CodeVoyager: Integrating Interactive Visual Aids with LLMs for Code ComprehensionUnderstanding unfamiliar codebases is essential yet challenging in software development. Visual aids such as call graphs and control flow graphs can help, but often lead to information overload and limited interactivity. Meanwhile, LLM-based code assistants provide accessible natural language explanations that reduce cognitive barriers, but lack spatial context for code navigation. We posit that integrating these two complementary approaches can address their respective limitations. To validate this integration, we introduce CodeVoyager, a tool that combines LLM with interactive visual aids to support more effective code comprehension. We first conducted an exploratory study (n=11) to assess the tool's potential and identify areas for refinement. Following iterative refinement, we evaluated the enhanced tool against a widely used chat-based code assistant in a within-subjects study (n=16). Results showed that CodeVoyager improved code comprehension and increased user trust. These improvements were achieved by enabling seamless interaction between textual explanations and visual code exploration, mirroring how developers naturally discuss code. This work contributes to visual-LLM integrated developer tools through (1) a novel integration approach mirroring natural code discussion, (2) empirical evidence of improved comprehension and trust, and (3) design implications for multimodal code comprehension systems.2026YKYeonjoon Kim et al.Seoul National UniversityHuman-LLM CollaborationInteractive Data VisualizationPrototyping & User TestingIUI
In-Situ Adaptive Interfaces for Online Browsing: Design Dimensions for Intent-Responsive Automation and User ControlOnline browsing often requires balancing open-ended exploration with focused comparison, yet most interfaces remain static regardless of user focus. Adaptive interfaces offer a way to better align interface presentation with browsing needs, but how such adaptations should be triggered, designed, and controlled in practice remains unclear. We investigate adaptive browsing interfaces that modify information hierarchy, information granularity, and session-based ordering in response to inferred browsing intent. Drawing from a framework of five adaptation dimensions, we implemented ReLay, a browser-based probe that applies lightweight, in-situ adaptations automatically while allowing user overrides. In a two-phase study (n = 10), participants welcomed adaptive changes when they were transparent, consistent, and easily reversible. Rather than treating control as error correction, users used it as a means of calibration—testing and tuning automation before accepting it. These findings illustrate how intent-responsive, controllable adaptation can support browsing without diminishing user agency. Our work contributes (1) a conceptual framework for adaptive interface behaviors in online browsing, (2) an instantiation of the framework that selectively operationalizes three dimensions, and (3) empirical insights into user acceptance, control, and design implications for adaptive browsing systems.2026EKEunhye Kim et al.KAISTAI-Assisted Decision-Making & AutomationRecommender System UXInformation Filtering & PersonalizationIUI
"I Should Know, But I Dare Not Ask": From Understanding Challenges in Healthcare Journeys to Deriving Design Implications for North Korean Defectors' AdaptationWhile it is known that North Korean defectors (NKDs) struggle with South Korea's healthcare system, the specific challenges of their patient journey remain underexplored. To investigate this, we conducted interviews with 10 NKDs about an 8-step patient journey and identified the clinical consultation step as a critical barrier for all participants, marked by three key challenges: expressing symptoms, managing social and cultural concerns, and overcoming language differences. In response, we developed Medibridge, a mobile prototype that allows users to rehearse with an AI doctor before a real hospital visit to generate a tangible ``Helper Note'' for their actual consultation. Our evaluation with 15 NKDs showed improvements in perceived communication capability, including greater expression clarity, reduced social and cultural concerns, and enhanced linguistic confidence. Our contributions include an empirical understanding of NKDs' healthcare challenges, a novel AI-powered rehearsal system that prepares users for real-world clinical communication, and design implications for inclusive technologies for displaced populations.2026HSHyungwoo Song et al.Seoul National UniversityMental Health Apps & Online Support CommunitiesTelemedicine & Remote Patient MonitoringAI Ethics, Fairness & AccountabilityCHI
AI in Webtoon Creation: Challenges, Perceptions, and Design ImplicationsWhile generative AI is rapidly advancing in creative industries, its adoption in webtoons---a mobile-first digital comics format---remains contentious. In this exploratory study, we conducted interviews with nine readers, four creators, and six platform stakeholders to examine the sociotechnical dynamics of AI integration. Findings reveal a complex tension: readers value the parasocial authenticity of human creators and reject AI as soulless, compelling creators to adopt strategic silence regarding their use of AI for efficiency. Platforms mediate this conflict by redefining authorship from manual labor to directing and leveraging strategic invisibility to reconcile industrial efficiency with the illusion of human touch. We propose a Tripartite Mediation Model, which maps the structural tensions between creative agency (Production), authenticity (Reception), and market stratification (Distribution). Our study contributes design implications for labor-aware disclosure, scaffolded agency, and personalized training frameworks to preserve artistic integrity while addressing the sequential and emotional demands of webtoon storytelling.2026SKSoomin Kim et al.Seoul National UniversityGenerative AI (Text, Image, Music, Video)Human-LLM CollaborationAI Ethics, Fairness & AccountabilityCHI
“Don’t Look, But I Know You Do”: Norms and Observer Effects in Shared LLM AccountsAccount sharing is common in subscription services and is now extending to generative AI platforms, which are still primarily designed for individual use. Sharing often requires workarounds that create new tensions. This study examines how LLM subscriptions are shared and the norms that develop. We combined a survey of 245 users with interviews of 36 participants to understand both patterns and lived experiences. Our analysis identified four types of account sharing, organized along two dimensions: whether the owner uses the account and whether subscription costs are shared. Within these types, we examined how norms were formed and how their fragility, especially privacy, became evident in practice. Users, fully aware of this, subtly adjusted their behavior, which we interpret through the lens of the observer effect. We frame LLM account sharing as a social practice of appropriation and outline design implications to adapt single-user platforms to multi-user realities.2026JSJi Eun Song et al.Seoul National UniversityHuman-LLM CollaborationPrivacy by Design & User ControlPrivacy Perception & Decision-MakingCHI
Editable XAI: Toward Bidirectional Human-AI Alignment with Co-Editable Explanations of Interpretable AttributesWhile Explainable AI (XAI) helps users understand AI decisions, misalignment in domain knowledge can lead to disagreement. This inconsistency hinders understanding, and because explanations are often read-only, users lack the control to improve alignment. We propose making XAI editable, allowing users to write rules to improve control and gain deeper understanding through the generation effect of active learning. We developed CoExplain, leveraging a neural network for universal representation and symbolic rules for intuitive reasoning on interpretable attributes. CoExplain explains the neural network with a faithful proxy decision tree, parses user-written rules as an equivalent neural network graph, and collaboratively optimizes the decision tree. In a user study (N=43), CoExplain and manually editable XAI improved user understanding and model alignment compared to read-only XAI. CoExplain was easier to use with fewer edits and less time. This work contributes Editable XAI for bidirectional AI alignment, improving understanding and control.2026HCHaoyang Chen et al.National University of SingaporeExplainable AI (XAI)AI-Assisted Decision-Making & AutomationCHI
JourneyVR: Designing for Continuous Workflow Experience in Virtual RealityManaging multiple activities in virtual reality (VR) is often hindered by fragmented workflows and disruptive application switching. We present JourneyVR, a metaphorical interaction model designed for spatial and sequential workflows that reframes tasks as continuous journeys rather than isolated sessions. Users construct a Journey Map as a layout of islands (tasks) and bridges (transitions), which expands into an immersive world where activities unfold as a coherent, embodied narrative. Through a formative study, a controlled comparison, and an expert evaluation, JourneyVR was shown to enhance experiential continuity, intention to use, and overall satisfaction compared to using a conventional app launcher. Participants highlighted how the metaphor fosters motivation and achievement, while we identify boundaries regarding task type, scalability, and flexibility. Our findings demonstrate that framing sequential activities as navigable journeys can transform fragmented tasks into meaningful narratives, offering concrete guidelines for sustained engagement and more flexible workflows in immersive environments.2026MKMinchae Kim et al.Korea UniversityMixed Reality WorkspacesImmersion & Presence ResearchSocial & Collaborative VRCHI
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
HyPockeTuner: Bringing Hyperparameter Optimization to Mobile DevicesHyperparameter optimization (HPO) is a long-running process that can span hours or even days. While recent Human-in-the-Loop HPO systems enable monitoring and steering of the process, they are typically designed for desktop environments, which limits their effectiveness in managing prolonged experiments in practice. To address these limitations, we present HyPockeTuner, an interactive mobile system that enables users to monitor, steer, and reflect on HPO experiments anytime, anywhere from smartphones. Its mobile-tailored interface supports tracking experiment history and visualizing the relationship between user interventions and performance changes. HyPockeTuner also employs a notification workflow that alerts users to important events, reducing the burden of constant monitoring while enabling timely interventions. In a pilot study, we validated that users could readily identify critical events, such as performance improvements and intervention points, through our visualization. Furthermore, two five-day deployment studies with follow-up reflection sessions demonstrated that users could integrate experiment management into their daily routines and reflect on past decisions, generating insights for future improvement.2026DHDonghee Hong et al.Sungkyunkwan UniversityAutoML InterfacesRemote Work Tools & ExperienceBehavior Change & Reflection TechnologyCHI
Breakdowns and Design Opportunities for Collaborative File ManagementEffective file management is central to coordination in collaborative work, as shared files serve as the primary medium through which collaborators exchange contributions. Building on existing PIM and CSCW literature on file management breakdowns, we recontextualize such breakdowns within specific dynamics of collaboration. In Study 1, we conducted a need-finding interview(N=33) and identified four recurring breakdowns in collaborative file management: ambiguous file placement and ownership, inefficient version management, uninterpretable metadata, and missing status cues. Building on these findings, Study 2 used a design probe evaluation(N=12) to examine potential benefits and concerns associated with supporting collaborative file management. Participants reported benefits such as clearer ownership, stronger reference convergence, improved metadata interpretability, and heightened progress visibility, while expressing concerns related to surveillance, exploration containment, overdisclosure, and social pressure. Taken together, the studies reframe well-known file management issues as a dichotomy between perceived benefits and concerns, thereby outlining design directions for file-level alignment.2026KSKiyeal Seo et al.Seoul national universityDistributed Team CollaborationKnowledge Management & Team AwarenessPrototyping & User TestingCHI
From Ballpark to Society: Understanding Stakeholders’ Adaptation to Automated Judgment via ABS in BaseballArtificial Intelligence (AI) is increasingly automating expert judgments across diverse domains. However, the practical dynamics of adaptation among diverse stakeholders remain underexplored. We investigated the Korea Baseball Organization’s adoption of the Automated Ball-Strike System (ABS), the first league-wide deployment of an AI adjudicator. Interviews with 38 stakeholders—umpires, players, coaches, and fans—revealed that adoption was driven by demands for fairness and frustration with human limitations, and was viewed as an inevitable trajectory. Acceptance depended less on accuracy than on verifiable consistency, which reduced interpersonal conflict by shifting judgment to technology. However, adaptive burdens were redistributed: players faced pressure to recalibrate strategies for survival, while umpires grappled with diminished authority. Systemic legitimacy hinged on procedural transparency and visible feedback mechanisms. Based on these findings, we propose governance principles emphasizing transparency and adaptive role reconfiguration for sustainable human-AI coexistence.2026DLDokyung Lee et al.Seoul National UniversityAI-Assisted Decision-Making & AutomationAI Ethics, Fairness & AccountabilityPrivacy by Design & User ControlCHI
InnerPond: Fostering Inter-Self Dialogue with a Multi-Agent Approach for IntrospectionIntrospection is central to identity construction and future planning, yet most digital tools approach the self as a unified entity. In contrast, Dialogical Self Theory (DST) views the self as composed of multiple internal perspectives, such as values, concerns, and aspirations, that can come into tension or dialogue with one another. Building on this view, we designed InnerPond, a research probe in the form of a multi-agent system that represents these internal perspectives as distinct LLM-based agents for introspection. Its design was shaped through iterative explorations of spatial metaphors, interaction scaffolding, and conversational orchestration, culminating in a shared spatial environment for organizing and relating multiple inner perspectives. In a user study with 17 young adults navigating career choices, participants engaged with the probe by co-creating inner voices with AI, composing relational inner landscapes, and orchestrating dialogue as observers and mediators, offering insight into how such systems could support introspection. Overall, this work offers design implications for AI-supported introspection tools that enable exploration of the self’s multiplicity.2026HJHayeon Jeon et al.Seoul National UniversityHuman-LLM CollaborationEmpathy & Emotional DesignAffective Human-Computer DialogueCHI
DeepAware: Using Experiential Deepfake Simulations to Enhance Cybersecurity Awareness in Older AdultsDeepfake scams, which use AI-generated audio or video to impersonate individuals, pose an increasing cybersecurity threat to older adults. Existing educational approaches present threats through generic examples, leaving learners to perceive scams as something that happens to others rather than to themselves. To address this gap, we conducted a formative study with five digital educators to identify design requirements, then developed DeepAware, a self-referential simulation platform that embeds participants' own faces and voices into deepfake scam scenarios. By making learners the target of simulated threats rather than passive observers, DeepAware aims to collapse the psychological distance between abstract warnings and personal vulnerability. A mixed-methods evaluation with 21 older adults found improvements in deepfake knowledge, threat perception, and coping confidence, though responses varied by prior familiarity. This work demonstrates the potential of self-referential simulation for cybersecurity education and offers design implications for future cybersecurity interventions.2026HOHana Oh et al.Human Centered Computing LabDeepfake & Synthetic Media DetectionCybersecurity Training & AwarenessCHI
"What Keeps Fans on the Silent Field?": Understanding Lean-Back Football Fans via AI Sports Broadcasting in Non-Event TimeModern media consumption habits challenge `lean-back' viewers—who prefer passive viewing—to stay engaged during the frequent periods of Non-Event Time in soccer matches. Existing commentary options often fail this audience, being either too dry or too interactive. To investigate their needs, we developed \textbf{ARUA}, a prototype that positions users as `directors' of their own AI commentary. This approach serves as a probe to understand their preferences for a more engaging viewing experience. In a qualitative study with 32 fans, we found users craft the commentary into a relational tool, tailoring its social presence and emotional tone to maintain engagement. They created proxy voices for their own emotions and curated balanced conversational panels. Our work contributes an understanding of lean-back viewers, introduces a user-directed paradigm for personalized media, and provides design principles for creating engaging, low-effort experiences that support control over social presence, emotional tone, and cognitive load.2026KKKyusik Kim et al.Seoul National UniversityGame UX & Player BehaviorGenerative AI (Text, Image, Music, Video)AI-Assisted Decision-Making & AutomationCHI
CrossLit: Connecting Visual and Textual Sensemaking for Literature ReviewConducting literature reviews is cognitively demanding, requiring researchers to navigate large volumes of work while constructing coherent narratives that position their contributions. The process unfolds through iterative stages of sensemaking, each demanding different support. Existing tools emphasize either visual interfaces that provide macroscopic overviews or textual interfaces that support thematic organization and narrative construction. However, keeping modalities separate forces researchers to switch between tools, disrupting workflow continuity. We present CrossLit, a system that integrates and synchronizes visual and textual interfaces to support the entire process from discovering papers to composing coherent narratives. CrossLit allows researchers to group and annotate papers visually while generating aligned textual structures, and to edit text that automatically updates visual representations. We find that CrossLit helps users develop and refine conceptual structures and build narratives iteratively through seamless cross-modal transitions. We conclude by discussing design implications for synchronizing visual and textual interfaces for sensemaking support.2026KCKiroong Choe et al.Seoul National UniversityInteractive Data VisualizationCollaborative Writing ToolsAnnotation & Markup ToolsCHI
Clarifying or Complicating?: Understanding Older Adults' Engagement with Real-World XAI in E-CommerceE-commerce platforms increasingly deploy explainability features to address concerns about algorithmic opacity. However, most XAI research has focused on younger, tech-savvy users, leaving open questions about how older adults engage with these features in everyday shopping. To address this gap, we conducted a qualitative study with 20 older adults aged 60+ who regularly use NAVER Shopping, one of South Korea's largest e-commerce platforms, examining their engagement with global (system-level) explanations, local (item-level) explanations, and a user-model dashboard. Our findings reveal that explainability does not operate uniformly. Many participants did not notice the explanation features during routine use or mistook them for advertisements. After guided interaction, global explanations elicited polarized responses: some participants deferred uncritically to algorithmic authority, whereas others dismissed the explanations as sophisticated marketing rhetoric. In contrast, local explanations grounded in users' behavior helped recalibrate skepticism, while a user-model dashboard exposed tensions between empowerment and surveillance. Based on these findings, we propose actionable design strategies for building inclusive and adaptive XAI systems for older adults.2026SKSeo Hyeong Kim et al.Seoul National UniversityExplainable AI (XAI)AI Ethics, Fairness & AccountabilityPrivacy by Design & User ControlCHI
Bodyfulness VR: Exploring Virtual Reality as a Medium for Somatic Meditative PracticesWithin body-centered HCI, growing attention has been given to the body not only as an instrument of the mind but as a source of experience and presence. Dance meditation exemplifies this orientation by fostering mindful connection to the body through spontaneous, unstructured movement. In this context, VR has been widely adopted for structured instruction in static meditation and choreographed dance, yet its potential to foster freer, meditative bodily engagement has received little attention. To address this gap, we collaborated with dance meditators and co-designed Bodyfulness VR, a four-module system supporting (1) somatic awareness, (2) movement exploration, (3) emotional release, and (4) physical relaxation. Using the system as a design probe, we examined how VR could mediate somatic meditative practices. Findings from our user study indicate that VR could lower barriers to dance meditation by reducing self-consciousness and supporting bodily engagement, while movement-guiding affordances may also redirect attention toward task-oriented action, constraining bodily expression. By articulating this tension, our work contributes design implications for balancing structure and openness in VR systems that aim to support somatic mindfulness.2026SYSuhwoo Yoon et al.Seoul National UniversityImmersion & Presence ResearchFull-Body Interaction & Embodied InputDance & Body Movement ComputingCHI
Bridging Gulfs in UI Generation through Semantic GuidanceWhile generative AI enables high-fidelity UI generation from text prompts, users struggle to articulate design intent and evaluate or refine results—creating gulfs of execution and evaluation. To understand the information needed for UI generation, we conducted a thematic analysis of UI prompting guidelines, identifying key design semantics and discovering that they are hierarchical and interdependent. Leveraging these findings, we developed a system that enables users to specify semantics, visualize relationships, and extract how semantics are reflected in generated UIs. By making semantics serve as an intermediate representation between human intent and AI output, our system bridges both gulfs by making requirements explicit and outcomes interpretable. A comparative user study suggests that our approach enhances users' perceived control over intent expression and outcome interpretation, and facilitates more predictable iterative refinement. Our work demonstrates how explicit semantic representation enables systematic and explainable exploration of design possibilities in AI-driven UI design.2026SPSeokhyeon Park et al.Seoul National UniversityGenerative AI (Text, Image, Music, Video)Human-LLM CollaborationExplainable AI (XAI)CHI
Good Fences Make Good Learning: How Self-Directed Language Learners Navigate LLM Delegation DecisionsSelf-directed language learners increasingly turn to large language models (LLMs) for assistance, but face the challenge of deciding what learning tasks to delegate to LLMs and how. While prior research has examined the effectiveness of LLM in improving language proficiency, less is known about how learners negotiate agency and what values guide delegation strategies. To address this gap, we conducted a two-part study: an analysis of discussions in the r/languagelearning subreddit to map learners' LLM usage patterns and factors driving delegation, followed by a technology probe study where learners designed learning activities and experimented with LLM support. Our findings reveal three key considerations influencing delegation: accuracy, independence, and authenticity. We analyze these considerations through two types of obstacles: selection challenges in choosing appropriate strategies and execution challenges in following through on intentions. These insights inform the design of AI-assisted learning systems that preserve learner agency while supporting diverse learning goals.2026JSJiwon Song et al.Seoul National UniversityHuman-LLM CollaborationIntelligent Tutoring Systems & Learning AnalyticsCHI
Actor’s Note: Examining the Role of AI-Generated Questions in Character Journaling for Actor TrainingCharacter journaling is a well-established exercise in actor training, but many actors struggle to sustain it due to cognitive burden, the blank page problem, and unclear short-term rewards. We reframe large language models not as co-authors but as maieutic partners—tools that guide reflection through context-aware questioning rather than producing text on behalf of the user. Based on this perspective, we designed Actor’s Note, a journaling tool that tailors questions to the script, role, and rehearsal phase while preserving actor agency. We evaluated the system in a 14-day crossover study with 29 actors using surveys, logs, and interviews. Results indicate that the tool reduced entry barriers, supported sustained reflection, and enriched character exploration, with participants describing different benefits when AI was introduced at earlier versus later rehearsal stages. This work contributes empirical insights and design principles for creativity-support tools that sustain reflective practices while preserving artistic immersion in performance training.2026SKSora Kang et al.Seoul National UniversityHuman-LLM CollaborationCreative Collaboration & Feedback SystemsInteractive Narrative & Immersive StorytellingCHI