Understanding the Use of a Large Language Model-Powered Guide to Make Virtual Reality Accessible for Blind and Low Vision PeopleAs social virtual reality (VR) grows more popular, addressing accessibility for blind and low vision (BLV) users is increasingly critical. Researchers have proposed an AI “sighted guide” to help users navigate VR and answer their questions, but it has not been studied with users. To address this gap, we developed a large language model (LLM)-powered guide and studied its use with 16 BLV participants in virtual environments with confederates posing as other users. We found that when alone, participants treated the guide as a tool, but treated it companionably around others, giving it nicknames, rationalizing its mistakes with its appearance, and encouraging confederate-guide interaction. Our work furthers understanding of guides as a versatile method for VR accessibility and presents design recommendations for future guides.2026JCJazmin Collins et al.Cornell UniversityVoice AccessibilitySocial & Collaborative VRHuman-LLM CollaborationCHI
WatchHand: Enabling Continuous Hand Pose Tracking On Off-the-Shelf SmartwatchesTracking hand poses on wrist-wearables enables rich, expressive interactions, yet remains unavailable on commercial smartwatches, as prior implementations rely on external sensors or custom hardware, limiting their real-world applicability. To address this, we present WatchHand, the first continuous 3D hand pose tracking system implemented on off-the-shelf smartwatches using only their built-in speaker and microphone. WatchHand emits inaudible frequency-modulated continuous waves and captures their reflections from the hand. These acoustic signals are processed by a deep-learning model that estimates 3D hand poses for 20 finger joints. We evaluate WatchHand across diverse real-world conditions---multiple smartwatch models, wearing-hands, body postures, noise conditions, pose-variation protocols---and achieve a mean per-joint position error of 7.87 mm in cross-session tests with device remounting. Although performance drops for unseen users or gestures, the model adapts effectively with lightweight fine-tuning on small amounts of data. Overall, WatchHand lowers the barrier to smartwatch-based hand tracking by eliminating additional hardware while enabling robust, always-available interactions on millions of existing devices.2026JKJiwan Kim et al.KAISTHand Gesture RecognitionSmartwatches & Fitness BandsContext-Aware ComputingCHI
Fairness-in-the-Workflow: How Machine Learning Practitioners at Big Tech Companies Approach Fairness in Recommender SystemsRecommender systems (RS), which are widely deployed across high-stakes domains, are susceptible to biases that can cause large-scale societal impacts. Researchers have proposed methods to measure and mitigate such biases - but translating academic theory into practice is inherently challenging. Through a semi-structured interview study (N=11), we map the RS practitioner workflow within large technology companies, focusing on how technical teams consider fairness internally and in collaboration with legal, data, and fairness teams. We identify key challenges to incorporating fairness into existing RS workflows: defining fairness in RS contexts, balancing multi-stakeholder interests, and navigating dynamic environments. We also identify key organization-wide challenges: making time for fairness work and facilitating cross-team communication. Finally, we offer actionable recommendations for the RS community, including practitioners and HCI researchers.2026JYJing Nathan Yan et al.Cornell UniversityAI Ethics, Fairness & AccountabilityRecommender System UXParticipatory DesignCHI
"When We're Looking at the Robot, We See Each Other": A Comparison of Robotic, Mirror-Based, and Hybrid Interventions for Stranger InteractionEye contact between strangers, even fleeting, can spark interaction and foster connection, happiness, and belonging. Yet in public spaces, such encounters are often suppressed by “civil inattention,” with many people absorbed in their phones. We explore how reconfiguring the ambient environment with MirrorBot, a mobile robot with adaptive mirrors, can encourage social encounters by subtly redirecting glances. By shifting reflections between self- and mutual recognition, MirrorBot invites serendipitous eye contact, shared awareness, and low-stakes engagement. In a controlled 2×2 between-subjects study with 90 participants (45 dyads) across four conditions (MirrorBot, Bot-only, Mirror-only, and control), we found that MirrorBot led participants to initiate conversation more often, feel greater closeness and togetherness, and have more enjoyable interactions. Our findings position robots not only as social agents but as socio-spatial interfaces that choreograph sight lines and shared attention in physical space, opening new possibilities for technologies that cultivate human connection in public life.2026SGSerena Ge Guo et al.University of Wisconsin-MadisonSocial Robot InteractionPhysical-Digital Hybrid InteractionSmart Cities & Urban SensingCHI
LLMs Homogenize Values in Constructive Arguments on Value-Laden TopicsLarge language models (LLMs) are increasingly used to promote prosocial and constructive discourse online. Yet little is known about how these models negotiate and shape underlying values when reframing people's arguments on value-laden topics. We conducted experiments with 465 participants from India and the United States, who wrote comments on homophobic and Islamophobic threads, and reviewed human-written and LLM-rewritten constructive versions of these comments. Our analysis shows that LLM systematically diminishes Conservative values while elevating prosocial values such as Benevolence and Universalism. When these comments were read by others, participants opposing same-sex marriage or Islam found human-written comments more aligned with their values, whereas those supportive of these communities found LLM-rewritten versions more aligned with their values. These findings suggest that value homogenization in LLM-mediated prosocial discourse runs the risk of marginalizing conservative viewpoints on value-laden topics and may inadvertently shape the dynamics of online discourse.2026FSFarhana Shahid et al.Cornell UniversityHuman-LLM CollaborationAI Ethics, Fairness & AccountabilityAlgorithmic Fairness & BiasCHI
Designing for Wayfinding in VR: Linking Navigation Interfaces to Spatial Learning and Cognitive MappingVarious virtual locomotion techniques and visual transition methods are used in VR-based navigation research, yet few studies have systematically examined their effects on spatial learning, cognitive map formation, and navigational performance in complex indoor environments. We conducted a between-subjects study (N=142) in two high-fidelity VR hospital contexts, including free exploration and task-based wayfinding, while treating locomotion and viewpoint transitions as experimental factors. Spatial learning was measured through pointing, distance estimation, and sketch-map accuracy; performance was measured through completion time and distance traveled; and experience was measured through cybersickness, perceived presence, and usability. Locomotion techniques affected task completion time, with teleportation associated with faster performance in the task-based context. Spatial learning effects were mixed, with patterns indicating that techniques without viewpoint transitions may better support cognitive mapping. Empirical insights and guidelines are provided to improve the reliability and real-world applicability of VR-based wayfinding research.2026AMArmin Mostafavi et al.Cornell UniversityImmersion & Presence ResearchAR Navigation & Context AwarenessVR Medical Training & RehabilitationCHI
From Clicks to Consensus: Collective Consent Assemblies for Data GovernanceObtaining meaningful and informed consent from users is essential for ensuring autonomy and control over one's data. Notice and consent, the standard for collecting consent, has been criticized. While other individualized solutions have been proposed, this paper argues that a collective approach to consent is worth exploring. First, individual consent is not always feasible to collect for all data collection scenarios. Second, harms resulting from data processing are often communal in nature, given the interconnected nature of some data. Finally, ensuring truly informed consent for every individual has proven impractical. We propose collective consent, operationalized through consent assemblies, as one alternative framework. We establish collective consent's theoretical foundations and use speculative design to envision consent assemblies leveraging deliberative mini-publics. We present two vignettes: i) replacing notice and consent, and ii) collecting consent for GenAI model training. Our paper employs future backcasting to identify the requirements for realizing collective consent and explores its potential applications in contexts where individual consent is infeasible.2026LKLin Kyi et al.Max Planck Institute for Security and PrivacyAI-Assisted Decision-Making & AutomationPrivacy by Design & User ControlParticipatory DesignCHI
TALES: A Taxonomy and Analysis of Cultural Representations in LLM-generated StoriesMillions of users across the globe turn to AI chatbots for their creative needs, inviting widespread interest in understanding how they represent diverse cultures. However, evaluating cultural representations in open-ended tasks remains challenging and underexplored. In this work, we present TALES, an evaluation of cultural misrepresentations in LLM-generated stories for diverse Indian cultural identities. First, we develop TALES-Tax, a taxonomy of cultural misrepresentations by collating insights from participants with lived experiences in India through focus groups (N=9) and individual surveys (N=15). Using TALES-Tax, we evaluate 6 models through a large-scale annotation study spanning 2,925 annotations from 108 annotators with lived experience and native language proficiency from across 71 regions in India and 14 languages. Concerningly, we find that 88% of the generated stories contain misrepresentations, and such errors are more prevalent in mid- and low-resourced languages and stories based in peri-urban regions in India. We also transform the annotations into TALES-QA, a standalone question bank to evaluate the cultural knowledge of models.2026KBKirti Bhagat et al.Indian Institute of ScienceHuman-LLM CollaborationAI Ethics, Fairness & AccountabilityLow-Resource Languages & Digital InclusionCHI
Who Gets Written In? Gender, Identity, and Moderation in AO3’s Celebrity FanfictionArchive of Our Own (AO3) is a prominent fanfiction platform widely recognized for its feminist design ethos, with a commitment to inclusive, pluralism and community-driven content creation. Among the content on it, Real Person Fiction (RPF) --- creations based on public figures rather than fictional characters --- offers a unique lens into how users engage with identity, visibility, and cultural narratives. In this study, we conduct a large-scale computational analysis to examine gender representation, thematic diversity, and occupational portrayals. Our findings reveal a significant gender imbalance, with man characters disproportionately over-represented. The readers themselves are also often portrayed as sexual figures. Overall, the relationship portrayals tend to mirror occupational roles, incorporate sexual elements, and reconstruct gender tropes. We interrogate how these patterns intersect with authorship, identity, and power. This work contributes to ongoing conversations about equity, ethics, and feminist values in digital content ecosystems and feminist HCI development.2026PZPeixian Zhang et al.The Hong Kong University of Science and Technology (Guangzhou)Gender & Race Issues in HCITechnology Ethics & Critical HCISocial Platform Design & User BehaviorCHI
Comparing Fabrication Workflows in CAD to Support Design ReasoningWhen novices fabricate, they start by choosing a workflow (e.g., laser cutting, 3D printing, etc.) and corresponding software from a narrow set they know. As they advance their design, another workflow might better suit their intent, but their models remain committed to the original workflow. This prohibits exploration, which fosters informed decision-making. In this paper, we investigate how CAD interfaces can guide exploration and comparison of workflows. Specifically, comparison can advance users' reasoning about design decisions. We developed a prototype interface, CAMeleon, which lets users compare fabrication workflows. Users load 3D models and preview outcomes from different workflows. We hypothesize that presenting alternative outcomes supports exploration and scaffolds informed decision-making. Upon workflow confirmation, CAMeleon allows users export both machine and human instructions for the chosen fabrication workflow. We interviewed seven fabrication educators to understand how such tools can be integrated into teaching and to demonstrate how we adjust our tool based on their insights. In user evaluation (N = 12), guided comparison helped participants consider a broader range of workflows, reflect on trade-offs, and experiment with new ways of planning.2026SFShuo Feng et al.Cornell TechDesktop 3D Printing & Personal FabricationLaser Cutting & Digital FabricationCircuit Making & Hardware PrototypingCHI
AI-Facilitated Coercive Control: An Experimental StudyWe present an experimental study that investigates how LLM-driven conversational AI tools might be weaponized to facilitate, exacerbate, or commoditize coercive control. Inspired by speculative design, we construct four scenarios that combine well-known coercive control tactics with the current capabilities of conversational AI tools. Then, we explore these scenarios via interactions with popular AI agents (ChatGPT, Gemini). We find that although AI tools refuse straightforward requests for harmful content, their guardrails can be circumvented via strategies such as gradual persuasion, splitting conversations, pre-prompting, and manipulating the AI agent's settings. Collectively, these strategies enable AI agents to be leveraged in ways that facilitate harassment, intimidation, gaslighting, monitoring, surveillance, and other coercive control tactics. To make these tools safer for everyone, we discuss opportunities for AI agents to resist being abused for coercive control via analysis of users’ conversational patterns, and ensuring that pre-programmed settings are clearly visible to prevent covert manipulation.2026HKHaesoo Kim et al.Cornell UniversityAgent Personality & AnthropomorphismAI Ethics, Fairness & AccountabilityPrivacy by Design & User ControlCHI
Understanding Remote Mental Health Supporters' Help-Seeking in Online CommunitiesProviding mental health support for loved ones across a geographic distance creates unique challenges for the remote caregivers, who sometimes turn to online communities for peer support. We qualitatively analyzed 522 Reddit threads to understand what drives remote caregivers’ online help-seeking behaviors and the responses they receive from the community. Their purposes of posting included requesting guidance, expressing emotions, and seeking validation. Community responses included providing emotional support, suggesting informational strategies, and sharing personal experiences. While certain themes in posts (emotional toll, monitoring symptoms, and prioritizing caregiver well-being) are shared across remote and non-remote contexts, remote caregivers’ posts surfaced nuanced experiences. For example, they often rely on digital cues, such as voice, to interpret care receivers’ well-being while struggling with digital silence during crises. We discuss the need for supporting communication and information sharing between remote caregivers and receivers, care coordination for crisis management, and design recommendations for caregiver communities.2026TLTuan-He Lee et al.Cornell UniversityMental Health Apps & Online Support CommunitiesTelemedicine & Remote Patient MonitoringElectrical Muscle Stimulation (EMS)CHI
SituFont: A Just-in-Time Adaptive Intervention Interface for Enhancing Mobile Readability in Situational Visual ImpairmentsSituational visual impairments (SVIs) hinder mobile readability, causing discomfort and limiting information access. Building on prior work in adaptive typography and accessibility, this paper presents SituFont, a context-aware and human-in-the-loop adaptive typography adjustment approach that enhances smartphone mobile readability by dynamically adjusting font parameters based on real-time contextual changes. Using smartphone sensors and a human-in-the-loop approach, SituFont personalizes text presentation to accommodate personal factors (e.g., fatigue, distraction) and environmental conditions (e.g., lighting, motion, location). To inform its design, we conducted formative interviews (N=15) to identify key SVI factors and controlled experiments (N=18) to quantify their impact on optimal text parameters. A comparative user study (N=12) across eight simulated SVI scenarios demonstrated SituFont's effectiveness in improving smartphone mobile readability in terms of improved efficiency and reduced workload compared with a non-trivial manual adjustment baseline.2026JCJingruo Chen et al.Cornell UniversityMobile Accessibility DesignBehavior Change & Reflection TechnologyContext-Aware ComputingCHI
Co-Designing Environment-Based Strategies with Neurodivergent Individuals for Sensory-Inclusive Dental Visit ExperiencesDental clinics can be challenging sensory environments, creating discomfort and stress, especially for neurodivergent individuals with Sensory Processing Disorder. Interactive environmental systems offer potential to transform these spaces, providing adaptable, sensory-inclusive experiences. However, the design space for environment-based interventions in dental settings remains largely unexplored. To address this, we conducted in-depth, 2-hour co-design sessions with 13 neurodivergent participants to explore environment-based strategies for meeting diverse sensory needs. We identified five core design goals for inclusive dental environments: experience transformation, distraction, exposure management, restoration, and social facilitation. Our technology-agnostic design catalogue can inform multiple implementation approaches, including projection mapping, ambient displays, and responsive physical elements. We contribute design patterns for interactive environmental systems, methodological insights for participatory design with neurodivergent communities, and demonstrate how tangible materials serve as proxies for environmental interventions, with implications for Augmented Reality system design. This study advances inclusive design practices and highlights co-designing with neurodivergent individuals.2026SGSerena Ge Guo et al.University of Wisconsin-MadisonCognitive Impairment & Neurodiversity (Autism, ADHD, Dyslexia)Inclusive DesignTangible Interaction in EducationCHI
Beyond Community Notes: A Framework for Understanding and Building Crowdsourced Context Systems for Social MediaSocial media platforms are increasingly adopting features that display crowdsourced context alongside posts, a technique pioneered by X's Community Notes. These systems---which we term \textit{Crowdsourced Context Systems} (CCS)---have the potential to reshape the information ecosystem as major platforms embrace them as alternatives to professional fact-checking. To understand the features and implications of these systems, we conduct a systematic literature review of existing CCS research (n=56) and analyze real-world CCS implementations. Based on our analysis, we develop a framework with two components. First, we present a theoretical model to conceptualize and define CCS. Second, we identify a design space encompassing six aspects: participation, inputs, curation, presentation, platform treatment, and transparency. We also surface normative implications of different CCS design and implementation choices. Our work integrates theoretical, design, and ethical perspectives to establish a foundation for future human-centered research on Crowdsourced Context Systems.2026TLTravis Lloyd et al.Cornell UniversityContent Moderation & Platform GovernanceMisinformation & Fact-CheckingParticipatory DesignCHI
Interactive Explainable RankingWe propose an interactive decision-making tool for discovering and exploring explainable rankings for a given set of choices (e.g., job offers, vacation destinations, award candidates). We define an explainable ranking as an ordering of choices based on some consistent weighting of measured criteria. Our tool is designed to help users explore different orderings, criteria, and criterion weights in search of an explainable ranking that reflects their own personal preferences. To achieve this, we combine visualization, optimization, and (optionally) the integration of AI to help users identify and correct or explain inconsistencies in their evaluation of different choices. Through user experiments, we demonstrate that our tool leads to more consistent explainable rankings with greater user confidence.2026CZChao Zhang et al.Cornell UniversityExplainable AI (XAI)Interactive Data VisualizationAI-Assisted Decision-Making & AutomationCHI
UnWEIRDing Peer Review in Human-Computer InteractionPeer review determines which scholarship is legitimized; however, review biases often disadvantage scholarship that diverges from the norm. Human-Computer Interaction (HCI) lacks a systemic inquiry into how such biases affect underrepresented Global South (GS) scholarship. To address this critical gap, we conducted four focus groups with 16 HCI researchers studying the GS. Participants reported experiencing reviews that confined them to development research, dismissed their theoretical contributions, and questioned situated knowledge from GS communities. Both as authors and reviewers, participants reported experiencing the epistemic burden of over-explaining why knowledge from GS communities matters. Further, they noted being tokenized as "cultural experts'' when assigned to review papers and pointed out that the hidden curriculum of writing HCI papers often gatekeeps GS scholarship. Using epistemic oppression as a lens, we discuss how review practices marginalize GS scholarship and outline actionable strategies for nurturing equitable epistemological evaluation of HCI scholarship.2026HNHellina Hailu Nigatu et al.UC BerkeleyDeveloping Countries & HCI for Development (HCI4D)Technology Ethics & Critical HCICHI
Understanding Older Adults’ Experiences of Support, Concerns, and Risks from Kinship-Role AI-Generated InfluencersAI-generated influencers are rapidly gaining popularity on Chinese short-video platforms, often adopting kinship-based roles such as "AI grandchildren'' to attract older adults. Although this trend has raised public concern, little is known about the design strategies behind these influencers, how older adults experience them, and the benefits and risks involved. In this study, we combined social media analysis with interviews to unpack the above questions. Our findings show that influencers use both visual and conversational cues to enact kinship roles, prompting audiences to engage in kinship-based role-play. Interviews further show that these cues arouse emotional resonance, help fulfill older adults’ informational and emotional needs, while also raising concerns about emotional displacement and unequal emotional investment. We highlight the complex relationship between virtual avatars and real family ties, shaped by broader sociocultural norms, and discuss how AI might strengthen social support for older adults while mitigating risks within cultural contexts.2026TSTianqi Song et al.National University of SingaporeAgent Personality & AnthropomorphismSocial Robot InteractionElderly Care & Dementia SupportCHI
Privacy Cards: Surfacing mental models and exploring privacy concerns of voice-first ambient interfacesWe investigate the ethical and privacy implications of voice-first ambient interfaces (VFAIs) for aging in place through an in-depth engagement with five older adults. Our participants were in the process of becoming experienced VFAI users, and had used a VFAI-based design probe for health data reporting. We create and iteratively refine an interview protocol using Privacy Cards. We customize Privacy Cards by drawing on participants’ previous interviews and device usage logs. Using Privacy Cards, we conduct interviews to surface their mental models, and explore their privacy concerns. We find insufficient mental models for proper consent. For example, participants did not know who could access their data, and experienced difficulty distinguishing built-in functionality from third-party apps. Participants initially expressed little worry about VFAI-related ethical concerns, but interviews with Privacy Cards revealed nuanced issues, resulting in various implications for future research and design.2026ACAndrea Cuadra et al.Olin CollegeVoice AccessibilityAging-in-Place Assistance SystemsPrivacy by Design & User ControlCHI
I, Robot? Exploring Ultra-Personalized AI-Powered AAC; an Autoethnographic AccountGeneric AI auto-complete for message composition often fails to capture the nuance of personal identity, requiring editing. While harmless in low-stakes settings, for users of Augmentative and Alternative Communication (AAC) devices, who rely on such systems to communicate, this burden is severe. Intuitively, the need for edits would be lower if language models were personalized to the specific user's communication. While personalization is technically feasible, it raises questions about how such systems affect AAC users’ agency, identity, and privacy. We conducted an autoethnographic study in three phases: (1) seven months of collecting all the lead author’s AAC communication data, (2) fine-tuning a model on this dataset, and (3) three months of daily use of personalized AI suggestions. We observed that: logging everyday conversations reshaped the author’s sense of agency, model training selectively amplified or muted aspects of his identity, and suggestions occasionally resurfaced private details outside their original context. We find that ultra-personalized AAC reshapes communication by continually renegotiating agency, identity, and privacy between user and model. We highlight design directions for building personalized AAC technology that supports expressive, authentic communication.2026TWTobias M Weinberg et al.Cornell TechAugmentative & Alternative Communication (AAC)Privacy & Data Ownership in Self-TrackingGenerative AI (Text, Image, Music, Video)CHI