The Influence of Distributed AI in Trust and Collaboration for Search-and-Rescue TeamsArtificial intelligence (AI) is increasingly deployed in high-stakes domains such as search-and-rescue (SAR), where detections or classifications can shape how teams share information, build trust, and make time-critical decisions. This paper investigates how teams of SAR professionals incorporate AI into their teamwork, highlighting both benefits and challenges. To support this study, we developed the Council of Wizards, a multi-agent Wizard-of-Oz technique that simulates distributed AI systems, enabling scalable and controlled evaluation of collaborative dynamics. Using this novel method, we conducted an experiment with 24 subject-matter experts (SMEs) who reviewed SAR video footage as small teams and made group decisions, with or without AI support. Quantitative results showed that AI-assisted teams reached consensus faster than controls. Qualitative feedback revealed how participants interpreted trust cues, adapted strategies, and sometimes struggled with overload or conflicting detections. Findings illustrate how AI shapes teamwork in SAR and provide design implications for trustworthy distributed human-AI interactions.2026MWMatthew Wilchek et al.Virginia TechAI-Assisted Decision-Making & AutomationTeleoperation & TelepresenceExplainable AI (XAI)CHI
"Having Lunch Now": Understanding How Users Engage with a Proactive Agent for Daily Planning and Self-ReflectionConversational agents have been studied as tools to scaffold planning and self-reflection for productivity and well-being. While prior work has demonstrated positive outcomes, we still lack a clear understanding of what drives these results and how users behave and communicate with agents that act as coaches rather than assistants. Such understanding is critical for designing interactions in which agents foster meaningful behavioral change. We conducted a 14-day longitudinal study with 12 participants using a proactive agent that initiated regular check-ins to support daily planning and reflection. Our findings reveal diverse interaction patterns: participants accepted or negotiated suggestions, developed shared mental models, reported progress, and at times resisted or disengaged. We also identified problematic aspects of the agent's behavior, including rigidity, premature turn-taking, and overpromising. Our work contributes to understanding how people interact with a proactive, coach-like agent and offers design considerations for facilitating effective behavioral change.2026AAAdnan Abbas et al.Virginia Polytechnic Institute & State University (Virginia Tech)Conversational ChatbotsAI-Assisted Decision-Making & AutomationBehavior Change & Reflection TechnologyCHI
Human-Human-AI Triadic Programming: Uncovering the Role of AI Agent and the Value of Human Partner in Collaborative LearningAs AI assistance becomes embedded in programming practice, researchers have increasingly examined how these systems help learners generate code and work more efficiently. However, these studies often position AI as a replacement for human collaboration and overlook the social and learning-oriented aspects that emerge in collaborative programming. Our work introduces human-human-AI (HHAI) triadic programming, where an AI agent serves as an additional collaborator rather than a substitute for a human partner. Through a within-subjects study with 20 participants, we show that triadic collaboration enhances collaborative learning and social presence compared to the dyadic human–AI (HAI) baseline. In the triadic HHAI conditions, participants relied significantly less on AI generated code in their work. This effect was strongest in the HHAI-shared condition, where participants had an increased sense of responsibility to understand AI suggestions before applying them. These findings demonstrate how triadic settings activate socially shared regulation of learning by making AI use visible and accountable to a human peer.2026TDTaufiq Daryanto et al.Virginia TechHuman-LLM CollaborationCollaborative Learning & Peer TeachingParticipatory DesignCHI
From Vulnerable to Resilient: Examining Parent and Teen Perceptions on How to Respond to Unwanted Cybergrooming AdvancesCybergrooming is a form of online abuse that threatens teens' mental health and physical safety. Yet, most prior work has focused on detecting perpetrators’ behaviors, leaving a limited understanding of how teens might respond to such unwanted advances. To address this gap, we conducted an online survey with 74 participants---51 parents and 23 teens---who responded to simulated cybergrooming scenarios in two ways: responses that they think would make teens more vulnerable or resilient to unwanted sexual advances. Through a mixed-methods analysis, we identified four types of vulnerable responses (encouraging escalation, accepting an advance, displaying vulnerability, and negating risk concern) and four types of protective strategies (setting boundaries, directly declining, signaling risk awareness, and leveraging avoidance techniques). As the cybergrooming risk escalated, both vulnerable responses and protective strategies showed a corresponding progression. This study contributes a teen-centered understanding of cybergrooming, a labeled dataset, and a stage-based taxonomy of perceived protective strategies, while offering implications for educational programs and sociotechnical interventions.2026XZXinyi Zhang et al.Virginia TechYouth Online Safety & PrivacyDigital Parenting & Screen Time ManagementMental Health Technology for YouthCHI
PuppetChat: Fostering Intimate Communication through Bidirectional Actions and MicronarrativesAs a primary channel for sustaining modern intimate relationships, instant messaging facilitates frequent connection across distances. However, today's tools often dilute care; they favor single tap reactions and vague emojis that do not support two way action responses, do not preserve the feeling that the exchange keeps going without breaking, and are weakly tied to who we are and what we share. To address this challenge, we present PuppetChat, a dyadic messaging prototype that restores this expressive depth through embodied interaction. PuppetChat uses a reciprocity aware recommender to encourage responsive actions and generates personalized micronarratives from user stories to ground interactions in personal history. Our 10-day field study with 11 dyads of close partners or friends revealed that this approach enhanced social presence, supported more expressive self disclosure, and sustained continuity and shared memories.2026EWEmma Jiren Wang et al.Virginia TechDigital Emotional Expression & TransmissionAffective Human-Computer DialogueEmpathy & Emotional DesignCHI
Does Personalized Nudging Wear Off? A Longitudinal Study of AI Self-Modeling for Behavioral EngagementSustaining the effectiveness of behavior change technologies remains a key challenge. AI self-modeling, which generates personalized portrayals of one’s ideal self, has shown promise for motivating behavior change, yet prior work largely examines short-term effects. We present one of the first longitudinal evaluations of AI self-modeling in fitness engagement through a two-stage empirical study. A 1-week, three-arm experiment (visual self-modeling (VSM), auditory self-modeling (ASM), Control; N=28) revealed that VSM drove initial performance gains, while ASM showed no significant effects. A subsequent 4-week study (VSM vs. Control; N=31) demonstrated that VSM sustained higher performance levels but exhibited diminishing improvement rates after two weeks. Interviews uncovered a catalyst effect that fostered early motivation through clear, attainable goals, followed by habituation and internalization which stabilized performance. These findings highlight the temporal dynamics of personalized nudging and inform the design of behavior change technologies for long-term engagement.2026QHQing He et al.University of PennsylvaniaBehavior Change & Reflection TechnologyHealth Self-TrackingEmotion-Sensing WearablesCHI
Understanding Digital Religion in the Lives of Black Christian Young AdultsChristian communities are increasingly using digital tools to engage their members. However, many young adults are moving away from traditional religious affiliations. This trend is notable among young adult Black Americans, who historically have maintained stronger religious identities than other racial groups. Given these converging trends of strong religious identity, increasing technology use, and the decline in traditional affiliation, we conducted an online survey and semi-structured interviews with Black Christians from 18 to 25 to understand their techno-spiritual practices. We found that while many participants used technology for Bible study and worship, most still valued non-digital aspects of spiritual practice; when watching live-streamed worship, most participants did not actively engage online. Finally, we observed a growing interest in the use of generative AI for spiritual guidance and study. Our findings provide insights in understanding techno-spirituality and spiritual practices for a marginalized young adult population in the United States.2026ASAlexa N. Smith et al.Virginia TechGenerative AI (Text, Image, Music, Video)Human-LLM CollaborationEmpowerment of Marginalized GroupsCHI
A Systematic Review of Interaction Techniques for Mobile Virtual RealityWhile low-cost smartphone-based mobile VR (MVR) improves access to extended reality (XR) technology, it lacks the interaction capabilities of high-end devices. Following PRISMA 2020 methodology, we present a survey of both established and emerging MVR interaction techniques for travel, selection, manipulation, and system control. We reviewed literature from four databases published between 2011–2025 that reported evaluations of MVR interaction techniques. We filtered an initial set of 1041 publications to 64 articles and synthesized the current state of MVR interaction, focusing on cost-accessible approaches. We found many effective low-cost emerging selection and travel techniques, but low-cost object manipulation techniques remain problematic. Acoustic sensing offered superior 3D interaction performance than other sensing modalities while keeping cost low. Our findings inform a novel taxonomy of emerging MVR interaction techniques. We further present a toolkit supporting the design of cost-accessible XR interactions. Our findings underscore practical advantages of DIY approaches to future standalone XR applications developments.2026KGKristen Grinyer et al.Carleton UniversityAR Navigation & Context AwarenessImmersion & Presence ResearchContext-Aware ComputingCHI
"Are we writing an advice column for Spock here?" Understanding Stereotypes in AI Advice for Autistic UsersAutistic individuals sometimes disclose autism when asking LLMs for social advice, hoping for more personalized responses. However, they also recognize that these systems may reproduce stereotypes, raising uncertainty about the risks and benefits of disclosure. We conducted a mixed-methods study combining a large-scale LLM audit experiment with interviews involving 11 autistic participants. We developed a six-step pipeline operationalizing 12 documented autism stereotypes into decision-making scenarios framed as users requesting advice (e.g., “Should I do A or B?”). We generated 345,000 responses from six LLMs and measured how advice shifted when prompts disclosed autism versus when they did not. When autism was disclosed, LLMs disproportionately recommended avoiding stereotypically stressful situations, including social events, confrontations, new experiences, and romantic relationships. While some participants viewed this as affirming, others criticized it as infantilizing or undermining opportunities for growth. Our study illuminates how the intermingling of affirmation and stereotyping complicates the personalization of LLMs.2026CWCaleb Wohn et al.Virginia TechHuman-LLM CollaborationAI Ethics, Fairness & AccountabilityCognitive Impairment & Neurodiversity (Autism, ADHD, Dyslexia)CHI
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
The Wetland Quest: Fostering Empathy and Literacy for Urban Herpetofauna Through VR Wetland ExplorationThis paper investigates how virtual reality (VR) can foster empathy and ecological literacy for urban herpetofauna—reptiles and amphibians often overlooked in conservation. We present The Wetland Quest (TWQ), an immersive VR experience set in a Shanghai wetland that employs embodiment and scale-shift mechanics to situate users in the world of local species. In a mixed-methods study with 62 participants, TWQ significantly improved species literacy and attitudes toward herpetofauna, supported by large quantitative gains and qualitative themes of immersion, empathy, and reduced aversion. This work contributes to HCI and environmental communication by: (1) introducing TWQ as a design case of VR for underrepresented species; (2) providing empirical evidence that immersive perspective-taking can enhance literacy and pro-environmental attitudes; and (3) demonstrating a methodological protocol that combines knowledge tests, validated attitude scales, observations, and interviews, offering a transferable approach for future VR conservation research.2026LXLei Xia et al.Tongji UniversityImmersion & Presence ResearchHuman-Nature Relationships (More-than-Human Design)Sustainable HCICHI
LingoQ: Bridging the Gap between EFL Learning and Work through AI-Generated Work-Related QuizzesNon-native English speakers performing English-related tasks at work struggle to sustain EFL learning, despite their motivation. Often, study materials are disconnected from their work context. Our formative study revealed that reviewing work-related English becomes burdensome with current systems, especially after work. Although workers rely on LLM-based assistants to address their immediate needs, these interactions may not directly contribute to their English skills. We present LingoQ, an AI-mediated system that allows workers to practice English using quizzes generated from their LLM queries during work. LingoQ leverages these on-the-fly queries using AI to generate personalized quizzes that workers can review and practice on their smartphones. We conducted a three-week deployment study with 28 EFL workers to evaluate LingoQ. Participants valued the quality-assured, work-situated quizzes and constantly engaging with the app during the study. This active engagement improved self-efficacy and led to learning gains for beginners and, potentially, for intermediate learners. Drawing on these results, we discuss design implications for leveraging workers' growing reliance on LLMs to foster proficiency and engagement while respecting work boundaries and ethics.2026YYYeonsun Yang et al.DGISTHuman-LLM CollaborationProgramming Education & Computational ThinkingIntelligent Tutoring Systems & Learning AnalyticsCHI
CodeStream: Augmenting Timelines with Code Annotation for Navigating Large Coding HistoriesCode edit histories can offer instructors valuable insight into students’ problem-solving processes, revealing unproductive behaviors that final code alone cannot capture. For example, a correct solution may contain large copy-and-pasted segments (suggesting the code originated elsewhere) or unguided trial-and-error (suggesting a lack of clear strategy). Timelines are a common way to visualize code histories, but existing timeline visualizations of code or document histories show only when and where edits occurred, not what changed. Without this context, it is difficult to answer key questions about how students invested effort or to infer their intentions. We present CodeStream, a visualization system that augments timelines with situational code annotations, whose granularity and visibility dynamically adapt to scale and interaction state. A comparison study shows that CodeStream enables context-aware navigation of coding histories, supporting fast and accurate pattern identification, and helping instructors reason about students’ coding behaviors and identify who may need intervention.2026AZAshley Ge Zhang et al.University of Michigan, Ann ArborInteractive Data VisualizationCollaborative Writing ToolsProgramming Education & Computational ThinkingCHI
When Less Can Be More: Evaluating the Impact of Animated and Interactive Demonstrations in Voice-Assisted Counting Games for Young ChildrenEarly counting forms a critical foundation for numeracy, involving coordination of visual representations, verbal number words, and physical actions such as pointing. Designing effective technologies for young children, therefore requires careful calibration of multimodal features. This study investigated how different levels of demonstrations paired with a voice assistant—static (baseline: image+voice), animated (animation+voice), and interactive (touch+animation+voice)—influence counting-related understanding and engagement in 2–4-year-olds. We developed a tablet-based counting game and conducted a within-subjects study with 32 children. Results showed that animated demonstration improved cardinal number word understanding over both baseline and the interactive demonstration. Analyses of verbal counting engagement showed that concurrent touch demands increased cognitive load, limiting children’s counting aloud. These findings suggest that more interactivity does not always yield better outcomes for young learners. We contribute empirical evidence and design guidance: voice+animation supports early counting, while touch interactivity should be lightweight and age-appropriate, informing the design of multimodal voice-assisted applications.2026SKSulakna Karunaratna et al.Virginia TechEarly Childhood Education TechnologyChild-Computer Interaction DesignVoice User Interface (VUI) DesignCHI
When Hands Meet Physics in Virtual Reality: Effects of Interaction Fidelity on User ExperiencePhysics governs everyday interaction, yet in Virtual Reality (VR) the fidelity of such interactions can diverge from reality. We investigate how Physical Fidelity (virtual object behavior) and Action Fidelity (virtual hand behavior) of physics-driven interaction shape user experience. In a within-subject study (n = 34), participants performed gamified tasks under three conditions: No-Physics (lower Physical and Action Fidelity), Object-Physics (higher Physical, lower Action Fidelity), and Full-Physics (higher Physical and Action Fidelity). Results show that higher Physical Fidelity reduces task efficiency and increases overall workload, with the No-Physics condition outperforming the others in these metrics. When combined with higher Action Fidelity, although efficiency gets even worse in some cases, the Full-Physics condition enhances body ownership and interaction quality. The hybrid Object-Physics condition consistently ranks lowest across all qualitative measures. Interpreting these results through the Interaction Fidelity Model, we offer design implications for VR applications.2026CLChristos Lougiakis et al.National and Kapodistrian University of AthensImmersion & Presence ResearchSocial & Collaborative VRShape-Changing Interfaces & Soft Robotic MaterialsCHI
Effects of Virtual Reality System Fidelity on Presence using the Fidelity-based Presence ScaleNumerous studies have investigated the effects of system fidelity as a whole on one’s total sense of presence in virtual reality (VR). The Fidelity-based Presence Scale (FPS), a recently introduced presence questionnaire, provides a method for investigating the effects of different system fidelities (interaction, scenario, and display) on different aspects of one’s sense of presence. In this paper, we present one of the first studies to investigate those effects for a locomotion task by conducting a 2 × 2 × 2 within-subjects experiment that reveals insight on how the components of system fidelity affect sense of presence. Like recent research, our results indicate that interaction fidelity and display fidelity significantly affect one’s interaction presence and display presence, respectively. However, unlike prior work, we did not find that changes in scenario fidelity significantly affected one’s scenario presence. We discuss other results and the possible implications of this research.2026JBJacob Belga et al.University of Central FloridaImmersion & Presence ResearchCHI
FretFlow: Adaptive Haptics for Rhythm and Articulation in Guitar LearningRhythm and articulation are essential for expressive guitar performance. Existing tools provide basic beat cues, whereas beginners often struggle to align with these cues when playing complex techniques, such as strumming and muting. Informed by a formative study with five instructors and grounded in embodied learning theories, we present FretFlow, a haptic vest-based tool that simulates common instructional practices to guide learners through physical interactions like tapping. The key to FretFlow is its design space that maps rhythmic and articulation patterns in various playing techniques to distinct haptic patterns, enabling authoring of haptic scores. FretFlow further dynamically adapts haptic intensity based on learners' real-time performance accuracy, accompanied by multimodal guidance across haptic, visual, and audio channels. We iteratively refined haptic designs across two rounds with 46 participants, followed by a two-week user study with 20 beginners. Results show that FretFlow improves learners’ rhythmic accuracy and expressive performance.2026XSXin Shu et al.Newcastle UniversityHaptic WearablesBehavior Change & Reflection TechnologyFull-Body Interaction & Embodied InputCHI
CHOIR: A Chatbot-mediated Organizational Memory Leveraging Communication in University Research LabsUniversity research labs often rely on chat-based platforms for communication and project management, where valuable knowledge surfaces but is easily lost in message streams. Documentation can preserve knowledge, but it requires ongoing maintenance and is challenging to navigate. Drawing on formative interviews that revealed organizational memory challenges in labs, we designed CHOIR, an LLM-based chatbot that supports organizational memory through four key functions: document-grounded Q&A, Q&A sharing for follow-up discussion, knowledge extraction from conversations, and AI-assisted document updates. We deployed CHOIR in four research labs for one month (n=21), where the lab members asked 107 questions and lab directors updated documents 38 times in the organizational memory. Our findings reveal a privacy-awareness tension: questions were asked privately, limiting directors' visibility into documentation gaps. Students often avoided contribution due to challenges in generalizing personal experiences into universal documentation. We contribute design implications for privacy-preserving awareness and supporting context-specific knowledge documentation.2026SLSangwook Lee et al.Virginia TechHuman-LLM CollaborationAI-Assisted Decision-Making & AutomationPrivacy by Design & User ControlCHI
Exploring The Impact of Proactive Generative AI Agent Roles In Time-Sensitive Collaborative Problem-Solving TasksCollaborative problem-solving under time pressure is common but difficult, as teams must generate ideas quickly, coordinate actions, and track progress. Generative AI offers new opportunities to assist, but we know little about how proactive agents affect the dynamics of real-time, co-located teamwork. We studied two forms of proactive support in digital escape rooms: a facilitator agent that offered summaries and group structures, and a peer agent that proposed ideas and answered queries. In a within-subjects study with 24 participants, we compared group performance and processes across three conditions: no AI, peer, and facilitator. Results show that the peer agent occasionally enhanced problem-solving by offering timely hints and memory support; however, it also disrupted flow, increased workload, and created over-reliance. In comparison, the facilitator agent provided light scaffolding but had a limited impact on outcomes. We provide design considerations for proactive generative AI agents based on our findings.2026AMAnirban Mukhopadhyay et al.Virginia TechHuman-LLM CollaborationAI-Assisted Decision-Making & AutomationParticipatory DesignCHI
An Empirical Study to Understand How Students Use ChatGPT for Writing EssaysAs large language models (LLMs) become widespread, students increasingly turn to systems like ChatGPT for writing tasks. Educators worry that this reliance may reduce critical engagement with writing and hinder students' learning processes. Although datasets exist on students’ use of LLMs for writing, how they functionally use ChatGPT in detail---and how this usage shapes their writing and perceptions---remains underexplored. We conducted an online study (n=77) in which students wrote an essay using an in-house ChatGPT we developed to capture their queries. Through qualitative analysis, we identified the types of assistance students sought and presented patterns of use, ranging from asking for opinions on a topic to delegating the entire writing task to ChatGPT. We also found that students' writing self-efficacy influenced their querying patterns and that levels of ownership and creativity varied depending on how they used ChatGPT. This study contributes empirical data to ongoing discussions about how writing education should incorporate or regulate LLM-powered tools.2026AJAndrew Jelson et al.Virginia TechHuman-LLM CollaborationAI-Assisted Writing & Text GenerationIntelligent Tutoring Systems & Learning AnalyticsCHI