Semantic See-through Goggles: Wearing Linguistic Virtual Reality in (Artificial Intelligence)When language is used as a medium for sensory information—as when one describes a scene in words—a kind of virtual reality emerges: realities projected into the same sentence become virtual/equivalent. We call this Linguistic VR (LVR). LVR is constituted by codes shaped chiefly by majority cultural norms. In today’s world—where AI, largely built on linguistic data and processes, is deeply entangled with the everyday mediation of sensory information—it is necessary to critically re-examine the virtuality of this VR. We propose Semantic See-through Goggles, a system that makes manifest, as a first-person experience, the LVR latent in the linguistic mediation of scenes, enabling intuitive understanding and analysis of its properties and issues. The system inserts a serial image-to-text and text-to-image transformation pipeline between the camera and head-mounted display (HMD) of video see-through goggles: the live view is converted into a single line of text, then re-generated as an image, and only this mediated image reaches the wearer’s eyes. We built a prototype and validated its basic properties, followed by a qualitative analysis of wearer experiences. The results suggest that this method enables subjective experience—and thus understanding—of the transformations of environmental information, and of the attendant issues, induced by the linguistic mediation of vision. We also obtained preliminary insights into both AI-driven linguistic mediation of sensory information and problems intrinsic to linguistic mediation itself.2026GMGoki Muramoto et al.The University of TokyoGenerative AI (Text, Image, Music, Video)Human-LLM CollaborationExplainable AI (XAI)IUI
TimeMarbles: A More Holistic Approach to Self-Reflecting on Focus in the Knowledge WorkplaceDigital tools promoting individual focus are increasingly popular in knowledge work. Yet their narrow framing of attention as a binary of focus versus non-focus can be unsustainable and discourage engagement in other vital activities, such as team coordination and collaboration. We introduce TimeMarbles, a web app that encourages more holistic self-reflection by tracking three modes of focus: high-focus, normal-focus, and break, as well as a team vs. individual dimension. In a two-week comparative structured observation in the field with 24 knowledge workers across six countries, we explored how users experience TimeMarbles vs. a more traditional focus-centric web app. Our thematic analysis shows that participants felt more positive about their day when tracking their time in TimeMarbles and that, despite the added logging effort, they preferred the more granular approach because it better represented the range of different attention and activities that characterize their workday. Our work points toward re-imagining digital workplace time-tracking tools to better support worker wellbeing.2026ARAnastasia Ruvimova et al.University of ZurichKnowledge Worker Tools & WorkflowsWorkplace Wellbeing & Work StressBehavior Change & Reflection TechnologyCHI
RealTwin: Concept Graph Representation and Grounding Framework for Reality-Preserving Digital Twin ReconstructionReconstructing realistic digital twins has become crucial as advances in mixed reality, metaverse, and robotics demand more accurate simulations for the physical world. Despite technical progress, building high-fidelity digital twins from a systematic and human-centered perspective remains underexplored. Drawing from the human processing model, we decompose human-centric reality into perception, motion, and cognition, and define a reality-preserving digital twin (RPDT) as a reconstruction integrating these dimensions. We present RealTwin, an attribute-graph-based representation and inference framework for RPDT. Leveraging the grounding capabilities of Multimodal Large Language Models (MLLMs), RealTwin chains AI tools to construct attribute graphs that faithfully encode real-world properties. We validate RealTwin through both technical evaluation, showing promising success in graph parsing and attribute inference, and a user study, assessing its applicability across diverse user groups. Enlightened by RealTwin, we discuss critical issues, including ecology, interaction space, and real-world adoption, for future end-to-end, fine-grained, and scalable digital twin reconstruction.2026ZLZisu Li et al.The Hong Kong University of Science and TechnologyImmersion & Presence ResearchMixed Reality WorkspacesHuman-LLM CollaborationCHI
Draped Surfaces: A Contour-Adaptive Interface Overlaid on the Physical Environment for Mixed Reality WorkspacesConventional Mixed Reality (MR) workspaces are frequently organized in cockpit-like layouts, where multiple floating windows surround the user. While this configuration facilitates access to digital content, it often induces occlusion, reducing understanding of the physical environment and limiting access to real-world objects. To overcome this challenge, we present the Contour-Adaptive Mixed Environment Overlays (CAMEO), a contour-adaptive MR interface that drapes virtual windows onto physical surfaces. This design integrates digital content with nearby items, thereby improving users’ visual access to background objects and supporting interaction with them. We evaluate CAMEO in two controlled studies. The first demonstrates that draping reduces hand-movement detours relative to flat mid-air surfaces, enabling more direct interaction with nearby items. The second shows that controlled window deformation does not significantly impair text legibility when compared to flat surfaces. Together, these findings contribute a novel design paradigm for MR workspaces that balances immersion, readability, and environmental understanding.2026SKSoonUk Kwon et al.University of British Columbia, OkanaganMixed Reality WorkspacesPhysical-Digital Hybrid InteractionCHI
The AI Genie Phenomenon and Three Types of AI Chatbot Addiction: Escapist Roleplays, Pseudosocial Companions, and Epistemic Rabbit HolesRecent reports on generative AI chatbot use raise concerns about its addictive potential. An in-depth understanding is imperative to minimize risks, yet AI chatbot addiction remains poorly understood. This study examines how to characterize AI chatbot addiction---why users become addicted, the symptoms commonly reported, and the distinct types it comprises. We conducted a thematic analysis of Reddit entries (n=334) across 14 subreddits where users narrated their experiences with addictive AI chatbot use, followed by an exploratory data analysis. We found: (1) users' dependence tied to the "AI Genie" phenomenon---users can get exactly anything they want with minimal effort---and marked by symptoms that align with addiction literature, (2) three distinct addiction types: Escapist Roleplay, Pseudosocial Companion, and Epistemic Rabbit Hole, (3) sexual content involved in multiple cases, and (4) recovery strategies' perceived helpfulness differ between addiction types. Our work lays empirical groundwork to inform future strategies for prevention, diagnosis, and intervention.2026MSM. Karen Shen et al.University of British ColumbiaGenerative AI (Text, Image, Music, Video)AI Ethics, Fairness & AccountabilityEmpathy & Emotional DesignCHI
AI Twin: Enhancing ESL Speaking Practice through AI Self-Clones of a Better MeAdvances in AI have enabled ESL learners to practice speaking through conversational systems. However, most tools rely on explicit correction, which can interrupt the conversation and undermine confidence. Grounded in second language acquisition and motivational psychology, we present AI Twin, a system that rephrases learner utterances into more fluent English and delivers them in the learner's voice. Embodying a more confident and proficient version of the learner, AI Twin reinforces motivation through alignment with their aspirational Ideal L2 Self. Also, its use of implicit feedback through rephrasing preserves conversational flow and fosters an emotionally supportive environment. In a within-subject study with 20 adult ESL learners, we compared AI Twin with explicit correction and a non-personalized rephrasing agent. Results show that AI Twin elicited higher emotional engagement, with participants describing the experience as more motivating. These findings highlight the potential of self-representative AI for personalized, psychologically grounded support in ESL learning.2026MPMinju Park et al.University of British ColumbiaHuman-LLM CollaborationIntelligent Voice Assistants (Alexa, Siri, etc.)Intelligent Tutoring Systems & Learning AnalyticsCHI
Crystallizing Schemas with Teleoscope: Thematic Curation of Large Text Corpora on RedditLarge text corpora, such as Reddit posts, have become an increasingly prevalent site of qualitative inquiry. However, most large text corpora are intractable for qualitative researchers. Instead, teams rely on statistical subsampling to reduce corpora to a manageable size for qualitative analysis. While previous work for navigating large corpora involves visualizing the dataset at the corpus-level using high-level statistical summaries, few systems offer the ability to curate data using an interpretivist approach. To address this, we developed Teleoscope, a web-based interface designed to scaffold iterative, interactive, and reflexive refinement of a large corpus, in a process we call thematic curation. Across three deployments, we learned that Teleoscope supports serendipitous discovery of new keywords, results in greater feelings of confidence in search saturation, and aids collaborative discussion of alternative curation pathways. Teleoscope empowers researchers to stay "close to the data" in order to make qualitative workflows methodologically coherent with large text corpora.2026PLPatrick Yung Kang Lee et al.University of TorontoUser Research Methods (Interviews, Surveys, Observation)Prototyping & User TestingExploratory Search & Information SeekingCHI
GhostUI: Unveiling Hidden Interactions in Mobile UIModern mobile applications rely on hidden interactions—gestures without visual cues like long presses and swipes—to provide functionality without cluttering interfaces. While experienced users may discover these interactions through prior use or onboarding tutorials, their implicit nature makes them difficult for most users to uncover. Similarly, mobile agents—systems designed to automate tasks on mobile user interfaces, powered by vision language models (VLMs)—struggle to detect veiled interactions or determine actions for completing tasks. To address this challenge, we present GhostUI, a new dataset designed to enable the detection of hidden interactions in mobile applications. GhostUI provides before-and-after screenshots, simplified view hierarchies, gesture metadata, and task descriptions, allowing VLMs to better recognize concealed gestures and anticipate post-interaction states. Quantitative evaluations with VLMs show that models fine-tuned on GhostUI outperform baseline VLMs, particularly in predicting hidden interactions and inferring post-interaction screens, underscoring GhostUI's potential as a foundation for advancing mobile task automation.2026MKMinkyu Kweon et al.Seoul National UniversityMobile App User ExperienceOne-Handed Operation & Mobile GesturesHuman-LLM CollaborationCHI
LLM-based Embodied Conversational Agent for Reducing Foreign Language Speaking Anxiety in Social VRForeign language speaking anxiety (FLSA) poses a major challenge for English-language learners, suppressing confidence and triggering a cycle of avoidance that hinders language acquisition. To address this, we explored the use of LLM-based embodied conversational agents (ECA) in social virtual reality (VR), which provide personalized support and multimodal interaction in a contextualized environment. We developed three English-language learning scenarios in social VR and conducted a five-day mixed-methods study where participants (N=20) engaged in daily 30-minute role-play practice with an LLM-based ECA to evaluate the efficacy of the system. Quantitative results showed a significant reduction in self-reported FLAS after 3 days, along with subtle gains in speaking proficiency measures. Qualitatively, learners perceived increased confidence, attributing it to the LLM-based ECA's non-judgmental stance, linguistic scaffolding, affective encouragement, and adaptive feedback. Our findings suggest the potential of LLM-based ECAs in social VR for language learning and offer considerations for future agent design.2026MPMengxu Pan et al.Northeastern UniversityHuman-LLM CollaborationSocial & Collaborative VRImmersion & Presence ResearchCHI
The Words That Can't Be Shared: Exploring the Design of Unsent MessagesPeople often have things they want to say but hold back in conversations, fearing being vulnerable or facing social consequences. Online, this restraint can take a distinctive form: even when such thoughts are written out - in moments of anger, guilt, or longing - people may choose to withhold them, leaving them unsent. This process is underexamined; we investigate the experience of writing such messages within people's digital communications. We find that unsent messages become expressive containers for suppressed feelings, where the act of writing creates a pause for reflection on the relationship and oneself. Building on these insights, we probed into how the design of the writing platforms of unsent messages affects people's experiences and motivations. Speculating with participants on nine evocative variants of a note-taking platform, we highlight how design shapes the emotional, temporal, and ritualistic qualities of unsent messages, revealing tensions between people's social desires and communicative actions.2026MYMichael Yin et al.University of British ColumbiaEmpathy & Emotional DesignTangible User Interface DesignAffective Human-Computer DialogueCHI
Advancing Inclusive Digital Well-Being Tools: How Neurodivergent Students Use Distraction BlockersNeurodivergent students bring diverse cognitive styles and work patterns, and they are often a key audience for digital distraction blockers aimed at managing attention. However, it remains unclear whether these tools are grounded in their lived experiences, raising concerns that tool design may overlook neurodivergent practices and inadvertently reinforce neuronormative perspectives. We conducted semi-structured interviews with 27 post-secondary students with ADHD, Autism Spectrum Disorder, and/or Generalized Anxiety Disorder to examine how they use distraction blockers. Our thematic analysis shows how neurodivergent students adapt blockers for regulating stimulation levels, but also encounter tensions between their work rhythms and tool design rooted in fixed, linear time structures, which may exacerbate self-stigmatizing comparisons. We call for distraction blockers that empower neurodivergent strengths by normalizing and scaffolding diverse ways of working, such as hyperfocus and non-linear workflows, and help navigate known tensions between flexibility and structure towards more inclusive digital well-being tools.2026MHMarvel Chrismatheo Hariadi et al.University of British ColumbiaCognitive Impairment & Neurodiversity (Autism, ADHD, Dyslexia)Aging-Friendly Technology DesignBehavior Change & Reflection TechnologyCHI
Cloning the Self for Mental Well-Being: A Framework for Designing Safe and Therapeutic Self-Clone ChatbotsAs digital tools increasingly mediate mental health care, self-clone chatbots can offer a uniquely novel approach to intra-personal exploration and self-derived support. Trained to replicate users’ conversational patterns, self-clones allow users to talk to themselves through their digital replicas. Despite the promises, these systems may carry risks around identity confusion, negative reinforcement, and blurred user agency. Through interviews with 16 mental health professionals and 6 general users, we aim to uncover tensions and design opportunities in this emerging space to guide responsible self-clone design. Our analysis produces a design framework organized around three priorities: (1) defining goals and grounding the approach in existing therapeutic models, (2) design dimensions including the self-clone persona and user-clone relationship dynamics, and (3) considerations for minimizing potential emotional and ethical harms. This framework contributes an interdisciplinary foundation for designing self-clone chatbots as AI-mediated self-interaction tools that are emotionally and ethically attuned in mental health contexts.2026MSMehrnoosh Sadat Shirvani et al.University of British ColumbiaConversational ChatbotsAffective Human-Computer DialogueMental Health Apps & Online Support CommunitiesCHI
Reflective Motion and a Physical Canvas: Exploring Embodied Journaling in Virtual RealityIn traditional journaling practices, authors express and process their thoughts by writing them down. We propose a somaesthetic-inspired alternative that uses the human body, rather than written words, as the medium of expression. We coin this embodied journaling, as people's isolated body movements and spoken words become the canvas of reflection. We implemented embodied journaling in virtual reality and conducted a within-subject user study (n=20) to explore the emergent behaviours from the process, comparing its expressive and reflective qualities to those of written journaling. When writing-based norms and affordances were absent, we found that participants defaulted towards unfiltered emotional expression, often forgoing words altogether. Rather, subconscious body motion and paralinguistic acoustic qualities unveiled deeper, sometimes hidden feelings, prompting reflection that happens after emotional expression rather than during it. We discuss both the capabilities and pitfalls of embodied journaling, ultimately challenging the idea that reflection culminates in linguistic reasoning.2026MYMichael Yin et al.University of British ColumbiaFull-Body Interaction & Embodied InputImmersion & Presence ResearchEmpathy & Emotional DesignCHI
Quantifying Latencies: A Conversation Analysis Approach to Human-Agent Interactions in Virtual RealityUsers feel frustrated when they do not know when to speak with LLM-based agents. Technical delays disrupt the natural rhythm of conversation (turn-taking), yet there is little understanding of how these specific delays impact the back-and-forth flow of interaction. To address this, we analyzed human-agent conversations in social VR to measure timing differences. We used conversation analysis techniques to track specific timing metrics, such as how long it takes to respond (response latencies) and how agents handle interruptions (repair attempts). We found that agents are significantly slower to respond with a median of 4.1 seconds compared to a human's 1.2 seconds. We identified a "conversational timing drift", noting that agents struggle with start-up latency, i.e., taking too long to start speaking, and wind-down latency, i.e., failing to stop speaking quickly when a user interrupts them. This is the first study to empirically quantify human-agent conversational latencies within VR. We offer design suggestions to help future agents manage conversational timing better, ultimately improving natural conversation and user experience.2026RCRaina Cao et al.University of British ColumbiaSocial & Collaborative VRHuman-LLM CollaborationAffective Human-Computer DialogueCHI
SCORE: A Framework for Quantifying Diegesis in Situated Visualization for Augmented RealityA central goal of Augmented Reality (AR)-based Situated Visualization (SV) is to seamlessly integrate digital information into its relevant physical context. While existing frameworks describe numerous design dimensions, the field lacks a rigorous model to evaluate this integration. To address this, we introduce SCORE, a framework for quantifying diegesis - a narratology concept describing the extent to which an element belongs to its narrative world. Grounded in a systematic analysis of 50 contemporary SV works, SCORE defines five dimensions of diegesis in SV: Spatial proximity, Concreteness, cOherence, Referential context, and Environmental context. In addition to qualitative comparison, our framework also provides a quantitative measure of diegesis, enabling SCORE to distinguish AR-based SVs that prior models have treated as theoretically equivalent. We validate the framework by demonstrating a consistent correlation between higher scores and positive usability outcomes. Based on these findings, we offer insights for SV designers.2026THTarik Hasan et al.The University of British ColumbiaAR Navigation & Context AwarenessImmersion & Presence ResearchInteractive Data VisualizationCHI
"It Seems to Understand My Heart": An Empirical Study of Persona-Driven Persuasive AI Agent for Aging-in-Place in Singapore Persona-based, empathetic approaches can foster sustainable long-term user-agent engagement in aging-in-place contexts. We present PersonaBot, a persona-driven persuasive agent built on a Dual-Persona framework that constructs user personas and generates culturally diverse, gender- and personality-varied agent personas, pairing users with preferred agent personas and adapting them over time. In an eight-week field deployment (8 participants; 1005 participant messages; 2432 agent messages), PersonaBot significantly increased perceived empathy, slowed engagement decline relative to a non-persona baseline, and elicited more elaborative interactions. Effectiveness varied with users’ technological self-efficacy, autonomy preferences, cultural identity, and social patterns, underscoring heterogeneous persona needs. Contrary to our initial assumptions, participants sometimes chose cross-cultural agents for perceived professionalism (over demographic similarity) and favored teacher-like personas balancing authority and warmth. Many framed the agent as a co-pilot rather than a caregiver replacement and engaged selectively, indicating agent personas should respect autonomy and invite—rather than demand—interaction.2026BGBO GAO et al.LILY(Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly)Agent Personality & AnthropomorphismElderly Care & Dementia SupportAging-in-Place Assistance SystemsCHI
Are You Comfortable Sharing It?: Leveraging Image Obfuscation Techniques to Enhance Sharing Privacy for Blind and Visually Impaired UsersPeople with Blind Visual Impairments (BVI) face unique challenges when sharing images, as these may accidentally contain sensitive or inappropriate content. In many instances, they are unaware of the potential risks associated with sharing such content, which can compromise their privacy and interpersonal relationships. To address this issue, we investigated image filtering techniques that could help BVI users manage sensitive content before sharing with various audiences, including family, friends, or strangers. We conducted a study with 20 BVI participants, evaluating different filters applied to images varying in sensitivity, such as personal moments or embarrassing shots. Results indicated that pixelation was the least preferred method, while preferences for other filters varied depending on image type and sharing context. Additionally, participants reported greater comfort when sharing filtered versus unfiltered images across audiences. Based on the results, we offer a set of design guidelines to enhance the image-sharing experience for BVI individuals.2026SDSatabdi Das et al.The University of British ColumbiaVisual Impairment Technologies (Screen Readers, Tactile Graphics, Braille)Privacy by Design & User ControlPrivacy & Data Ownership in Self-TrackingCHI
Exploring Learners' Expectations and Engagement When Collaborating with Constructively Controversial Peer AgentsPeer agents can supplement real-time collaborative learning in asynchronous online courses. Constructive Controversy (CC) theory suggests that humans deepen their understanding of a topic by confronting and resolving controversies. This study explores whether CC’s benefits apply to LLM-based peer agents, focusing on the impact of agents’ disputatious behaviors and disclosure of agents’ behavior designs on the learning process. In our mixed-method study (n=144), we compare LLMs that follow detailed CC guidelines (regulated) to those guided by broader goals (unregulated) and examine the effects of disclosing the agents’ design to users (transparent vs. opaque). Findings show that learners' values influence their agent interaction: those valuing control appreciate unregulated agents' willingness to cease push-back upon request, while those valuing intellectual challenges favor regulated agents for stimulating creativity. Additionally, design transparency lowers learners' perception of agents’ abilities. Our findings lay the foundation for designing effective collaborative peer agents in isolated educational settings.2026TTThitaree Tanprasert et al.University of British ColumbiaHuman-LLM CollaborationIntelligent Tutoring Systems & Learning AnalyticsCollaborative Learning & Peer TeachingCHI
PASTA: A Scalable Framework for Multi-Policy AI Compliance EvaluationAI compliance is becoming increasingly critical as AI systems grow more powerful and pervasive. Yet the rapid expansion of AI policies creates substantial burdens for resource-constrained practitioners lacking policy expertise. Existing approaches typically address one policy at a time, making multi-policy compliance costly. We present PASTA, a scalable compliance tool integrating four innovations: (1) a comprehensive model-card format supporting descriptive inputs across development stages; (2) a policy normalization scheme; (3) an efficient LLM-powered pairwise evaluation engine with cost-saving strategies; and (4) an interface delivering interpretable evaluations via compliance heatmaps and actionable recommendations. Expert evaluation shows PASTA’s judgments closely align with human experts (ρ ≥ .626). The system evaluates five major policies in under two minutes at approximately $3. A user study (N = 12) confirms practitioners found outputs easy-to-understand and actionable, introducing a novel framework for scalable automated AI governance.2026YYYu Yang et al.University of British ColumbiaExplainable AI (XAI)AI Ethics, Fairness & AccountabilityAlgorithmic Transparency & AuditabilityCHI
Leveraging Head Movement for Navigating Off-Screen Content on Large Curved DisplaysLarge curved displays are ideal for viewing 360° content, such as 3D maps, but typically restrict users to a 180° viewport, leaving information off-screen. Since users naturally direct their heads toward regions on-screen before interacting, head movements offer a promising alternative for workspace manipulation to bring off-screen content into view. We explore rate control functions (linear, sigmoid, polynomial) and zone control functions (continuous, friction, interrupted, additive) to translate head rotations into workspace control, enabling users to access off-screen content. Polynomial rate control emerges as the best choice, achieving the fastest trial times and highest subjective ratings. Using a map navigation task, our second study demonstrates that users perform better with the polynomial head-based technique than with the industry-standard controller-based methods, click-and-drag and joystick-push, for 360° workspace navigation. Based on these findings, we provide guidelines to inform the design of future 360° workspace navigation techniques for large curved displays.2026AUA K M Amanat Ullah et al.University of British ColumbiaWall & Tabletop Large Display InteractionInteractive Floors & Spatial InterfacesCHI