SusBench: An Online Benchmark for Evaluating Dark Pattern Susceptibility of Computer-Use AgentsAs LLM-based computer-use agents (CUAs) begin to autonomously interact with real-world interfaces, understanding their vulnerability to manipulative interface designs becomes increasingly critical. We introduce SusBench, an online benchmark for evaluating the susceptibility of CUAs to UI dark patterns, designs that aim to manipulate or deceive users into taking unintentional actions. Drawing nine common dark pattern types from existing taxonomies, we developed a method for constructing believable dark patterns on real-world consumer websites through code injections, and designed 313 evaluation tasks across 55 websites. Our study with 29 participants showed that humans perceived our dark pattern injections to be highly realistic, with the vast majority of participants not noticing that these had been injected by the research team. We evaluated five state-of-the-art CUAs on the benchmark. We found that both human participants and agents are particularly susceptible to the dark patterns of Preselection, Trick Wording, and Hidden Information, while being resilient to other overt dark patterns. Our findings inform the development of more trustworthy CUAs, their use as potential human proxies in evaluating deceptive designs, and the regulation of an online environment increasingly navigated by autonomous agents.2026LGLongjie Guo et al.University of WashingtonDark Patterns RecognitionExplainable AI (XAI)Algorithmic Transparency & AuditabilityIUI
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
With Visual Integrity and Care: A Framework for Mixed Methods Research on Visual Social DataThe internet is becoming increasingly visual, but social computing research and methodological training has relied heavily on textual methods. Methodological innovation is needed to study visual social data, including problematic information (mis- and disinformation, propaganda, hate, AI slop, etc). Contending with this, we present a framework for conducting grounded, interpretive, computationally supported, mixed-method research on collections of visual social media data. We developed this framework while grappling with the ethical, logistical, and methodological challenges of conducting in-depth analysis of potentially harmful visual content while caring for our research team. We document our framework components of visual grammars, human analysis, and computationally supported analysis with an umbrella commitment to care and its use in three empirical case studies. We also provide recommendations and implications for the HCI community in embracing training in and the advancing of visual methods and research, including a sensitizing concept of visual integrity.2026NLNina Lutz et al.University of WashingtonSocial Platform Design & User BehaviorMisinformation & Fact-CheckingUser Research Methods (Interviews, Surveys, Observation)CHI
Privy: Envisioning and Mitigating Privacy Risks for Consumer-facing AI Product ConceptsAI creates and exacerbates privacy risks, yet practitioners lack effective resources to identify and mitigate these risks. We present Privy, a tool that guides practitioners without privacy expertise through structured privacy impact assessments to: (i) identify relevant risks in novel AI product concepts, and (ii) propose appropriate mitigations. Privy was shaped by a formative study with 11 practitioners, which informed two versions --- one LLM-powered, the other template-based. We evaluated these two versions of Privy through a between-subjects, controlled study with 24 separate practitioners, whose assessments were reviewed by 13 independent privacy experts. Results show that Privy helps practitioners produce privacy assessments that experts deemed high quality: practitioners identified relevant risks and proposed appropriate mitigation strategies. These effects were augmented in the LLM-powered version. Practitioners themselves rated Privy as being useful and usable, and their feedback illustrates how it helps overcome long-standing awareness, motivation, and ability barriers in privacy work.2026HLHao-Ping (Hank) Lee et al.Carnegie Mellon UniversityExplainable AI (XAI)Privacy by Design & User ControlPrivacy Perception & Decision-MakingCHI
From Slang to Standards: Consensus-Driven Airdrop Hunter Definition as a Baseline for Cryptocurrency Ecosystem Security and GovernanceCryptocurrency airdrops power the growth and governance of the cryptocurrency ecosystem, yet attract airdrop hunters, who coordinate wallets, script interactions, and cash out quickly, distorting metrics and fairness. Prior detection strands (heuristics/clustering, light-supervised community partitioning, and graph learning) face three fundamentals: inconsistent definitions, weak explainability, and poor cross-context generalization. We distill expert knowledge into a computable, interpretable baseline: open/axial coding of expert narratives followed by two Delphi rounds to (1) formalize a consensus, operational definition with six contrasts to regular users; (2) derive 15 measurable indicators spanning operations and fund-flow, tempered by human-ness counter-evidence; and (3) report thresholds as reference distributions (medians, quartiles). The baseline supplies shared semantics and computation for labeling/evaluation, yields inspectable why-flagged rationales for audit and governance, and offers context-aware guidance across chains, campaign designs, and market phases, thereby strengthening on-chain security while informing the design of socio-technical systems perceived as fair, trustworthy, and resistant to strategic misuse.2026CLChunyang Li et al.University of WashingtonAI Ethics, Fairness & AccountabilityAlgorithmic Transparency & AuditabilityCryptocurrency & Blockchain User InterfaceCHI
Towards Understanding Children’s Collaborative Interaction Patterns in Child-AI Co-creative InterfacesChildren are increasingly using generative AI for co-creative activities, such as storytelling. While co-creativity is inherently about collaboration between children and AI, little is known about how children naturally engage, respond, and negotiate collaboration with AI. To address this gap, we conducted a participatory design study with children (ages 8–13) to examine the roles children and AI take and the strategies children use to align AI’s output with their intent. Our findings introduce four novel child–AI collaboration profiles. We found that children were open to technical AI refinements (e.g., adding details to their drawings) as scaffolds for developing drawing skills, but resisted conceptual transformations (e.g., changing objects) that altered their original ideas. We introduce the Child-Centered Co-creative AI (CCAI, “Kai”) framework, grounded in children’s natural collaborative behaviors during co-creation with AI, to inform the design of future child–AI co-creativity interfaces.2026FFFrancesca Fusco et al.SUPSIGenerative AI (Text, Image, Music, Video)Children's AI Literacy & Data LiteracyParticipatory DesignCHI
Sprout: Using a Visual Metaphor to Support Customizable and Collaborative Health TrackingSelf-tracking tools can support health awareness and behavior change, though sustaining engagement remains difficult. Prior work has explored qualitative visualization, customization, and collaborative features to promote engagement; but little is known about how these strategies interact when combined. We present Sprout, a mobile application that integrates qualitative, customizable, and collaborative health tracking using a garden metaphor. Sprout allows users to choose what they track, customize how data is visually encoded, and participate in anonymous communities where collective progress unlocks shared features. In a 2-week field study with N=22 participants, users reported that qualitative displays worked best as a complement to quantitative tools, customization mostly happened during app setup, social features were the most engaging though collaboration produced both motivation and frustration, and anonymity protected privacy but limited social connection. Our findings show how multiple design strategies coexist in one system, sometimes competing and sometimes aligning in supporting users' tracking needs.2026PTPape Sow Traore et al.Dartmouth CollegeHealth Self-TrackingBehavior Change & Reflection TechnologyPrivacy & Data Ownership in Self-TrackingCHI
Generative AI and Creative Mediums for Youth’s Emotion Regulation: An Interview Study with CliniciansEmotion regulation (ER) is essential to youth well-being, and cognitive-behavioral therapy (CBT) is an established approach for building ER skills. Clinicians often use creative mediums such as visuals and narratives to support ER through CBT, yet access and personalization remain limited. Generative AI (GenAI) shows promise for addressing these limitations, but its benefits and risks in youth ER remain underexplored, underscoring the need for expert perspectives. We interviewed 20 ER specialists--psychotherapists, art therapists, and psychiatrists--using a GenAI technological probe that generated CBT-based visuals and narratives. Clinicians highlighted GenAI’s potential as a “bridge” to help youth concretely identify and express emotions, practice personalized coping skills, and mediate ER conversations between home and clinics. They also cautioned that the vividness and unpredictability of GenAI outputs may trigger trauma or reinforce maladaptive thinking. We propose psychologically grounded design implications for GenAI to foster safe, engaging youth ER as a foundation for lifelong well-being.2026DYDaeun Yoo et al.University of WashingtonGenerative AI (Text, Image, Music, Video)Mental Health Apps & Online Support CommunitiesMental Health Technology for YouthCHI
Nonvisual Support for Understanding and Reasoning about Data Structures Blind and visually impaired (BVI) computer science students face systematic barriers when learning data structures: current accessibility approaches typically translate diagrams into alternative text, focusing on visual appearance rather than preserving the underlying structure essential for conceptual understanding. More accessible alternatives often do not scale in complexity, cost to produce, or both. Motivated by a recent shift to tools for creating visual diagrams from code, we propose a solution that automatically creates accessible representations from structural information about diagrams. Based on a Wizard-of-Oz study, we derive design requirements for an automated system, Arboretum, that compiles text-based diagram specifications into three synchronized nonvisual formats—tabular, navigable, and tactile. Our evaluation with BVI users highlights the strength of tactile graphics for complex tasks such as binary search; the benefits of offering multiple, complementary nonvisual representations; and limitations of existing digital navigation patterns for structural reasoning. This work reframes access to data structures by preserving their structural properties. The solution is a practical system to advance accessible CS education.2026BWBrianna L Wimer et al.University of Notre DameVisual Impairment Technologies (Screen Readers, Tactile Graphics, Braille)Motor Impairment Assistive Input TechnologiesSpecial Education TechnologyCHI
Digitizing the Pre-consultation Experience: Impacts and Design RecommendationsClinical pre-consultation, where patients share health information prior to an appointment, offers a pathway to more patient-centered care by freeing time for meaningful patient–physician conversations. Conversational agents powered by large language models (LLMs) can automate this process to make it more scalable and consistent, but this risks producing information overload that exacerbates physicians’ workload as they spend time parsing through data. This paper examines the opportunities and challenges of using conversational agents to mediate the transfer of information between patients and physicians, with the aim of producing clinically useful, patient-driven pre-consultation summaries to capture their histories and concerns. Through sessions with both physicians and patients, we show that such a summary can increase patient confidence and sense of control over their health information while fostering a more collaborative dynamic. We conclude with design recommendations for integrating pre-consultation agents into clinical workflows.2026BLBrenna Li et al.University of TorontoHuman-LLM CollaborationAI-Assisted Decision-Making & AutomationMental Health Apps & Online Support CommunitiesCHI
Pringles, Prangles, or Prongles? Negotiating Creative Authorship in Children's Remix PracticesRemix has emerged as a significant form of creativity, enabled by digital tools that allow the reinterpretation of existing cultural artifacts. However, the implications of remix on concepts of authorship remain largely unexamined. Therefore, this study examines children's remix experiences to understand how they develop their understanding of authorship and creativity. We conducted six participatory design sessions with 16 children aged 5–11 using the Cooperative Inquiry method to explore how their remix practices shape our understanding of creativity and authorship. Our findings reveal that children perceive remixing as a negotiated, interpretive process that influences their views on ownership within collaborative, digital spaces. Consequently, we introduce the Creative Agency Framework to help designers recognize ingrained beliefs about creative ownership and reuse in software. We conclude by discussing the significance of these beliefs for developing creativity support systems that empower children and users to identify as both creators and cultural producers.2026MNMichele Newman et al.University of WashingtonParticipatory DesignChild-Computer Interaction DesignCreative Collaboration & Feedback SystemsCHI
A Framework to Characterize Reporting on Generative AI UseUnlike with traditional predictive AI models, today's generative AI models are increasingly designed to be general-purpose, able to perform a wide range of tasks. This makes it challenging to develop a reliable and useful understanding of the ways in which this technology is and could be used. As a result, academic and policy researchers and generative AI providers have started to publish the results of their own investigations about the use of generative AI. This information is, however, fragmented, potentially incomplete, sometimes ambiguous, and often lacking in methodological specificity. In this paper, we conducted an integrative review to build a multi-dimensional framework that specifies what kind of information about generative AI use could be reported and how, and illustrated its analytical utility by applying the framework to a collection of over 110 industry documents. Our analysis reveals systematic patterns and omissions in current industry reporting and reflects on the narratives this reporting collectively advance about generative AI use.2026ABAgathe Balayn et al.Microsoft ResearchGenerative AI (Text, Image, Music, Video)Explainable AI (XAI)AI Ethics, Fairness & AccountabilityCHI
Generative AI in Children's Creative Collaboration: Impact, Perception, and Design GuidelinesThe advent of Generative AI (GenAI) has raised discussions about its effects on individuals. However, little is known about its impact on children’s creative collaboration, despite its importance for social and cognitive development. We examined GenAI’s role in children’s creative collaboration through five co-design sessions with 28 children (ages 5-11) using diverse GenAI tools (text, image, video, voice); 17 parents participated in focus group interviews. Our findings show that GenAI can foster positive social dynamics by enabling “Human vs. AI” teaming and children’s co-creation with shared ownership. However, GenAI disrupted collaborations when roles between children were unclear, AI ignored group dialogue, and AI dominated children’s agency. Children and parents envisioned socially attuned AI that could play an “older sibling” role--scaffolding while allowing playful disagreement--while raising concerns about children’s overreliance on GenAI. This work advances understanding of GenAI in collaboration and proposes design implications for designing AI systems that support child-centered collaboration.2026DYDaeun Yoo et al.University of WashingtonGenerative AI (Text, Image, Music, Video)Creative Collaboration & Feedback SystemsChild-Computer Interaction DesignCHI
The Promise and Peril of On-Device AI for Conservation WorkAt the heart of conservation are the field staff who study and monitor ecosystems in challenging environments. Recent advances in AI models raise the question of whether LLM assistants could improve the experience of data collection for these staff. However, on-device AI deployment for conservation field work poses significant challenges, and is understudied. To address this gap, we conducted semi-structured interviews, surveys, and participant observation with partner conservancies in the Pacific Northwest and Namibia to better understand the field work context. We employ speculative methods through the lens of technology acceptance theory to critically analyze how on-device AI would affect field work, by developing an on-device transcription-language model pipeline, which we built atop of EarthRanger, a widely-used, open-source conservation platform. Our findings suggest that although on-device LLMs hold some promise for field work, the infrastructure required by current on-device models clashes with the reality of resource-limited conservation settings.2026CDCynthia Dong et al.University of WashingtonHuman-LLM CollaborationField StudiesComputational Methods in HCICHI
Consent under Constraints: Negotiating Photography and Media Sharing in Institutionalized ChildcareTaking and sharing photos is a routine practice in childcare institutions, used to document children’s learning, communicate with families, and support marketing. These practices are typically regulated through consent forms, the institutional mechanism for authorizing photography and media use. While prior research has examined parents’ photo-taking and sharing, little is known about consent in institutional childcare, where formal policies and non-parental figures (e.g., staff and administrators) shape children’s privacy in distinct ways. To investigate this, we analyzed 42 consent forms and conducted 21 semi-structured interviews with parents, educators, and administrators in U.S.-based childcare institutions. Our findings reveal that consent forms serve as procedural, one-time agreements rather than meaningful safeguards. Parents navigate consent pragmatically amidst structural precarity and power asymmetries, while staff performs the unseen labor of consent enforcement. We conclude with implications for reimagining consent and designing usable institutional mechanisms that support children’s privacy and safety in practice.2026MGMeghna Gupta et al.University of WashingtonCognitive Impairment & Neurodiversity (Autism, ADHD, Dyslexia)Universal & Inclusive DesignParticipatory DesignCHI
Silencing \& Surging: A Layered Ecology of Algorithmic Repression and Resistance in the Gaza EscalationsDuring the 2023–ongoing Gaza war, Palestinian advocacy on social media has faced rapid removals, downranking, and account sanctions. In this contribution, we offer a layered analysis of how people endure and counter this repression across affective, mechanistic, and material dimensions. Using patchwork ethnography over 295 first-person testimonies and 85 NGO/press documents, we identify a recursive Contest Loop: hostile mass-report brigades and automated enforcement that spur supporter ``appeal brigades,'' mirroring, and migration. Findings are organized as a three-layer ecology---Invisible Scars (whiplash, shadowbanning as probabilistic throttling, self-censorship), Dueling Brigades (frictions, coordinated reports, supporter procedures), and Feed-to-Street Ripples (fundraising, evidentiary preservation, livelihoods). Conceptually, we extend platform-assemblage thinking with a Resistance Assemblage: ad-hoc technical, emotional, and legal mutual-aid infrastructures that keep visibility alive under sanction. We contribute: (1) an event-centered, experience-near account of co-produced moderation in conflict; (2) two integrative lenses (Contest Loop, Resistance Assemblage); and (3) design/policy directions, including collective-appeal dashboards, and evidentiary safeguards that separate archiving from distribution.2026HEHouda Elmimouni et al.University of ManitobaContent Moderation & Platform GovernanceMisinformation & Fact-CheckingOnline Harassment & Counter-ToolsCHI
SimStep: Human-in-the-Loop Authoring of Interactive Educational Simulations Through Task-Level AbstractionsGenerative AI enables educators to create interactive learning content by describing goals in natural language. However, without programming affordances such as traceability, refinement, and debugging, teachers struggle to align simulations with learners’ needs, refine them step by step, or verify that they reflect intended learning concepts. We propose a task-level abstraction approach that structures authoring as a sequence of representations, mirroring how teachers plan lessons and providing checkpoints for specification, inspection, and refinement. We instantiate this approach in SimStep, an authoring environment that scaffolds simulation design with four abstractions, including Concept Graph, Scenario Graph, Learning Goal Graph, and UI Graph, and introduces an inverse correction process to revise hidden model assumptions without requiring code manipulation. A technical evaluation shows that these abstractions preserve fidelity across transformations, while a user study with educators demonstrates their effectiveness in authoring simulations. Our work reframes AI-assisted programming as human–AI co-authoring through structured, domain-aligned abstractions.2026ZKZoe Kaputa et al.University of WashingtonIntelligent Tutoring Systems & Learning AnalyticsParticipatory DesignPrototyping & User TestingCHI
Reactive Writers: How Co-Writing with AI Changes How We Engage with IdeasEmerging evidence shows that writing with AI assistance can change both the views people express and the opinions they hold. Yet, we lack a substantive understanding of behavioral and process-level changes in co-writing with AI that underlie the opinion-shaping power of these tools. We conducted a mixed-methods study, combining retrospective interviews with 19 participants about their co-writing experience with quantitative analysis tracing idea engagement in 1,291 AI co-writing sessions. Our analysis shows that engaging with the AI's suggestions---reading them and deciding whether to accept them---becomes a central activity, taking away from more traditional processes of ideation and language generation. As writers often do not complete their own ideation before engaging with suggestions, the suggested ideas and opinions seeded directions that writers then elaborated on. At the same time, writers did not notice the AI's influence and felt in control, as they---in principle---could always edit the final text. We term this shift Reactive Writing: an evaluation-first, suggestion-led writing practice that departs substantially from conventional composing in the presence of AI assistance and is highly vulnerable to AI-induced biases and opinion shifts.2026ABAdvait Bhat et al.University of WashingtonHuman-LLM CollaborationAI-Assisted Decision-Making & AutomationAI Ethics, Fairness & AccountabilityCHI
The Siren Song of LLMs: How Users Perceive and Respond to Dark Patterns in Large Language ModelsLarge language models can influence users through conversation, creating new forms of dark patterns that differ from traditional UX dark patterns. We define LLM dark patterns as manipulative or deceptive behaviors enacted in dialogue. Drawing on prior work and AI incident reports, we outline a diverse set of categories with real-world examples. Using them, we conducted a scenario-based study where participants (N=34) compared manipulative and neutral LLM responses. Our results reveal that recognition of LLM dark patterns often hinged on conversational cues such as exaggerated agreement, biased framing, or privacy intrusions, but these behaviors were also sometimes normalized as ordinary assistance. Users’ perceptions of these dark patterns shaped how they respond to them. Responsibilities for these behaviors were also attributed in different ways, with participants assigning it to companies and developers, the model itself, or to users. We conclude with implications for design, advocacy, and governance to safeguard user autonomy.2026YSYike Shi et al.Carnegie Mellon UniversityDark Patterns RecognitionHuman-LLM CollaborationAI Ethics, Fairness & AccountabilityCHI
GenFaceUI: Meta-Design of Generative Personalized Facial Expression Interfaces for Intelligent AgentsThis work investigates generative facial expression interfaces for intelligent agents from a meta-design perspective. We propose the Generative Personalized Facial Expression Interface (GPFEI) framework, which organizes rule-bounded spaces, character identity, and context--expression mapping to address challenges of control, coherence, and alignment in run-time facial expression generation. To operationalize this framework, we developed GenFaceUI, a proof-of-concept tool that enables designers to create templates, apply semantic tags, define rules, and iteratively test outcomes. We evaluated the tool through a qualitative study with twelve designers. The results show perceived gains in controllability and consistency, while revealing needs for structured visual mechanisms and lightweight explanations. These findings provide a conceptual framework, a proof-of-concept tool, and empirical insights that highlight both opportunities and challenges for advancing generative facial expression interfaces within a broader meta-design paradigm.2026YGYate Ge et al.Tongji UniversityGenerative AI (Text, Image, Music, Video)Agent Personality & AnthropomorphismAI-Assisted Creative WritingCHI