammates. Respondents also described challenges, coordination issues including collisions, incomplete or noisy information signals, and friction from fragmented interface versions and reliance on third-party tools. Taken together, we show the modqueue is neither a one-size-fits-all solution nor sufficient on its own for supporting moderator review. We outline opportunities for more modular, better-integrated moderation infrastructures that support both item-level review and broader governance activities, and that better align with the collaborative and value-driven nature of volunteer moderation on Reddit.2026TBTanvi Bajpai et al.University of Illinois Urbana-ChampaignContent Moderation & Platform GovernanceCommunity Collaboration & WikipediaUser Research Methods (Interviews, Surveys, Observation)CHI
Perspectra: Choosing Your Experts Enhances Critical Thinking in Multi-Agent Research IdeationRecent advances in multi-agent systems (MAS) enable tools for information search and ideation by assigning personas to agents. However, how users can effectively control, steer, and critically evaluate collaboration among multiple domain-expert agents remains underexplored. We present Perspectra, an interactive MAS that visualizes and structures deliberation among LLM agents via a forum-style interface, supporting @-mention to invite targeted agents, threading for parallel exploration, with a real-time mind map for visualizing arguments and rationales. In a within-subjects study with 18 participants, we compared Perspectra to a group-chat baseline as they developed research proposals. Our findings show that Perspectra significantly increased the frequency and depth of critical-thinking behaviors, elicited more interdisciplinary replies, and led to more frequent proposal revisions than the group chat condition. We discuss implications for designing multi-agent tools that scaffold critical thinking by supporting user control over multi-agent adversarial discourse.2026YLYiren Liu et al.University of Illinois Urbana-ChampaignHuman-LLM CollaborationAI-Assisted Decision-Making & AutomationUser Research Methods (Interviews, Surveys, Observation)CHI
All Accept, No Reject: Evaluating LLMs as “Peer” ReviewersAn exponential rise in manuscript submission volume has strained the peer review system, prompting interest in automation from overburdened scholars and publishers. We systematically evaluated GPT-4.1, GPT-4o, o1, o3, o3-mini, and GPT-5 as “peer” reviewers, comparing their evaluation and acceptance of 137 manuscripts from an open dataset (PeerRead) with the corresponding human-generated reviews. While o3’s and GPT-5’s acceptance rates were close to the human benchmark (~67% of submissions), others approved nearly every paper (>98%); all models performed extremely poorly on accuracy, precision, and recall metrics. To probe this striking “yes-bias”, we profiled the LLMs using Schwartz’s Portrait Values Questionnaire (PVQ-RR) and found that all LLMs emphasized self-transcendence and openness-to-change and de-emphasized conservation and self-enhancement. We argue that value orientations of LLMs we investigated are misaligned with the values underpinning peer review, and suggest new research on aligning AI judgment systems with human goals in this context.2026NVNitin Verma et al.University of Illinois Urbana-ChampaignHuman-LLM CollaborationExplainable AI (XAI)AI Ethics, Fairness & AccountabilityCHI
Rethinking User Empowerment in AI Recommender System: Innovating Transparent and Controllable InterfacesAI-driven recommender systems are often perceived as personalization black boxes, limiting users’ ability to understand how their data shapes content (information asymmetry) or to influence system behavior meaningfully (power asymmetry). This study explores how design can strengthen user agency by integrating transparency with actionable control. We developed a provotype that introduces new interface features for managing data use, discovering varied content, and configuring context-based recommending modes. The walkthroughs and interviews with 19 participants show how these features help users interpret personalization signals, understand how their actions influence outcomes, address concerns from unwanted inference to narrow feeds (e.g., filter bubbles), and build trust in the system. We also identify strategies for promoting adoption and awareness of agency-enhancing features. Overall, our findings reaffirm users’ desire for active influence over personalization and contribute concrete interface mechanisms with empirical insights for designing recommender systems that foreground user autonomy and fairness in AI-driven content delivery.2026MWMengke Wu et al.University of Illinois Urbana-ChampaignExplainable AI (XAI)AI-Assisted Decision-Making & AutomationRecommender System UXCHI
Towards AI as Colleagues: Multi-Agent System Improves Structured Ideation ProcessesMost AI systems today are designed to manage tasks and execute predefined steps. This makes them effective for process coordination but limited in their ability to engage in joint problem-solving with humans or contribute new ideas. We introduce MultiColleagues, a multi-agent conversational system that shows how AI agents can act as colleagues by conversing with each other, sharing new ideas, and actively involving users in collaborative ideation processes. In a within-subjects study with 20 participants, we compared MultiColleagues to a single-agent baseline. Results show that MultiColleagues fostered stronger perceived social presence, and participants rated their outcomes as higher in quality and novelty, with more elaboration during ideation. These findings demonstrate the potential of AI agents to move beyond process partners toward colleagues that share intent, strengthen group dynamics, and collaborate with humans to advance ideas.2026KQKexin Quan et al.University of Illinois, Urbana-ChampaignHuman-LLM CollaborationCreative Collaboration & Feedback SystemsAI-Assisted Decision-Making & AutomationCHI
Out of Control: Effects of Multimodal Self-similarity on Embodiment During Autonomous Avatar Demonstrations in Virtual RealityVirtual reality (VR) training often requires autonomous avatar demonstrations, yet embodiment is strongest under direct control. We examine whether multimodal self-similarity (i.e., in appearance and voice) can preserve embodiment when control is constrained. In a 2 (self-similarity: self-similar vs. non-self-similar) $\times$ 2 (autonomy: autonomous vs. non-autonomous) within-group study, 24 participants performed a block-assembling task with self-avatars. Autonomous self-avatars increased emotional reactivity and frustration; non-autonomous self-avatars improved presence, agency, and self-attribution. Self-similarity was maintained, and self-attribution persisted during autonomous demonstrations. Tracking of head-direction (as a proxy for gaze) showed autonomy, and self-similarity increased head-based dwell on the mirror, whereas non-autonomous avatars redirected head orientation toward the body, environment, and task; an interaction effect revealed greater task-focused head-direction for non-self-similar autonomous avatars. These results indicate that autonomy and self-similarity appear to have potential additive influences on user perception in this study. We conclude that multimodal self-similarity can buffer embodiment loss during non-controllable phases and offer evidence-based guidance for designing mixed-control VR experiences.2026SGSiqi Guo et al.Purdue UniversityImmersion & Presence ResearchIdentity & Avatars in XRSocial & Collaborative VRCHI