Exploring Data-Driven Approaches to Stress Management: A Systematic Review of Stress Tracking, Intervention, and System Evaluation MethodsAdvances in ubiquitous and wearable sensing and HCI research have made stress monitoring increasingly accessible, enabling the development of personalized stress management technologies. Yet, stress is a subjective and contextual experience, making effective intervention design challenging. Prior studies often isolate stress detection or intervention, without providing an integrated view of how these components connect and are evaluated in real-world use. To address this gap, we conducted a systematic review of 2,152 papers and selected 52 empirical studies where stress tracking informed interventions. Using a framework based on three stress constructs (subjective stress, psycho-physiological stress, and exposure stress), we analyzed how definitions of stress shape detection indicators, intervention design and timing, and evaluation methods. We show that stress conceptualization strongly influences system design, and we propose a conceptual framework linking detection, intervention, and evaluation to guide future user-centered stress management technologies.2026YKYoungji Koh et al.KAISTSleep & Stress MonitoringHealth Self-TrackingBehavior Change & Reflection TechnologyCHI
Building Human–Multi-Agent Teams for Creative WorksTeam-based collaboration is a cornerstone of modern creative work. Recent advances in generative AI open possibilities for humans to collaborate with multiple AI agents in distinct roles to address complex creative workflows. Yet, how to form Human–Multi-Agent Teams (HMATs) is underexplored, especially given that inter-agent interactions increase complexity and the risk of unexpected behaviors. In this exploratory study, we aim to understand how to form HMATs for creative work using CrafTeam, a technology probe that allows users to form and collaborate with their teams. We conducted a study with 12 design practitioners, in which participants iterated through a three-step cycle: forming HMATs, ideating with their teams, and reflecting on their teams' ideation. Our findings reveal that while participants initially attempted autonomous team operations, they ultimately adopted team formations in which they directly orchestrated agents. We discuss design considerations for HMAT formation that humans can effectively orchestrate multiple agents.2026HLHyunseung Lim et al.KAISTGenerative AI (Text, Image, Music, Video)Creative Collaboration & Feedback SystemsCHI
Constructing Everyday Well-Being: Insights from God-Saeng (God生) for Personal InformaticsWhile Personal Informatics (PI) systems support behavior change, everyday well-being involves more than achieving individual target behaviors. It is shaped by cultural narratives that give actions meaning. In South Korea, the God-Saeng (God生) phenomenon—encompassing disciplined, collective, and publicly documented self-improvement practices—offers a lens into how well-being is negotiated in daily life. We conducted a 10-day probe (N=24) with bite-sized missions to examine how young adults engaged in God-Saeng. Participants relied on planning practices, accountability infrastructures, and datafication to stabilize themselves, yet these same routines also intensified pressures toward self-monitoring and performance. They navigated tensions between consistency and flexibility, authenticity and visibility, and productivity and broader values such as relationships, and reinterpreted ordinary activities through sociocultural contexts. These insights suggest design opportunities for PI systems that move beyond tracking, toward digital instruments that help users negotiate tensions, make meaning, and reflexively understand how technologies participate in their culturally and existentially situated well-being.2026ISInhwa Song et al.Princeton UniversityBehavior Change & Reflection TechnologyData-Driven Personal Decision-MakingInclusive DesignCHI
Dark and Bright Side of Participatory Red-Teaming with Targets of Stereotyping for Eliciting Harmful Behaviors from Large Language ModelsRed-teaming—where adversarial prompts are crafted to expose harmful behaviors and assess risks—offers a dynamic approach to surfacing underlying stereotypical bias in large language models. Because such subtle harms are best recognized by those with lived experience, involving targets of stereotyping as red-teamers is essential. However, critical challenges remain in leveraging their lived experience for red-teaming while safeguarding psychological well-being. We conducted an empirical study of participatory red-teaming with 20 individuals stigmatized by stereotypes against nonprestigious college graduates in South Korea’s rigid educational meritocracy. Through mixed-methods analysis, we found participants transformed experienced discrimination into strategic expertise for identifying biases, while facing psychological costs such as stress and negative reflections on group identity. Notably, red-team participation enhanced their sense of agency and empowerment through their role as guardians of the AI ecosystem. We discuss the implications for designing participatory red-teaming that prioritizes both the ethical treatment and the empowerment of stigmatized groups.2026SKSieun Kim et al.KAISTHuman-LLM CollaborationAI Ethics, Fairness & AccountabilityAlgorithmic Fairness & BiasCHI
“I Choose to Live, for Life Itself”: Understanding Agency of Home-Based Care Patients Through Information Practices and Relational Dynamics in Care NetworksHome-based care (HBC) delivers medical and care services in patients' living environments, offering unique opportunities for patient-centered care. However, patient agency is often inadequately represented in shared HBC planning processes. Through 23 multi-stakeholder interviews with HBC patients, healthcare professionals, and care workers, alongside 60 hours of ethnographic observations, we examined how patient agency manifests in HBC and why this representation gap occurs. Our findings reveal that patient agency is not a static individual attribute but a relational capacity shaped through maintaining everyday continuity, mutual recognition from care providers, and engagement with material home environments. Furthermore, we identified that structured documentation systems filter out contextual knowledge, informal communication channels fragment patient voices, and doctor-centered hierarchies position patients as passive recipients. Drawing on these insights, we propose design considerations to bridge this representation gap and to integrate patient agency into shared HBC plans.2026SKSung-In Kim et al.Seoul National University Bundang HospitalElderly Care & Dementia SupportAging-in-Place Assistance SystemsResearch Ethics & Open ScienceCHI
Moodialogue: Transforming Emotions into Personified, Conversational AgentsUnderstanding emotion for self-awareness requires recognizing not only its type and intensity but also the surrounding context and the insights it provides. While prior work has studied emotion recording, little attention has given to how such records might foster reflection on context. To address this gap, we developed Moodialogue, a system that enables users to personify emotion and engage in dialogue with it. In a six-week field study with nine participants, we found that personified emotion records supported dialogues that uncovered overlooked contexts and coexisting feelings beyond a single entry. Participants also reported savoring positive emotions more deeply and reframing negative ones from new perspectives. These findings point to design opportunities for systems that move beyond recording, enabling post-recording interactions that deepen reflection on emotional context and meaning.2026SJSangsu Jang et al.Chung-Ang UniversityEmpathy & Emotional DesignAffective Human-Computer DialogueBehavior Change & Reflection TechnologyCHI
When Scaffolding Breaks: Investigating Student Interaction with LLM-Based Writing Support in Real-Time K-12 EFL ClassroomsLarge language models (LLMs) are promising tools for scaffolding students' English writing skills, but their effectiveness in real-time K-12 classrooms remains underexplored. Addressing this gap, our study examines the benefits and limitations of using LLMs as real-time learning support, considering how classroom constraints, such as diverse proficiency levels and limited time, affect their effectiveness. We conducted a deployment study with 157 eighth-grade students in a South Korean middle school English class over six weeks. Our findings reveal that while scaffolding improved students' ability to compose grammatically correct sentences, this step-by-step approach demotivated lower-proficiency students and increased their system reliance. We also observed challenges to classroom dynamics, where extroverted students often dominated the teacher's attention, and the system's assistance made it difficult for teachers to identify struggling students. Based on these findings, we discuss design guidelines for integrating LLMs into real-time writing classes as inclusive educational tools.2026JMJunho Myung et al.KAISTHuman-LLM CollaborationProgramming Education & Computational ThinkingK-12 Digital Education ToolsCHI
Designing OWN, The Inner World as a Virtual Space: By and For IntrospectionNurturing the inner world of emotions, thoughts, and self is essential to our well-being. However, the abstract and invisible nature of the inner world makes it difficult for people to sense and manage. Then, what if people build their own virtual world where they can introspect inner sides? This pictorial presents the creation process of ‘OWN’, a personal virtual space where users, the OWNers, can explore and interact with their inner world. As part of the co-creation process, each OWNer expresses a personal narrative about themselves and their surroundings, allowing psychologists to gain an in-depth insight into their world. The psychologists’ analysis of the OWNer’s inner state suggests various elements that the designer visualizes as space in the virtual world. By designing OWN with expressive activities, OWNers were able to reflect more deeply on their lives. OWN then became a virtual oasis where OWNers could relax, reflect, and improve themselves.2025SASOOYEON AHN et al.Immersion & Presence ResearchMental Health Apps & Online Support CommunitiesC&C
Thinking Outside the Data Box: Investigating the Potential of Data Manipulation for Self-Reflection on Personal DataIn the practice of personal informatics (PI), self-reflection is crucial for enhancing self-knowledge and driving behavior change. Numerous studies have focused on effectively interpreting and representing data to support self-reflection. However, despite their efforts, some self-trackers find themselves stuck in repetitive insights and stagnant process. For them, a fundamental shift beyond re-representing existing data could provide a significant opportunity. We explore data manipulation—altering the data value or structure—as an alternative approach. We conducted an exploratory workshop and a one-week field trial with 10 self-trackers, using five types of data manipulation. We found that data manipulation could revitalize self-reflection, uncovering diverse perspectives and overlooked aspects. It also fostered positive illusions and emotions, potentially setting the stage for behavioral change and engagement. However, it introduces perceptual distortions and has limited applicability, highlighting the importance of balanced use. We further discuss design implications for integrating data manipulation into future PI systems.2025YJYeohyun Jung et al.Interactive Data VisualizationTime-Series & Network Graph VisualizationContext-Aware ComputingDIS
Exploring Design Spaces to Facilitate Household Collaboration for Cohabiting CouplesHousehold collaboration among cohabiting couples presents unique challenges due to the intimate nature of the relationships and the lack of external rewards. Current efficiency-oriented technologies neglect these distinct dynamics. Our study aims to examine the real-world context and underlying needs of couples in their collaborative homemaking. We conducted a 10-day empirical investigation involving six Korean couples, supplemented by a probe approach to facilitate reflection on their current homemaking practices. We identified the requirement for ideal household collaboration as a 'shared ritual for celebratory interaction' and pinpointed the challenges in achieving this goal. We propose three design opportunities for domestic technology to address this gap: strengthening the meaning of housework around family values, supporting recognition of the partner's efforts through visualization, and initiating negotiation through defamiliarization. These insights extend the design considerations for domestic technologies, advocating for a broader understanding of the values contributing to satisfactory homemaking activities within the household.2025GBGahyeon Bae et al.KAIST, Department of Industrial DesignSmart Home Interaction DesignParticipatory DesignCHI
Peerspective: A Study on Reciprocal Tracking for Self-awareness and Relational InsightPersonal informatics helps individuals understand themselves, but it often struggles to capture non-conscious behaviors such as stress responses, habitual actions, and communication styles. Incorporating social aspects into PI systems offers new perspectives on self-understanding, yet prior research has largely focused on unidirectional approaches that center benefits on the primary tracker. To address this gap, we introduce the Peerspective study, which explores reciprocal tracking---a bidirectional practice where two participants observe and provide feedback to each other, fostering mutual self-understanding and collaboration. In a week-long study with eight peer dyads, we explored how reciprocal observation and feedback influence self-awareness and interpersonal relationships. Our findings reveal that reciprocal tracking not only helps participants uncover blind spots and expand their self-concepts but also enhances empathy, deepens communication, and promotes sustained engagement. We discuss key facilitators and challenges of integrating reciprocity into personal informatics systems and offer design considerations for supporting collaborative tracking in everyday contexts.2025KLKwangyoung Lee et al.KAIST, Department of Industrial DesignCollaborative Learning & Peer TeachingMental Health Apps & Online Support CommunitiesContext-Aware ComputingCHI
AACessTalk: Fostering Communication between Minimally Verbal Autistic Children and Parents with Contextual Guidance and Card RecommendationAs minimally verbal autistic (MVA) children communicate with parents through few words and nonverbal cues, parents often struggle to encourage their children to express subtle emotions and needs and to grasp their nuanced signals. We present AACessTalk, a tablet-based, AI-mediated communication system that facilitates meaningful exchanges between an MVA child and a parent. AACessTalk provides real-time guides to the parent to engage the child in conversation and, in turn, recommends contextual vocabulary cards to the child. Through a two-week deployment study with 11 MVA child-parent dyads, we examine how AACessTalk fosters everyday conversation practice and mutual engagement. Our findings show high engagement from all dyads, leading to increased frequency of conversation and turn-taking. AACessTalk also encouraged parents to explore their own interaction strategies and empowered the children to have more agency in communication. We discuss the implications of designing technologies for balanced communication dynamics in parent-MVA child interaction.2025DCDasom Choi et al.KAIST, Department of Industrial DesignCognitive Impairment & Neurodiversity (Autism, ADHD, Dyslexia)Augmentative & Alternative Communication (AAC)CHI
Beyond Swipes and Scores: Investigating Practices, Challenges and User-Centered Values in Online Dating AlgorithmsThe reliability of online dating algorithms has sparked considerable debate, particularly regarding skepticism about their excessive emphasis on evaluating and getting evaluated which often overshadows the quest for authentic romantic connections. To understand the multifaceted influence of dating algorithms on end-users and explore avenues for algorithmic features considering the dynamics of human relationships, we conducted a mixed-methods study comprising in-depth interviews (N = 22) and a metaphoric co-design workshop (N = 12) with active users of online dating platforms. Interviews revealed that users perceive and respond to algorithmic evaluations with varied perception and behaviors, often expressing concerns about the emotional burden of constant self-presentation and the pursuit of quantitative assessments over genuine connections. In the design workshop, users envisioned desired algorithmic features to overcome investigated challenges, such as prioritizing personal values, tailored matchmaking, and support for personal growth in relationships. This research contributes to unravel the complex dynamics of human-algorithm interaction in the context of online dating. By aligning algorithmic functions more closely with user desires and relationship goals, this study paves the way for more meaningful and authentic connections in the digital dating landscape.2024CKChowon Kang et al.Session 3a: Self-Presentation and Relationships in Digital SpacesCSCW
Co-Creating Question-and-Answer Style Articles with Large Language Models for Research PromotionResearch promotion enables researchers to share advanced knowledge with pertinent academic communities. The question-and-answer (QA) style articles are effective for researchers to promote their research by enabling readers to understand research on complex subjects. Recent advances in large language models (LLMs) have opened avenues for supporting researchers in creating QA-style articles for research promotion. However, without the authors' involvement, these models may only partially capture the researcher's intention and voice. We developed AQUA, a research probe that enables researchers to co-create QA-style articles with LLMs to promote their research papers. A user study (n=12) reveals that LLMs reduced authors' burden and helped them understand the readers' perspectives. Nevertheless, LLMs failed to capture the unique intent of the authors, and their automated generation discouraged authors from carefully revising their answers. Based on our findings, we discuss human-LLM interaction design to enable authors to create QA-style articles that reflect their intention.2024HLHyunseung Lim et al.Human-LLM CollaborationData StorytellingDIS
DiaryMate: Understanding User Perceptions and Experience in Human-AI Collaboration for Personal JournalingWith their generative capabilities, large language models (LLMs) have transformed the role of technological writing assistants from simple editors to writing collaborators. Such a transition emphasizes the need for understanding user perception and experience, such as balancing user intent and the involvement of LLMs across various writing domains in designing writing assistants. In this study, we delve into the less explored domain of personal writing, focusing on the use of LLMs in introspective activities. Specifically, we designed DiaryMate, a system that assists users in journal writing with LLM. Through a 10-day field study (N=24), we observed that participants used the diverse sentences generated by the LLM to reflect on their past experiences from multiple perspectives. However, we also observed that they are over-relying on the LLM, often prioritizing its emotional expressions over their own. Drawing from these findings, we discuss design considerations when leveraging LLMs in a personal writing practice.2024TKTaewan Kim et al.KAISTHuman-LLM CollaborationMental Health Apps & Online Support CommunitiesAI-Assisted Creative WritingCHI
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
MindfulDiary: Harnessing Large Language Model to Support Psychiatric Patients' JournalingLarge Language Models (LLMs) offer promising opportunities in mental health domains, although their inherent complexity and low controllability elicit concern regarding their applicability in clinical settings. We present MindfulDiary, an LLM-driven journaling app that helps psychiatric patients document daily experiences through conversation. Designed in collaboration with mental health professionals, MindfulDiary takes a state-based approach to safely comply with the experts' guidelines while carrying on free-form conversations. Through a four-week field study involving 28 patients with major depressive disorder and five psychiatrists, we examined how MindfulDiary facilitates patients' journaling practice and clinical care. The study revealed that MindfulDiary supported patients in consistently enriching their daily records and helped clinicians better empathize with their patients through an understanding of their thoughts and daily contexts. Drawing on these findings, we discuss the implications of leveraging LLMs in the mental health domain, bridging the technical feasibility and their integration into clinical settings.2024TKTaewan Kim et al.KAISTHuman-LLM CollaborationAI-Assisted Decision-Making & AutomationMental Health Apps & Online Support CommunitiesCHI
Unlock Life with a Chat(GPT): Integrating Conversational AI with Large Language Models into Everyday Lives of Autistic IndividualsAutistic individuals often draw on insights from their supportive networks to develop self-help life strategies ranging from everyday chores to social activities. However, human resources may not always be immediately available. Recently emerging conversational agents (CAs) that leverage large language models (LLMs) have the potential to serve as powerful information-seeking tools, facilitating autistic individuals to tackle daily concerns independently. This study explored the opportunities and challenges of LLM-driven CAs in empowering autistic individuals through focus group interviews and workshops (N=14). We found that autistic individuals expected LLM-driven CAs to offer a non-judgmental space, encouraging them to approach day-to-day issues proactively. However, they raised issues regarding critically digesting the CA responses and disclosing their autistic characteristics. Based on these findings, we propose approaches that place autistic individuals at the center of shaping the meaning and role of LLM-driven CAs in their lives, while preserving their unique needs and characteristics.2024DCDasom Choi et al.KAISTHuman-LLM CollaborationCognitive Impairment & Neurodiversity (Autism, ADHD, Dyslexia)CHI
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
“Enjoy, but Moderately!”: Designing a Social Companion Robot for Social Engagement and Behavior Moderation in Solitary Drinking ContextSocially assistive robots can support people in making behavior changes by socially engaging in or moderating certain behaviors, such as physical exercise and snacking. However, there has not been much work on designing social robots that aim to support both social engagement and behavior moderation, i.e., offering social interactions for engaging in behaviors without over-engagement. This work explores how social robots can moderate alcohol consumption while socially engaging them in a solitary drinking context. As alcohol consumption can have benefits when done in moderation, this companion robot aims to guide the user toward moderate drinking by using social engagement (i.e., creating an enjoyable atmosphere) and drinking moderation (i.e., regulating the drinking pace). Our preliminary user study (n=20) reveals that the robot is perceived as a friendly companion, and its human-likeness is partly attributed to the robot's intervention. Most participants followed the robot's guidance and perceived it as an intelligent friend due to its social interactions and behavior tracking features. We discuss the benefit of physical interactions for social engagement, utilizing interaction rituals for enjoyable but moderate commensality, and ethical considerations in solitary drinking contexts.2023YJYugyeong Jung et al.Human Robot InteractionCSCW