ROOTED in Us: A Framework for Cultivating Community Ecosystems through Relationships and DataAs data becomes integral to civic processes and resource distribution, there is a need for methods in which communities generate, interpret, and act on data to address their priorities. We introduce ROOTED (Reclaiming and Organizing Our Truths for Equity through Data), a community-centered framework grounded in Black Feminist Thought. By cultivating community data practices, ROOTED helps residents leverage their local insights, lived experiences, and data to pursue equitable outcomes by using data as a tool for advocacy, organizing, and local transformation. Through two case studies, we demonstrate how researchers and communities can collaboratively implement ROOTED. Our findings suggest that residents use data to build power and relationships to collectively achieve their goals. This paper contributes a framework and case study examples that demonstrate how to design community data systems and practices that produce actionable outcomes aligned with residents’ visions for their futures.2026SESheena Erete et al.University of Maryland College ParkEmpowerment of Marginalized GroupsCitizen Science & Crowdsourced DataCommunity Engagement & Civic TechnologyCHI
Participatory, not Punitive: Student-Driven AI Policy Recommendations in a Design ClassroomGenerative AI is reshaping education, yet most university AI policies are written without students and focus on penalizing misuse. This top-down approach sidelines those most affected from decisions that shape their everyday learning, resulting in confusion and fear about acceptable use. We examine how participatory, student-driven AI policy design can address this disconnect. We report on a three-part workshop series in a graduate design course at a minority-serving university in the U.S., where two student leaders facilitated discussions without faculty present. Eight participants shared candid accounts of their AI use, co-authored ten policy recommendations, and visualized them in a zine that circulated across campus. The resulting policies surfaced concerns absent from top-down governance, such as the double standard of requiring students to disclose or abstain from AI use while faculty face no such expectations. We argue that engaging students in AI governance carries value beyond the resulting policies, and offer transferable strategies for fostering participation across disciplines—a model for calling students in rather than calling students out.2026KSKaoru Seki et al.University of Maryland, Baltimore CountyParticipatory DesignHuman-LLM CollaborationAI Ethics, Fairness & AccountabilityCHI
As Content and Layout Co-Evolve: TangibleSite for Scaffolding Blind People’s Webpage Design through Multimodal InteractionCreating webpages requires generating content and arranging layout while iteratively refining both to achieve a coherent design, a process that can be challenging for blind individuals. To understand how blind designers navigate this process, we conducted two rounds of co-design sessions with blind participants, using design probes to elicit their strategies and support needs. Our findings reveal a preference for content and layout to co-evolve, but this process requires external support through cues that situate local elements within the broader page structure as well as multimodal interactions. Building on these insights, we developed TangibleSite, an accessible web design tool that provides real-time multimodal feedback through tangible, auditory, and speech-based interactions. TangibleSite enables blind individuals to create, edit, and reposition webpage elements while integrating content and layout decisions. A formative evaluation with six blind participants demonstrated that TangibleSite enabled independent webpage creation, supported refinement across content and layout, and reduced barriers to achieving visually consistent designs.2026JLJiasheng Li et al.University of MarylandVisual Impairment Technologies (Screen Readers, Tactile Graphics, Braille)Universal & Inclusive DesignTangible User Interface DesignCHI
Designing Multi-Robot Ground Video Sensemaking with Public Safety ProfessionalsVideos from fleets of ground robots can advance public safety by providing scalable situational awareness and reducing professionals’ burden. Yet little is known about how to design and integrate multi-robot videos into public safety workflows. Collaborating with six police agencies, we examined how such videos could be made practical. In Study 1, we presented the first testbed for multi-robot ground video sensemaking. The testbed includes 38 events-of-interest (EoI) relevant to public safety, a dataset of 20 robot patrol videos (10 day/night pairs) covering EoI types, and 6 design requirements aimed at improving current video sensemaking practices. In Study 2, we built MRVS, a tool that augments multi-robot patrol video streams with a prompt-engineered video understanding model. Participants reported reduced manual workload and greater confidence with LLM-based explanations, while noting concerns about false alarms and privacy. We conclude with implications for designing future multi-robot video sensemaking tools.2026PZPuqi Zhou et al.George Mason UniversityTeleoperation & TelepresenceExplainable AI (XAI)AI-Assisted Decision-Making & AutomationCHI
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
"Having Confidence in My Confidence Intervals": How Data Users Engage with Privacy-Protected Wikipedia DataIn response to calls for open data and growing privacy threats, organizations are increasingly adopting privacy-preserving techniques that add noise to published datasets. These techniques seek to protect privacy of data subjects while enabling useful analyses. With expert feedback, we developed empirically-driven documentation explaining the noise characteristics of two Wikipedia pageview datasets: one using rounding (heuristic privacy) and another using differential privacy (DP, formal privacy). We then used these documents to conduct a task-based contextual inquiry (n=15) exploring how data users—largely unfamiliar with these methods—perceive, interact with, and interpret privacy-preserving noise during data analysis. Participants readily used simple uncertainty metrics from the documentation, but struggled when computing confidence intervals across multiple noisy estimates. They better devised simulation-based approaches for computing uncertainty with DP-noised vs. rounded data. Surprisingly, several participants incorrectly believed DP's stronger utility implied weaker privacy protections. We offer design recommendations for documentation and tools to better support data users working with privacy-noised data.2026HTHarold Triedman et al.Cornell TechExplainable AI (XAI)Privacy by Design & User ControlPrivacy Perception & Decision-MakingCHI
Sense and Sensability: Exploring Future Immersive Environments for Scholarly SensemakingScholars must often make sense of vast amounts of complex and diverse scholarly information, much of which is not "senseable": crucial information like questions, concepts, or assertions, along with key properties like truthlikeness or evocativeness, are primarily identified through effortful search or reasoning, rather than direct perception through the senses. In this paper, we explore how we might augment scholarly sensemaking by making the full range of scholarly information more senseable. First, we systematically reviewed systems for scholarly sensemaking, and enumerated key types of scholarly information and their properties. Then, we synthesized design patterns for materializing abstract information in modern artworks, and connected them with our enumerated scholarly information and properties to develop three novel conceptual designs for senseable scholarly sensemaking in immersive environments. Our work lays the foundation for a novel design framework for exploring future immersive environments for scholarly sensemaking.2025SZSiyi Zhu et al.Immersion & Presence ResearchPrototyping & User TestingC&C
Will Too Many Editors Spoil The Tag? Conflicts and Alignment in Q&A CategorizationQ&A websites compile useful knowledge through user-generated questions and responses. Many Q&As use collaborative tagging systems to improve search and discovery while distributing the work of categorizing and organization throughout the community. Although early work on collaborative tagging questioned whether consistent categorization schemes could emerge from large groups with little to no coordination, empirical studies have found surprising coherence among users’ tags. We build on this research by testing whether coherence emerges in tag usage on Q&As, a more challenging context, focusing in particular on mismatches in the specificity of tags (basic level disagreement). We found that some users shifted toward more specific tag usage over time slightly increasing conflict, but that moderators were instrumental in helping to resolve some of this conflict. This study highlights the importance of learning and moderation in the development of coherence in collaborative tagging systems.2018JCJoohee ChoiCollaboration in Online CommunitiesCSCW