QuerySwitch: Supporting the Design Process by Balancing Vagueness through Large Language ModelsDesigners often regard vagueness as an essential aspect of creative work, as it fosters diverse interpretations and helps prevent fixation. Although large language models (LLMs) are increasingly viewed as a promising creative partner, designers struggle to productively incorporate vagueness into AI-supported workflows. To address this challenge, we present QuerySwitch, an interactive prototype that enables fashion designers to manage vagueness by flexibly switching between two distinct query-output modes. Findings from a user study show that QuerySwitch helps fashion designers balance vagueness, enhances the usability of LLMs in design tasks, and promotes creative exploration. This work contributes to HCI by (1) foregrounding a critical construct in human–AI collaboration, (2) demonstrating how interaction mechanisms can scaffold designer agency in LLMs use, and (3) articulating design principles—structuring exploration and preserving key query formulations—that extend to creativity-driven domains.2026MKMyungjin Kim et al.Hanyang UniversityHuman-LLM CollaborationCreative Collaboration & Feedback Systems360° Video & Panoramic ContentCHI
Behavior-Aware Anthropometric Scene Generation for Human-Usable 3D LayoutsWell-designed indoor scenes should prioritize how people can act within a space rather than merely what objects to place. However, existing 3D scene generation methods emphasize visual and semantic plausibility, while insufficiently addressing whether people can comfortably walk, sit, or manipulate objects. To bridge this gap, we present a Behavior-Aware Anthropometric Scene Generation framework. Our approach leverages vision–language models (VLMs) to analyze object–behavior relationships, translating spatial requirements into parametric layout constraints adapted to user-specific anthropometric data. We conducted comparative studies with state-of-the-art models using geometric metrics and a user perception study (N=16). We further conducted in-depth human-scale studies (individuals, N=20; groups, N=18). The results showed improvements in task completion time, trajectory efficiency, and human-object manipulation space. This study contributes a framework that bridges VLM-based interaction reasoning with anthropometric constraints, validated through both technical metrics and real-scale human usability studies.2026SJSemin Jin et al.Hanyang UniversityComputational Methods in HCIParticipatory DesignPrototyping & User TestingCHI
The Augmented Undercommons: A Framework for Liberatory HCI Research within Ethically Compromised InstitutionsSociocultural engineering research is being systematically attacked under the current US government, pressuring researchers to eliminate cultural inquiry from our work. These attacks present an existential crisis for HCI because technological innovation and understanding cultural impact are fundamentally intertwined. Marginalized HCI practitioners are at particular risk from these policies. Compliance with authoritarian demands is untenable. We need strategic, principled ways of resisting. We propose the augmented undercommons, a framework grounded in Harney and Moten’s undercommons that supports liberatory, culturally grounded technology development parallel and in opposition to ethically compromised institutions. We outline five guiding principles, demonstrate their use in HCI through three case studies, and reflect further on one principle's dimensions in practice. The augmented undercommons builds upon past knowledge from oppressed scholars to offer one possible survival strategy for our current moment, while critically reflecting on the HCI community’s current and future responsibilities.2026PCPayton Croskey et al.University of California Santa BarbaraTechnology Ethics & Critical HCIEmpowerment of Marginalized GroupsGender & Race Issues in HCICHI
Criticmate: Stagewise Human-AI Co-Critique in UI Design through Situation AwarenessAI tools are increasingly used for UI evaluation, yet most treat evaluation as a single-pass, black-box process that limits both effective model reasoning and human involvement. Grounded in Situation Awareness (SA) theory, we reframe single-screen heuristic evaluation of mobile UIs as stagewise human--AI co-critique, structuring evaluation into three editable stages: Perception (what is on the screen), Comprehension (what elements mean and do), and Projection (what problems and fixes follow). We instantiate this framing in Criticmate, an interactive system that exposes intermediate reasoning artifacts for intervention. Across offline benchmarks and a controlled user study, we show that stagewise co-critique yields more expert-like and better balanced critiques than single-pass approaches, while supporting higher trust and engagement without reducing perceived autonomy.2026JKJisu Ko et al.Hanyang UniversityHuman-LLM CollaborationPrototyping & User TestingMobile App User ExperienceCHI
Building Resilience in Human–Robot Collaboration: Affective and Cognitive Feedback from Robot for Human-Initiated Failure HandlingHuman–robot collaboration increasingly frames robots as teammates rather than tools, yet there is limited guidance on how robots should respond when failures are attributed to the human collaborator. We investigate how robot collaborators should respond to support collaboration experience after a human-attributed failure. In a 4 × 2 mixed factorial design (N = 60), participants completed a collaborative block-stacking task with either a humanoid robot (NAO) or a human collaborator under four scenarios: success, affective feedback, cognitive feedback, and no feedback. We measured collaboration experience in terms of teamwork quality, perceived copresence, and intimacy. Both affective and cognitive feedback improved these outcomes compared with no feedback: affective cues yielded the strongest socio-relational gains (copresence, intimacy), whereas cognitive cues more strongly enhanced perceived teamwork quality. These patterns were consistent across human–robot and human–human collaboration, indicating shared team-level expectations that extend beyond the individual actor. The results provide empirical evidence for socially adaptive robots that pair brief emotional reassurance with concrete guidance to support collaboration after human-attributed failures.2026JKJihwan Kim et al.Hanyang UniversityHuman-Robot Collaboration (HRC)Affective Feedback & Emotion Regulation InterfacesAffective Human-Computer DialogueCHI
LAPS: Automating Hypothesis-Driven Statistical Analysis of Public Survey Using Large Language ModelsPublic surveys are indispensable resources for understanding social dynamics, yet their analysis often imposes a high cognitive load due to structural complexity. In this paper, we present LAPS, a Large Language Model (LLM)-assisted automated framework that supports end-to-end, hypothesis-driven statistical analysis of survey data. LAPS consists of four modules (i.e., Operationalization, Planning, Execution, and Reporting) with human-in-the-loop mechanisms to balance automation with user agency. To evaluate the applicability of LAPS, we conducted a within-subjects user study with 12 social science researchers across three analytical environments: traditional statistical tools, a general-purpose LLM, and LAPS. Our findings demonstrate that LAPS ensures researcher agency and analytical stability, reduces the cognitive burden in the analysis workflow, and produces trustworthy, coherent outputs. Based on these findings, we reflect on how LAPS improves researchers’ workflows and discuss design implications for scalable and trustworthy human-AI collaboration in survey-based research.2026JKJaehoon Kim et al.Hanyang UniversityHuman-LLM CollaborationAI-Assisted Decision-Making & AutomationExplainable AI (XAI)CHI
Oscillation Design in Online Pet Loss Support Groups: Understanding Motivations, Outcomes, and ChallengesPet loss is a distressing experience often underappreciated by societal norms, leading to disenfranchised grief. We investigate how bereaved pet owners engage in online support groups, focusing on their motivations, interactions, and challenges. Through in-depth interviews with 18 participants, we identified key motivations for joining, including grief expression and validation, emotional and informational support, anonymity and accessibility. Engagement in these groups facilitated emotional expression, grief validation, memorialization practices, and the development of coping mechanisms, while also fostering shared rituals and collective identity. However, challenges like compulsory grief—where grievers feel pressured to remain in a constant state of mourning—and insufficient support for dynamic coping persisted. Drawing on the dual process model of bereavement, we propose the metaphor of oscillation design, balancing loss-oriented and restoration-oriented coping. Our findings show that current platforms overemphasize loss, underscoring the need for design interventions that rebalance asymmetric oscillation and enable more dynamic coping trajectories.2026SKSoomin Kim et al.Taejae UniversityGrief Support TechnologyPost-Mortem Social Media Account ManagementParticipatory DesignCHI
"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
"Can LLMs Persuade Humans with Deception?": From a Deceptive Strategy Taxonomy to a Large-Scale Empirical StudyBeyond hallucinations, Large Language Models (LLMs) can craft deceptive arguments that erode users' critical thinking, posing a significant yet underexamined societal risk. To address this gap, we develop a taxonomy of eight deceptive persuasion strategies by integrating top-down rhetorical theory with a bottom-up analysis of 3,360 AI-generated messages by four LLM families and examining their effects on user perceptions. Through a large-scale user study (N=602) complemented by a think-aloud protocol, we found that participants were vulnerable to \textit{Information Manipulation} and \textit{Uncertainty Exploitation}, especially when a message contradicted their prior beliefs. Vulnerability was significantly higher for participants with low cognitive reflection, low topic knowledge, and low topic involvement. Qualitative analyses further revealed that participants were persuaded by the plausibility of an overall narrative even when they distrust specific details, interpreting deceptive outputs as logically framed information that broadens perspective. We discuss critical implications of these findings for the design of trustworthy AI systems, adaptive user interfaces, and targeted literacy education.2026HYHaein Yeo et al.Hanyang UniversityAI Ethics, Fairness & AccountabilityExplainable AI (XAI)Privacy by Design & User ControlCHI
To Guide or to Disturb - How to Teach Dexterous Skills Using AI?Learning a skill is a complex cognitive task that requires processing multiple sensorimotor information. To help the skill learners, in this study, we explored and compared how to exploit AI wisely to teach skills. We set an initial target skill of engraving implemented on a haptic-audio-visual (HAV) VR environment. As the haptic device, we used a desktop force-feedback device to render the movement and force profile of the tooltip. To build teaching AI for engraving, we gathered experts' motion and force profile data. Then, we designed an Long-Short Term Memory (LSTM)-based AI model that discriminates the user's behavior and status of the tooltip using the data. With the VR environment and the AI model, we compared and evaluated three teaching strategies for haptic dexterous skill transfer- Guidance, Disturbance, and Hybrid Assistance. Hybrid Assistance alters its force between Guidance and Disturbance based on the user's performance. We conducted a user experiment with seven training sessions: one pre-test, three main training, one immediate retention, and two delayed retention sessions. In the results, we found: 1) Guidance showed a steep learning curve during the training sessions, but the participants lost the learning effect in the retention sessions, and 2) the learning with Hybrid Assistance was the slowest but remained longer, even showed a better performance in delayed retention tests. These results seem to follow a guidance hypothesis in learning, which suggests how to design the AI model to determine its policy to provide the best performance for the user's training.2025JPJiyoung Park et al.Force Feedback & Pseudo-Haptic WeightFull-Body Interaction & Embodied InputBrain-Computer Interface (BCI) & NeurofeedbackIUI
GenPara: Enhancing the 3D Design Editing Process by Inferring Users' Regions of Interest with Text-Conditional Shape ParametersIn 3D design, specifying design objectives and visualizing complex shapes through text alone proves to be a significant challenge. Although advancements in 3D GenAI have significantly enhanced part assembly and the creation of high-quality 3D designs, many systems still to dynamically generate and edit design elements based on the shape parameters. To bridge this gap, we propose GenPara, an interactive 3D design editing system that leverages text-conditional shape parameters of part-aware 3D designs and visualizes design space within the Exploration Map and Design Versioning Tree. Additionally, among the various shape parameters generated by LLM, the system extracts and provides design outcomes within the user's regions of interest based on Bayesian inference. A user study (N = 16) revealed that GenPara enhanced the comprehension and management of designers with text-conditional shape parameters, streamlining design exploration and concretization. This improvement boosted efficiency and creativity of the 3D design process.2025JCJiin Choi et al.Hanyang University, Design Informatics Lab, Interior Architecture DesignGenerative AI (Text, Image, Music, Video)3D Modeling & AnimationCreative Collaboration & Feedback SystemsCHI
"I Don’t Know Why I Should Use This App”: Holistic Analysis on User Engagement Challenges in Mobile Mental HealthOver the past decade, mobile apps have been widely adopted as a digital intervention method for mental health support, offering scalable and accessible solutions to address the growing global mental health challenges. However, sustaining user engagement in real-world settings remains a major challenge in the development of these applications. This study systematically examines factors that hinder user engagement in existing mobile mental health support systems through a scoping review of the literature. After an initial identification of 1,267 papers, we conducted a final analysis of 111 empirical studies using mobile app-based mental health support systems. The study investigates the main factors that negatively affect user engagement from user and system perspectives. Based on these findings, we propose guidelines for enhancing user engagement and structuring personalized emotional interaction design along three dimensions: adaptive, continuous, and multimodal interactions. Furthermore, we discuss the potential for integration with advanced AI methods (e.g., LLM-based AI agents) as a way to achieve these design implications and suggestions. Our results provide critical insights for enhancing long-term user engagement in the development of future mental health support systems.2025SJSeungwan Jin et al.Hanyang University, Department of Data ScienceHuman-LLM CollaborationMental Health Apps & Online Support CommunitiesCHI
HearHere: Mitigating Echo Chambers in News Consumption through an AI-based Web SystemConsiderable efforts are currently underway to mitigate the negative impacts of echo chambers, such as increased susceptibility to fake news and resistance towards accepting scientific evidence. Prior research has presented the development of computer systems that support the consumption of news information from diverse political perspectives to mitigate the echo chamber effect. However, existing studies still lack the ability to effectively support the key processes of news information consumption and quantitatively identify a political stance towards the information. In this paper, we present HearHere, an AI-based web system designed to help users accommodate information and opinions from diverse perspectives. HearHere facilitates the key processes of news information consumption through two visualizations. Visualization 1 provides political news with quantitative political stance information, derived from our graph-based political classification model, and users can experience diverse perspectives (Hear). Visualization 2 allows users to express their opinions on specific political issues in a comment form and observe the position of their own opinions relative to pro-liberal and pro-conservative comments presented on a map interface (Here). Through a user study with 94 participants, we demonstrate the feasibility of HearHere in supporting the consumption of information from various perspectives. Our findings highlight the importance of providing political stance information and quantifying users' political status as a means to mitigate political polarization. In addition, we propose design implications for system development, including the consideration of demographics such as political interest and providing users with initiatives.2024YJYoungseung Jeon et al.Session 2e: Echo Chambers and Fake News in FocusCSCW
PRECYSE: Predicting Cybersickness using Transformer for Multimodal Time-Series Sensor DataJeong 等人提出PRECYSE框架，利用Transformer融合头部运动、眼动和生理信号等多模态时序数据，实现VR场景下网络晕动症的提前预测。2024DJDayoung Jeong et al.Motion Sickness & Passenger ExperienceEye Tracking & Gaze InteractionBiosensors & Physiological MonitoringUbiComp
Transportation Mode Detection Technology to Predict Wheelchair Users' Life Satisfaction in Seoul, South KoreaHwang 等人开发轮椅用户出行方式检测技术，并利用该技术预测首尔轮椅用户的生活满意度。2024SHSungjin Hwang et al.Human Pose & Activity RecognitionCognitive Impairment & Neurodiversity (Autism, ADHD, Dyslexia)UbiComp
More Data for People with Disabilities! Comparing Data Collection Efforts for Wheelchair Transportation Mode DetectionTransportation mode detection (TMD) for wheelchair users is essential for applications that facilitate enhancing accessibility and quality of life. Yet, the lack of extensive datasets from disabled individuals hinders the development of tailored TMD systems. Our study assesses two data collection methods in TMD for disability research: using non-wheelchair users to simulate wheelchair activities (Simulation Real IMU) and generating synthetic sensor data from videos (Virtual IMU). Results show that, when using a larger dataset and multiple sensor modalities, models trained on Simulation Real IMU perform better. However, models trained on both Simulation Real IMU and Virtual IMU exhibited similar performances when sensors were restricted to accelerometer and gyroscope only. This finding guides future researchers toward the use of Simulation Real IMU for comprehensive, multimodal sensor studies, provided they have sufficient budget and time. However, the more cost and time-efficient Virtual IMU can be a viable alternative in scenarios using basic sensors.2024SHSungjin Hwang et al.Human Pose & Activity RecognitionIntelligent Tutoring Systems & Learning AnalyticsBiosensors & Physiological MonitoringUbiComp
“Is Text-Based Music Search Enough to Satisfy Your Needs?” A New Way to Discover Music with Images Music is intrinsically connected to human experience, yet the plethora of choices often renders the search for the ideal piece perplexing, especially when the search terms are ambiguous. This study questions the viability of employing visual data, specifically images, in innovative queries for music search, and it aims to better align search results with users' moods and situational context. We designed and evaluated three prototype systems for music search—TTTune (text-based), VisTune (image-based), and VTTune (hybrid)—to comparatively assess user experience and system usability. In a comprehensive user study involving 236 participants, each participant interacted with one of the systems and subsequently completed post-experimental surveys. A subset of participants also participated in in-depth interviews to further elucidate the potential and the advantages of image-based music retrieval (IMR) systems. Our findings reveal a marked preference for the user experience and usability offered by the IMR approach, as compared with the traditional text-based method. This underscores the potential of the image in an effective search query. Based on these findings, we discuss interface design guidelines tailored for IMR systems and factors affecting system performance, contributing to the evolving landscape of music search methods.2024JPJeongeun Park et al.Hanyang UniversityRecommender System UXMusic Composition & Sound Design ToolsCHI
The Impact of Sketch-guided vs. Prompt-guided 3D Generative AIs on the Design Exploration ProcessVarious modalities have emerged in the field of 3D generative AI (GenAI) to enhance design outcomes. While some designers find inspiration in prompts to guide their design options, others prefer sketching to embody creative visions. Nonetheless, the impact of the different modalities of 3D GenAI on the design process remains largely unexplored. This study examines the utilization of prompt- and sketch-guided modalities within the design process by conducting linkography and workflow analyses with 12 designers. The results revealed that prompts played a pivotal role in stimulating initial ideation, whereas sketches played a crucial role in embodying design ideas. This investigation highlights the distinct contributions of these modalities at different phases of the design process, suggesting the potential for a more refined and synergistic collaboration between humans and AI. By elucidating the diverse functions of sketches and prompts, we propose prospective directions for the UX framework of the 3D GenAI.2024SLSeung Won Lee et al.Hanyang UniversityGenerative AI (Text, Image, Music, Video)3D Modeling & AnimationCHI
Narrating Routines through Game Dynamics: Impact of a Gamified Routine Management App for Autistic IndividualsMaintaining a daily routine has profound implications for physical, emotional, and social well-being. Autistic individuals may experience various challenges in establishing and maintaining a healthy daily routine due to their tendency to be inactive in daily life combined with their characteristics and preferences. Previous studies employing mobile technology to support autistic individuals have primarily focused on self-help functions, with limited exploration into the detailed needs of these individuals to develop and maintain personalized routines. In this study, we conducted a nine-week field study with 18 autistic individuals using RoutineAid, a gamified app designed to support key routines of autistic individuals (i.e., physical activity, diet, mindfulness, and sleep). Our analysis incorporated five measures of self-evaluation on daily life, app usage logs, Fitbit physical activity data, and interviews. Our findings demonstrate the effectiveness of RoutineAid and highlight its two primary affordances for autistic individuals: (1) promoting self-efficacy and embedding health behavior and (2) refining daily routines for healthier outcomes. We discuss salient design insights for developing daily routine management systems for autistic individuals.2024BKBogoan Kim et al.Hanyang UniversityCognitive Impairment & Neurodiversity (Autism, ADHD, Dyslexia)Gamification DesignCHI
V-DAT (Virtual Reality Data Analysis Tool): Supporting Self-Awareness for Autistic People from Multimodal VR Sensor DataVirtual reality (VR) has become a valuable tool for social and educational purposes for autistic people, as it provides flexible environmental support to create a variety of experiences. A growing body of recent research has examined the behaviors of autistic people using sensor-based data to better understand autistic people and investigate the effectiveness of VR. Comprehensive analysis of the various signals that can be easily collected in the VR environment can promote understanding of autistic people. While this quantitative evidence has the potential to help both autistic people and others (e.g., autism experts) to understand behaviors of autistic people, existing studies have focused on single signal analysis and have not determined the acceptability of signal analysis results from the autistic person's point of view. To facilitate the use of multiple sensor signals in VR for autistic people and experts, we introduce V-DAT (Virtual Reality Data Analysis Tool), designed to support a VR sensor data handling pipeline. V-DAT takes into account four sensor modalities - head position and rotation, eye movement, audio, and physiological signals - that are actively used in current VR research for autistic people. We explain the characteristics and processing methods of the data for each modality as well as the analysis with comprehensive visualizations of V-DAT. We also conduct a case study to investigate the feasibility of V-DAT as a way of broadening understanding of autistic people from the perspectives of both autistic people and autism experts. Finally, we discuss issues with the process of V-DAT development and complementary measures for the applicability and scalability of a sensor data management system for autistic people.2023BKBogoan Kim et al.VR Medical Training & RehabilitationCognitive Impairment & Neurodiversity (Autism, ADHD, Dyslexia)Visualization Perception & CognitionUIST