VoiceAlign: A Shimming Layer for Enhancing the Usability of Legacy Voice User Interface SystemsVoice user interfaces (VUIs) are rapidly transitioning from accessibility features to mainstream interaction modalities. Yet most operating systems' built-in voice commands remain underutilized despite possessing robust technical capabilities. Through our analysis of four commercial VUI systems and a formative study with 16 participants, we found that fixed command formats require exact phrasing, restrictive timeout mechanisms discard input during planning pauses, and insufficient feedback hampers multi-step interactions. To address these challenges, we developed VoiceAlign, an adaptive shimming layer that mediates between users and legacy VUI systems. VoiceAlign intercepts natural voice commands, transforms them to match the required syntax using a large language model, and transmits these adapted commands through a virtual audio channel that remains transparent to the underlying system. In our evaluation with 12 participants, VoiceAlign reduced command failures by half, required 25% fewer commands per task, and significantly lowered cognitive and temporal demands when paired with an existing legacy VUI system. Furthermore, we created a synthetic dataset informed by our studies and fine-tuned a small language model that achieves over 90% accuracy with 200 ms response time when served locally, eliminating dependence on third-party APIs while enabling real-time interaction on edge devices. This work demonstrates how modern AI techniques can unlock the underutilized potential of legacy VUI systems without requiring system modifications, offering a practical solution without replacing existing infrastructure.2026MEMd Ehtesham-Ul-Haque et al.Pennsylvania State UniversityVoice User Interface (VUI) DesignHuman-LLM CollaborationExplainable AI (XAI)IUI
"My Brother Is a School Principal, Earns About $80,000 Per Year... But When the Kids See Me, 'Wow, Uncle, You Have 1,500 Followers on TikTok!'": A Study of Blind TikTokers' Alternative Professional Development ExperiencesOne’s profession is an essential part of modern life. Traditionally, professional development has been criticized for excluding people with disabilities. People with visual impairments, for example, face disproportionately low employment rates, highlighting persistent gaps in professional opportunities. Recently, there has been growing research on social media platforms as spaces for more equitable career development approaches. In this paper, we present an interview study on the professional development experiences of 60 people with visual impairments on TikTok (also known as “BlindTokers”). We report BlindTokers’ goals, strategies, and challenges, supported by detailed examples and in-depth analysis. Based on the findings, we identified that BlindTokers’ practices reveal an alternative professional development approach that is more flexible, inclusive, personalized, and diversified than traditional models. Our study also extends professional development research by foregrounding emerging digital skills and proposing design implications to foster more equitable and inclusive professional opportunities.2026YLYao Lyu et al.University of MichiganSocial Platform Design & User BehaviorCognitive Impairment & Neurodiversity (Autism, ADHD, Dyslexia)Universal & Inclusive DesignCHI
The Impact of Uncertainty Visualization on Trust in Thematic MapsThematic maps are widely used to communicate spatial patterns to non-expert audiences. Although uncertainty is inherent in thematic map data, it is rarely visualized, raising questions about how its inclusion affects trust. Prior work offers mixed perspectives: some argue that uncertainty fosters trust through transparency, while others suggest it may reduce trust by introducing confusion. Yet few empirical studies explicitly measure trust in thematic maps. We conducted a between-subjects experiment (N = 161) to evaluate how visualizing uncertainty at varying levels (low, medium, high) influences trust. We find that uncertainty visualization generally reduces trust, with greater reductions observed as uncertainty levels increase. However, maps dominated by low uncertainty do not significantly differ in trust from those with no uncertainty. Moreover, while uncertainty visualization tends to make readers question the accuracy of the data, it appears to have a weaker influence on perceptions of the mapmaker’s integrity.2026VSVarun Srivastava et al.Arizona State UniversityUncertainty VisualizationGeospatial & Map VisualizationCHI
Giving Meaning to Movements: Challenges and Opportunities in Expanding Communication by Pairing Unaided AAC with Speech Generated MessagesAugmentative and Alternative Communication (AAC) technologies are categorized into two forms: aided AAC, which uses external devices like speech-generating systems to produce standardized output, and unaided AAC, which relies on body-based gestures for natural expression but requires shared understanding. We investigate how to combine these approaches to harness the speed and naturalness of unaided AAC while maintaining the intelligibility of aided AAC, a largely unexplored area for individuals with communication and motor impairments. Through 18 months of participatory design with AAC users, we identified key challenges and opportunities and developed AllyAAC, a wearable system with a wrist-worn IMU paired with a smartphone app. We evaluated AllyAAC in a field study with 14 participants and produced a dataset containing over 600,000 multimodal data points featuring atypical gestures—the first of its kind. Our findings reveal challenges in recognizing personalized, idiosyncratic gestures and demonstrate how to address them using Transformer-based large machine learning (ML) models with different pretraining strategies. In sum, we contribute design principles and a reference implementation for adaptive, personalized systems combining aided and unaided AAC.2026IKImran Kabir et al.Pennsylvania State UniversityElectrical Muscle Stimulation (EMS)Haptic WearablesBehavior Change & Reflection TechnologyCHI
Deaf and Hard of Hearing Access to Intelligent Personal Assistants: Comparison of Voice-Based Options with an LLM-Powered Touch InterfaceWe investigate intelligent personal assistants (IPAs) accessibility for deaf and hard of hearing (DHH) people who can use their voice in everyday communication. The inability of IPAs to understand diverse accents including deaf speech renders them largely inaccessible to non-signing and speaking DHH individuals. Using an Echo Show, we compared the usability of natural language input via two spoken English methods against that of a large language model (LLM)-assisted touch interface in a mixed-methods study. The two spoken English methods consisted of Alexa's built-in automatic speech recognition and a Wizard-of-Oz setting with a trained facilitator re-speaking commands. The touch method was navigated through an LLM-powered ‘task prompter,’ which integrated the user's history and smart environment to suggest contextually-appropriate commands. Quantitative results showed no significant differences across both spoken English conditions vs LLM-assisted touch. Qualitative results showed variability in opinions on the usability of each method. Ultimately, it will be necessary to have robust deaf-accented speech recognized natively by IPAs.2026PDPaige S DeVries et al.Gallaudet UniversityVoice AccessibilityIntelligent Voice Assistants (Alexa, Siri, etc.)Human-LLM CollaborationCHI
"It's totally pointless, but it's fun”: How BeReal Fits into Young Adults’ Social Media Ecosystem and Friendships.The social media app BeReal positions itself as a space for meaningful connections, however, little is known about how the app’s unique combination of ephemerality, informality, and improvisation actually supports relationship maintenance. We aimed to understand what role BeReal plays in young adults’ friendships and lives. Drawing on interviews with 31 young adults at a large university in the northeastern U.S., we find that users treated BeReal as a fun, low-effort space to share glimpses of everyday life with smaller networks of friends. BeReal helped users maintain relationships, especially with past or geographically distant friends but not necessarily deepen bonds with close friends. Users welcomed the app’s minimalistic user experience but raised doubts about the platform’s longevity. Based on our findings, we present the Social Media Effort (SME) heuristic to help designers and researchers visualize how content and audience shape the social media ecosystem. We advocate that the HCI community design new platforms, since dominant business models are not poised to support relationship maintenance.2026PKPriya C. Kumar et al.Pennsylvania State UniversitySocial Platform Design & User BehaviorOnline Identity & Self-PresentationCHI
Cultural Variations in Human-AI Partnership: Initial Cross-Cultural Validation of the Transactive Memory System with GenAI (TMS-GenAI) Measurement ToolThis study introduces and examines the Transactive Memory System with GenAI (TMS-GenAI) measurement tool, an instrument designed to capture transactive memory processes in human–AI partnership. Drawing from Transactive Memory System theory, Extended Mind theory, and Cognitive Self-Esteem, the tool encompasses six theoretically grounded dimensions: Ability to Think, Ability to Remember, Specialization, Coordination, Credibility, and Generative AI Offloading. Using exploratory factor analysis across culturally distinct samples (Turkiye, N=437; United States, N=476), we evaluated structural consistency and cultural divergence of these dimensions. Results indicate strong cross-cultural stability for self-evaluative and offloading constructs (Ability To Think, Ability to Remember, and Generative AI Offloading), alongside culturally specific structuring of teamwork-related dimensions. As the first phase of a multi-stage development process, this study provides foundational evidence for the TMS-GenAI measurement tool. Future research employing confirmatory factor analysis, measurement invariance testing, and item refinement will enable its progression into a validated scale. The findings offer practical insights for assessing human–AI partnership across educational, professional, and organizational contexts.2026MAMahir Akgun et al.Pennsylvania State UniversityHuman-LLM CollaborationCross-Cultural Usability ResearchParticipatory DesignCHI
Prompt Coaching for Inclusiveness: A Media Literacy Approach to Increase Users’ Awareness of Algorithmic Bias and Prompting EfficacyLarge language models often produce biased or stereotypical outputs. One way to reduce this possibility is to be more inclusive in our prompts, but doing so may not come naturally to most users. Therefore, we designed a tool that coaches users to write more inclusive prompts—a strategy that leverages design friction to provide a media literacy intervention. Data from a user study (N=344) show that compared to no coaching, inclusive prompt coaching directly increased users’ awareness of algorithmic bias and their perceived prompting efficacy. It also indirectly enhanced their trust in the system and perceived trust calibration through cognitive elaboration. However, inclusive prompt coaching resulted in a less satisfying user experience. These findings have implications for ethical interventions in prompting for better communicating and combating algorithmic bias. We discuss the benefits and limitations of inclusive prompt coaching, as well as ways to balance usability for long-term adoption of generative AI systems.2026CCCheng Chen et al.Oregon State UniversityHuman-LLM CollaborationAI Ethics, Fairness & AccountabilityInclusive DesignCHI
Interaction Context Often Increases Sycophancy in LLMsWe investigate how the presence and type of interaction context shapes sycophancy in LLMs. While real-world interactions allow models to mirror a user's values, preferences, and self-image, prior work often studies sycophancy in zero-shot settings devoid of context. Using two weeks of interaction context from 38 users, we evaluate two forms of sycophancy: (1) agreement sycophancy -- the tendency of models to produce overly affirmative responses, and (2) perspective sycophancy -- the extent to which models reflect a user's viewpoint. Agreement sycophancy tends to increase with the \textit{presence} of user context, though model behavior varies based on the context \textit{type}. User memory profiles are associated with the largest increases in agreement sycophancy (e.g. +45% for Gemini 2.5 Pro), and some models become more sycophantic even with non-user synthetic contexts (e.g. +15% for Llama 4 Scout). Perspective sycophancy increases only when models can accurately infer user viewpoints from interaction context. Overall, context shapes sycophancy in heterogeneous ways, underscoring the need for evaluations grounded in real-world interactions and raising questions for system design around alignment, memory, and personalization.2026SJShomik Jain et al.Massachusetts Institute of TechnologyHuman-LLM CollaborationAI-Assisted Decision-Making & AutomationAI Ethics, Fairness & AccountabilityCHI
Teen Vigilance: Navigating Risky Social Interactions on DiscordTeenagers are avid users of Discord, a fast-growing platform for synchronous communication where they often interact with strangers. Because Discord combines private DMs, semi-private voice channels, and public servers in one place, it creates a hybrid environment that can produce complex—and underexplored—safety risks for teenagers. Drawing on 16 interviews with teenage Discord users, this study examines their strategies for navigating risky social interactions in the platform. Our findings reveal that when teenagers encounter risks during social interactions, they exercise vigilance by evaluating suspicious interactions before forming friendships, using safety tools, and engaging in controlled risk-taking to safeguard their privacy and security. At the community level, they mitigate risks through selective participation in servers, a practice supported by vigilant governance structures. We discuss how vigilance enables teenagers to act during risky encounters to protect themselves, advancing understanding of teenagers’ agency in risk navigation and informing teen-centered designs for safer online environments.2026EKElena Koung et al.The Pennsylvania State UniversityYouth Online Safety & PrivacyOnline Harassment & Counter-ToolsSocial Platform Design & User BehaviorCHI
"I'm Constantly Getting Comments Like, 'Oh, You're Blind. You're Like the Only Woman That I Stand a Chance With.'": A Study of Blind TikTokers' Intersectional Experiences of Gender and SexualitySocial media platforms are important venues for identity expression, and the Human-Computer Interaction community has been paying growing attention to how marginalized groups express their identities on these platforms. Joining the emerging literature on intersectional experiences, we study blind TikTokers (“BlindTokers”) who are also women and/or LGBTQ+. Using interview data from 41 participants, we identify their intersectional experiences as mediated by TikTok’s socio-technical affordances. We argue that BlindTokers’ intersectional marginalization is infrastructural: TikTok’s classification and moderation features interact with social norms in ways that push them aside and distort how they are treated on the platform. We use this infrastructure perspective to understand what these experiences are, how they were formed, and how they become harmful. We further recognize participants’ infrastructuring work to address these problems. This study guides future social media design with accessible creator tools, inclusive identity options, and context-aware moderation developed in partnership with communities.2026YLYao Lyu et al.University of MichiganSocial Platform Design & User BehaviorGender & Race Issues in HCIEmpowerment of Marginalized GroupsCHI
Surveillance as Care: Configuring Baby Monitors in the HomeWith the development of consumer surveillance technologies, monitoring has become increasingly accessible and woven into family life. Prior work has examined parents’ attitudes, privacy concerns, and selected uses of surveillance technologies like smart cameras and location-tracking apps, but offers limited accounts of how parents, as surveillants, configure and experience these technologies themselves in daily parenting. We address this gap by focusing on baby monitoring technologies (BMTs) as a high-salience context during a sensitive stage of family life. Using inductive thematic analysis of Reddit discussions, we examine how parents engage with BMTs in practice. Our findings revealed how parents actively assemble and configure BMTs, navigate and manage their emotions through them, negotiate privacy frictions and boundaries, and safeguard security in their use of such technologies within parenting and caregiving. We conclude by discussing implications for surveillance research and design for monitoring technologies in care.2026QSQiurong Song et al.The Pennsylvania State UniversitySmart Home Privacy & SecurityYouth Online Safety & PrivacyDigital Parenting & Screen Time ManagementCHI
Why Creators Break Rules: Quantitative Evidence on Moral Disengagement and Self-ControlSocial video platforms such as YouTube and Twitch increasingly moderate noncompliant video content, yet we know little about what psychological factors drive creators to produce such videos. Drawing on theories of self-control and moral disengagement, we examine how self-control and moral disengagement influence creators' production of noncompliant videos, their moderation experience, and, once moderated, their perceived fairness of moderation decisions and their coping strategies. By analyzing data from a survey with 400 video creators, we find that moral disengagement increases the creation of noncompliant videos and moderation experience, while self-control reduces them. Self-control and moral disengagement influence creators’ adoption of coping strategies in response to moderation decisions, but in distinct ways. The effects of moral disengagement and self-control are moderated by creators' reliance on video creation for income. These findings offer a fuller account of why creators offend. We discuss implications for better supporting punished creators’ behavioral improvement.2026YLYao Li et al.University of Central FloridaSocial Platform Design & User BehaviorContent Moderation & Platform GovernanceCyberbullying & Online HarassmentCHI
Privacy Control in Conversational LLM Platforms: A Walkthrough StudyLarge language models (LLMs) are increasingly integrated into daily life through conversational interfaces, processing user data via natural language inputs and exhibiting advanced reasoning capabilities, which raises new concerns about user control over privacy. While much research has focused on potential privacy risks, less attention has been paid to the data control mechanisms these platforms provide. This study examines six conversational LLM platforms, analyzing how they define and implement features for users to access, edit, delete, and share data. Our analysis reveals an emerging paradigm of data control in conversational LLM platforms, where user data is generated and derived through interaction itself, natural language enables flexible yet often ambiguous control, and multi-user interactions with shared data raise questions of co-ownership and governance. Based on these findings, we offer practical insights for platform developers, policymakers, and researchers to design more effective and usable privacy controls in LLM-powered conversational interactions.2026ZLZhuoyang LI et al.Eindhoven University of TechnologyExplainable AI (XAI)Privacy by Design & User ControlPrivacy Perception & Decision-MakingCHI
What Happened to Scenario-Based Design in HCI?: A Scoping ReviewScenario-based design (SBD) was developed in human-computer interaction (HCI) in the early 1990s. It recommended the use of scenarios (narratives that describe episodes of human activity) as a focal tool for describing, analyzing, designing, and developing new human-computer interactions. In this context, a scenario describes the appropriation of a design that has not yet been implemented. SBD argues that narratives can serve as powerful design tools for HCI professionals, facilitating evocative but low-fidelity envisioning of design ideas, encouraging critical and imaginative analysis throughout design, highlighting human values and perspectives to designers, and enabling diverse stakeholder participation in design activities. SBD was developed in the SIGCHI community and has been used for 35 years. We examine the role of SBD over time, focusing on the decade 2015-2025. SBD has continued to develop design and method themes as in previous decades, but has also incorporated new themes, including speculative design and ethics.2026JCJohn M. Carroll et al.Pennsylvania State UniversityParticipatory DesignDesign FictionTechnology Ethics & Critical HCICHI
What is Safety? Corporate Discourse, Power, and the Politics of Generative AI SafetyThis work examines how leading generative artificial intelligence companies construct and communicate the concept of "safety" through public-facing documents. Drawing on critical discourse analysis, we analyze a corpus of corporate safety-related statements to explicate how authority, responsibility, and legitimacy are discursively established. These discursive strategies consolidate legitimacy for corporate actors, normalize safety as an experimental and anticipatory practice, and push a perceived participatory agenda toward safe technologies. We argue that uncritical uptake of these discourses risks reproducing corporate priorities and constraining alternative approaches to governance and design. The contribution of this work is twofold: first, to situate safety as a sociotechnical discourse that warrants critical examination; second, to caution human-computer interaction scholars against legitimizing corporate framings, instead foregrounding accountability, equity, and justice. By interrogating safety discourses as artifacts of power, this paper advances a critical agenda for human-computer interaction scholarship on artificial intelligence.2026ADAnkolika De et al.Pennsylvania State UniversityGenerative AI (Text, Image, Music, Video)AI Ethics, Fairness & AccountabilityTechnology Ethics & Critical HCICHI
Red Teaming LLMs as Socio-Technical Practice: From Exploration and Data Creation to EvaluationRecently, red teaming, with roots in security, has become a key evaluative approach to ensure the safety and reliability of Generative Artificial Intelligence. However, most existing work emphasizes technical benchmarks and attack success rates, leaving the socio-technical practices of how red teaming datasets are defined, created, and evaluated under-examined. Drawing on 22 interviews with practitioners who design and evaluate red teaming datasets, we examine the data practices and standards that underpin this work. Because adversarial datasets determine the scope and accuracy of model evaluations, they are critical artifacts for assessing potential harms from large language models. Our contributions are first, empirical evidence of practitioners conceptualizing red teaming and developing and evaluating red teaming datasets. Second, we reflect on how practitioners’ conceptualization of risk leads to overlooking the context, interaction type, and user specificity. We conclude with three opportunities for HCI researchers to expand the conceptualization and data practices for red-teaming.2026AGAdriana Alvarado Garcia et al.IBM ResearchExplainable AI (XAI)AI Ethics, Fairness & AccountabilityAlgorithmic Transparency & AuditabilityCHI
Collective Privacy Sensemaking of Everyday Lived Experiences: A Study of Reddit and Discord Teen Communities Teenagers regularly use social media to connect and share information with peers. While much existing research focuses on the adverse impacts of social media on teens' privacy and well-being, little research has examined how teens' privacy could be strengthened through participating in online peer communities. Through a qualitative analysis of conversations in two teen-oriented communities on Reddit and Discord, we explore how teens leverage storytelling and conversations with peers to unpack privacy dilemmas in their lives. Our findings highlight the potential of these online interactions to help teens cope with privacy violations, make sense of complex social matters, and nurture their sense of agency. We recommend platform design directions to explore the implications of collective sensemaking in peer-driven online contexts, and call for a broader conceptualization of youth privacy and research on privacy literacy.2026HDHongyi Dong et al.Pennsylvania State UniversityPrivacy Perception & Decision-MakingYouth Online Safety & PrivacyUser Research Methods (Interviews, Surveys, Observation)CHI
Surveillance, Spacing, Screaming and Scabbing: How Digital Technology Facilitates Union BustingDespite high approval ratings for unions and growing worker interest in organizing, employees in the United States still face significant barriers to securing collective bargaining agreements. A key factor is employer counter-organizing: efforts to suppress unionization through rule changes, retaliation, and disruption. Designing sociotechnical tools and strategies to resist these tactics requires a deeper understanding of the role computing technologies play in counter-organizing against unionization. In this paper, we examine three high-profile organizing efforts–at Amazon, Starbucks, and Boston University–using publicly available sources to identify four recurring technological tactics: surveillance, spacing, screaming and scabbing. We analyze how these tactics operate across contexts, highlighting their digital dimensions and strategic deployment. We conclude with implications for organizing in digitally-mediated workplaces, directions for future research, and emergent forms of worker resistance.2026FRFrederick Reiber et al.Boston UniversityTechnology Ethics & Critical HCIImpact of Automation on WorkOnline Harassment & Counter-ToolsCHI
Understanding Parents’ Perspectives on Responsible AI for Children’s Self-Directed LearningGenerative AI is increasingly present in children’s learning environments, yet little is known about how families navigate this technology in middle childhood (ages 7–13), when parental guidance remains strong but children seek independence. \rev{Drawing on self-directed learning (SDL), we explore how parents in our exploratory sample perceived children’s emerging self-directness and agency.} Through focus groups with 13 parent–child pairs, we examine parents’ views on children’s AI literacy development, readiness factors, and mediation strategies. Parents described emergent pathways shaped by screen time, self-directness, and knowledge growth. They often confined AI to learning-only contexts, positioning it as a tutor while overlooking non-learning uses and risks such as privacy and infrastructural embedding. Many acknowledged limited AI literacy and turned to joint engagement as opportunities for co-learning. Our findings surface possible parental pathways of children’s AI literacy, highlight gaps between pragmatic expectations and critical literacies, and offer situated design considerations for AI systems that scaffold SDL while balancing oversight with autonomy.2026JXJingyi Xie et al.San José State UniversityChildren's AI Literacy & Data LiteracyHuman-LLM CollaborationCognitive Impairment & Neurodiversity (Autism, ADHD, Dyslexia)CHI