Embodied Digital Therapists with LLM Personalization for Aphasia Rehabilitation: Characterizing Human-AI Collaboration BoundariesAphasia following stroke affects millions globally, yet rehabilitation remains severely limited by speech therapist shortages. Existing digital systems rely on static video demonstrations, single-modality assessment, and rule-based feedback, failing to address authentic clinical needs. Through formative investigation with five therapists, three patients, and their caregivers, we identified concrete clinical challenges: therapists spending 30-40% of time on repetitive demonstrations, existing tools providing only speech scores without articulatory evaluation, and patients struggling with complex interfaces and monotonous content. To address these challenges, we developed an integrated rehabilitation system combining an embodied digital therapist for Action Observation Therapy, tri-dimensional assessment coordinating speech quality, lip movement accuracy, and semantic understanding, and large language model-driven personalization for content generation and adaptive training. We conducted a proof-of-concept evaluation across two in-situ hospital training sessions with six patients, six caregivers, and three therapists. Results demonstrated substantial efficiency gains, with therapists spending 69-78% less time per patient. Patient acceptance improved 42.6% across sessions, and low digital literacy patients showed steepest gains (+69.0%). However, human intervention remained necessary for 24-30% of session time to provide emotional support. These findings empirically characterize human-AI collaboration boundaries in clinical rehabilitation, revealing both automation's potential to enhance efficiency and bridge digital divides, and the persistent necessity of human therapeutic presence—providing evidence for responsible deployment of AI-assisted healthcare systems.2026MYMengting Yu et al.Southeast UniversityBrain-Computer Interface (BCI) & NeurofeedbackTelemedicine & Remote Patient MonitoringVR Medical Training & RehabilitationIUI
Understanding Nature Engagement Experiences of Blind PeopleNature plays a crucial role in human health and well-being, but little is known about how blind people experience and relate to it. We conducted a survey of nature relatedness with blind (N=20) and sighted (N=20) participants, along with in-depth interviews with 16 blind participants, to examine how blind people engage with nature and the factors shaping this engagement. Our survey results revealed lower levels of nature relatedness among blind participants compared to sighted peers. Our interview study further highlighted: 1) current practices and challenges of nature engagement, 2) attitudes and values that shape engagement, and 3) expectations for assistive technologies that support safe and meaningful engagement. We also provide design implications to guide future technologies that support nature engagement for blind people. Overall, our findings illustrate how blind people experience nature beyond vision and lay a foundation for technologies that support inclusive nature engagement.2026MTMengjie Tang et al.Southeast UniversityVisual Impairment Technologies (Screen Readers, Tactile Graphics, Braille)Universal & Inclusive DesignHuman-Nature Relationships (More-than-Human Design)CHI
DiLLS: Interactive Diagnosis of LLM-based Multi-agent Systems via Layered Summary of Agent BehaviorsLarge language model (LLM)-based multi-agent systems have demonstrated impressive capabilities in handling complex tasks. However, the complexity of agentic behaviors makes these systems difficult to understand. When failures occur, developers often struggle to identify root causes and to determine actionable paths for improvement. Traditional methods that rely on inspecting raw log records are inefficient, given both the large volume and complexity of data. To address this challenge, we propose a framework and an interactive system, DiLLS, designed to reveal and structure the behaviors of multi-agent systems. The key idea is to organize information across three levels of query completion: activities, actions, and operations. By probing the multi-agent system through natural language, DiLLS derives and organizes information about planning and execution into a structured, multi-layered summary. Through a user study, we show that DiLLS significantly improves developers’ effectiveness and efficiency in identifying, diagnosing, and understanding failures in LLM-based multi-agent systems.2026RSRui Sheng et al.The Hong Kong University of Science and TechnologyHuman-LLM CollaborationAI-Assisted Decision-Making & AutomationExplainable AI (XAI)CHI
NatureCapture: Transforming Real-World Nature into Shared VR Experiences for Deepened Human-Nature ConnectionUrbanization has limited people’s daily exposure to nature, reducing nature connection and overall well-being. Although virtual reality (VR) offers an alternative access to nature-like environments, it often lacks authenticity and connection to physical experiences. We present NatureCapture, a multi-user system that bridges real-world and virtual nature engagement by allowing users to capture real-world natural elements and collaboratively integrate them as interactive 3D models into a shared VR nature space. A two-week within-subjects study indicated that NatureCapture promoted nature connectedness, emotional regulation, and social closeness. Interviews further revealed that NatureCapture supported immersive and extended interactions with nature, promoted reciprocal engagement in virtual and real environments, and strengthened nature interaction through socially mediated engagement. Our work highlights the potential of user-driven real-to-3D transformation to preserve physical encounters with nature, combining the spatial flexibility and interactivity of VR. Such cross-environment paradigm offers a novel approach for sustaining human-nature connection in technologically mediated life.2026YZYidan Zhang et al.School of Computer Science and Engineering, Southeast UniversityImmersion & Presence ResearchHuman-Nature Relationships (More-than-Human Design)Social & Collaborative VRCHI
Towards Understanding the Design of Shared Bodily Control via Exoskeleton-based PlayEmerging technologies such as exoskeletons and electrical muscle stimulation can initiate movement within the human body, blurring the boundary between user and machine. While prior research has explored how such systems augment bodily action, most focus on movement execution rather than decision-making. In this work, we investigate what happens when a bodily-integrated system acts with its own logic and initiates bodily movement alongside users. We present three game scenarios where an exoskeleton controls one arm while the user controls the other, designed to evoke different relational framings: proxy, collaboration, and opposition. Through a qualitative study (N = 16), we examine how users interpret such interactions, and how shared bodily control shapes bodily experience and human-machine relationship. We further contribute a set of implications for designing bodily technologies that decide and move together with users, opening up design possibilities for systems that share bodily control, not merely actuate on users' behalf.2026ZLZhuying Li et al.Southeast UniversityForce Feedback & Pseudo-Haptic WeightSerious & Functional GamesHuman-Robot Collaboration (HRC)CHI
Depictions of Privacy Invasion and Surveillance in Artworks and Potential Lessons For Privacy CommunicationUser-facing communication about privacy (e.g., privacy policies, privacy tools' user interfaces) is frequently ignored and often ineffective. In contrast to these arguably staid interfaces, artworks often focus on provocation, engagement, and critical interpretation. For decades, artists have created privacy art—artistic media in galleries relating to the surveillance and privacy of individuals. What are artists saying about privacy, and how? Crucially, what lessons might they have for designing privacy-focused user interfaces? To this end, we compiled over 800 privacy artworks, qualitatively analyzing a sample. Common topics spanned artistic media (from paintings to immersive installations) and eras. Artworks built upon familiar concepts (e.g., cameras, homes) to speculate on society's future and present personal information (e.g., artist, viewer, public). We discuss lessons for making non-artistic privacy communication more engaging and powerful through directing attention (e.g., lighting, collage) and setting a tone (e.g., unsettling, fun, mundane).2026TETess Eschebach et al.University of ChicagoPrivacy by Design & User ControlDigital Art Installations & Interactive PerformanceTechnology Ethics & Critical HCICHI
Virtual Minds, Real Work: LLM-Powered Preference-Based Planning through Spatial Multi-Agent-Human CollaborationPeople frequently face preference-based planning tasks requiring balancing goals with nuanced constraints, yet even advanced LLMs demand considerable effort to produce and adjust plans reflecting complex user preferences. We present MAVIS (Multi-Agent Virtual Interactive Synergy), a multi-agent system within a virtual workspace that introduces an incremental collaboration mechanism. This mechanism automatically decomposes tasks into guidelines and sequentially introduces expert agents. Each agent proactively engages users in focused dialog to uncover implicit preferences, while successive agents add perspectives and transparently negotiate trade-offs. To mitigate textual overload, MAVIS employs spatial visualizations that externalize agents' reasoning through step-linked summaries and context-aware boards, with embodied avatars supporting natural interaction. Across studies, Study 1 showed our collaboration mechanism doubled expressed preferences and improved planning quality by 60.3% over a conventional LLM baseline. Study 2 affirmed visualization's benefits over a non-spatial baseline, while Study 3 confirmed its versatility across VR and desktop modalities and diverse tasks.2026ZZZiyi Zhang et al.Southeast UniversityHuman-LLM CollaborationMixed Reality WorkspacesImmersion & Presence ResearchCHI
GreenCompass: Weaving Playful Nature Engagement into Urban Micro-Moments through Context-Aware GamificationUrbanization increasingly separates people from nature, negatively affecting well-being. While prior work has explored technological interventions to foster human-nature interaction (HNI), embedding nature engagement into daily urban life remains challenging. We present GreenCompass, a social mobile application that recommends nearby micro-opportunities for nature encounters through context-aware scheduling, gamification, and adaptive tasks. To evaluate its effectiveness, we conducted a four-week mixed-methods field study with 40 participants split into two conditions: GreenCompass and a reminder-based baseline. Results showed that GreenCompass better improved nature relatedness, outdoor activity levels, well-being, perceived stress, and social connectedness. Our analysis identified four design themes: embedding nature into micro-moment integration, collective nature engagement, cultivating intrinsic bonds with nature, and system challenges. We further provide design implications to support everyday urban nature engagement. This work demonstrates how everyday mobile technology can help bridge the urban-nature divide and promote well-being.2026MWMingtao Wu et al.Southeast UniversitySustainable HCIEcological Design & Green ComputingBehavior Change & Reflection TechnologyCHI
Zenergy: Designing Taoist-Inspired Transformative Nature Imagery for Everyday EmpowermentNature has long been valued for its restorative impact on emotion and well-being, motivating many HCI systems to incorporate nature as a calming design material. However, cultural traditions such as Taoism frame nature not as passive, but as an active, symbolic force for emotional transformation. We present Zenergy, a mobile application that uses a large language model to generate personalized guided meditations grounded in Taoist nature imagery. Based on users' emotional and contextual input, Zenergy leads them through a symbolic journey using natural metaphors such as wind to release burdens, rivers to restore flow, and sunlight to renew strength. A mixed-method field study (N = 27) showed that Zenergy enhanced users' self-efficacy, emotional clarity, and spiritual connection. We introduce transformative nature imagery as a design lens for everyday empowerment, and offer strategies for embedding culturally grounded symbolism into interactive well-being technologies.2026ZLZhuying Li et al.Southeast UniversityGenerative AI (Text, Image, Music, Video)Mental Health Apps & Online Support CommunitiesAffective Feedback & Emotion Regulation InterfacesCHI
Grand Challenges around Designing Computers’ Control Over Our BodiesAdvances in emerging technologies, such as on-body mechanical actuators and electrical muscle stimulation, have allowed computers to take control over our bodies. This presents opportunities as well as challenges, raising fundamental questions about agency and the role of our body when interacting with technology. To advance this research field as a whole, we brought together expert perspectives in a week-long seminar to articulate the grand challenges that should be tackled when it comes to the design of computers’ control over our bodies. These grand challenges span technical, design, user, and ethical aspects. By articulating these grand challenges, we aim to begin initiating a research agenda that positions bodily control not only as a technical feature but as a central, experiential, and ethical concern for future human–computer interaction endeavors.2026FMFlorian 'Floyd' Mueller et al.Monash UniversityElectrical Muscle Stimulation (EMS)Brain-Computer Interface (BCI) & NeurofeedbackEmpathy & Emotional DesignCHI
ComViewer: An Interactive Visual Tool to Help Viewers Seek Social Support in Online Mental Health CommunitiesOnline mental health communities (OMHCs) offer rich posts and comments for viewers, who do not directly participate in the communications, to seek social support from others’ experience. However, viewers could face challenges in finding helpful posts and comments and digesting the content to get needed support, as revealed in our formative study (N=10). In this work, we present an interactive visual tool named ComViewer to help viewers seek social support in OMHCs. With ComViewer, viewers can filter posts of different topics and find supportive comments via a zoomable circle packing visual component that adapts to searched keywords. Powered by LLM, ComViewer supports an interactive sensemaking process by enabling viewers to interactively highlight, summarize, and question any community content. A within-subjects study (N=20) demonstrates ComViewer’s strengths in providing viewers with a more simplified, more fruitful, and more engaging support-seeking experience compared to a baseline OMHC interface without ComViewer. We further discuss design implications for facilitating information-seeking and sense making in online mental health communities.2025SWShiwei Wu et al.Designing for Mental Health SupportCSCW
Script-Strategy Aligned Generation: Aligning LLMs with Expert-Crafted Dialogue Scripts and Therapeutic Strategies for PsychotherapyChatbots or conversational agents (CAs) are increasingly used to improve access to digital psychotherapy. Many current systems rely on rigid, rule-based designs, heavily dependent on expert-crafted dialogue scripts for guiding therapeutic conversations. Although advances in large language models (LLMs) offer potential for more flexible interactions, their lack of controllability and explanability poses challenges in psychotherapy. In this work, we explored how aligning LLMs with expert-crafted scripts can enhance psychotherapeutic chatbot performance. Our comparative Study 1 showed that LLMs aligned with expert-crafted scripts through prompting and fine-tuning significantly outperformed both pure LLMs and rule-based chatbots, achieving an effective balance between dialogue flexibility and adherence to therapeutic principles. Building on findings, we proposed ``Script-Strategy Aligned Generation (SSAG)'', a more flexible alignment approach that reduces reliance on fully scripted content while maintaining LLMs' therapeutic adherence and controllability. In a 10-day field Study 2, SSAG demonstrated performance comparable to full script alignment, empirically supporting SSAG as an efficient approach for aligning LLMs with domain expertise. Our work advances LLM applications in psychotherapy by providing a controllable and scalable solution, reducing reliance on expert effort. It also provides a collaborative framework for domain experts and developers to efficiently build expertise-aligned chatbots, broadening access to broader context of psychotherapy.2025XSXin Sun et al.Facilitating Support and BelongingCSCW
CoGrader: Transforming Instructors' Assessment of Project Reports through Collaborative LLM IntegrationGrading project reports are increasingly significant in today’s educational landscape, where they serve as key assessments of students' comprehensive problem-solving abilities. However, it remains challenging due to the multifaceted evaluation criteria involved, such as creativity and peer-comparative achievement. Meanwhile, instructors often struggle to maintain fairness throughout the time-consuming grading process. Recent advances in AI, particularly large language models, have demonstrated potential for automating simpler grading tasks, such as assessing quizzes or basic writing quality. However, these tools often fall short when it comes to complex metrics, like design innovation and the practical application of knowledge, that require an instructor’s educational insights into the class situation. To address this challenge, we conducted a formative study with six instructors and developed CoGrader, which introduces a novel grading workflow combining human-LLM collaborative metrics design, benchmarking, and AI-assisted feedback. CoGrader was found effective in improving grading efficiency and consistency while providing reliable peer-comparative feedback to students. We also discuss design insights and ethical considerations for the development of human-AI collaborative grading systems.2025ZCZixin Chen et al.Human-LLM CollaborationIntelligent Tutoring Systems & Learning AnalyticsSTEM Education & Science CommunicationUIST
Navigating the Unknown: A Chat-Based Collaborative Interface for Personalized Exploratory TasksThe rise of large language models (LLMs) has revolutionized user interactions with knowledge-based systems, enabling chatbots to synthesize vast amounts of information and assist with complex, exploratory tasks. However, LLM-based chatbots often struggle to provide personalized support, particularly when users start with vague queries or lack sufficient contextual information. This paper introduces the Collaborative Assistant for Personalized Exploration (CARE), a system designed to enhance personalization in exploratory tasks by combining a multi-agent LLM framework with a structured user interface. CARE's interface consists of a Chat Panel, Solution Panel, and Needs Panel, enabling iterative query refinement and dynamic solution generation. The multi-agent framework collaborates to identify both explicit and implicit user needs, delivering tailored, actionable solutions. In a within-subject user study with 22 participants, CARE was consistently preferred over a baseline LLM chatbot, with users praising its ability to reduce cognitive load, inspire creativity, and provide more tailored solutions. Our findings highlight CARE's potential to transform LLM-based systems from passive information retrievers to proactive partners in personalized problem-solving and exploration. The code will be made available at https://aka.ms/chatbot-care.2025YPYingzhe Peng et al.Human-LLM CollaborationCrowdsourcing Task Design & Quality ControlIUI
Light Up Fireflies: Exploring the Design of Interpersonal Bodily Intertwinement in Social Body GamesThis paper explores the design of interpersonal bodily intertwinement in social body games. We present ``Light Up Fireflies'', a two-player VR game where players embody a single avatar, with each player responsible for controlling one half of the avatar’s body. Players must coordinate closely to navigate the virtual environment and engage with the game’s tasks, where any misalignment might cause the avatar to fall. Unlike previous research, which often focused on partial or segmented bodily interactions, our game encourages a fully integrated form of bodily coordination. Players do not merely react to each other’s movements but co-experience the avatar's body, fostering a richer and more immersive connection between them. Through a study with 16 participants, we identified three key player experiences: bodily strangeness, intertwined bodily movements, and interpersonal bodily understanding. We also provide design implications for future social body games that aim to facilitate deeper, more intertwined embodied experiences.2025YLYingtong Lu et al.Southeast University, School of Computer Science and EngineeringFull-Body Interaction & Embodied InputSocial & Collaborative VRCHI
Scaffolded Turns and Logical Conversations: Designing Humanized LLM-Powered Conversational Agents for Hospital Admission InterviewsHospital admission interviews are critical for patient care but strain nurses' capacity due to time constraints and staffing shortages. While LLM-powered conversational agents (CAs) offer automation potential, their rigid sequencing and lack of humanized communication skills risk misunderstandings and incomplete data capture. Through participatory design with clinicians and volunteers, we identified essential communication strategies and developed a novel CA that implements these strategies through: (1) dynamic topic management using graph-based conversation flows, and (2) context-aware scaffolding with few-shot prompt tuning. Technical evaluation on an admission interview dataset showed our system achieving performance comparable to or surpassing human-written ground truth, while outperforming prompt-engineered baselines. A between-subject study (N=44) demonstrated significantly improved user experience and data collection accuracy compared to existing solutions. We contribute a framework for humanizing medical CAs by translating clinician expertise into algorithmic strategies, alongside empirical insights for balancing efficiency and empathy in healthcare interactions, and considerations for generalizability.2025DLDingdong Liu et al.The Hong Kong University of Science and TechnologyConversational ChatbotsHuman-LLM CollaborationCHI
iGripper: A Semi-Active Handheld Haptic VR Controller Based on Variable Stiffness MechanismWe introduce iGripper, a handheld haptic controller designed to render stiffness feedback for gripping and clamping both rigid and elastic objects in virtual reality. iGripper directly adjusts physical stiffness by using a small linear actuator to modify the spring’s position along a lever arm, with feedback force generated by the spring's reaction to the user's input. This enables iGripper to render stiffness from zero to any specified value, determined by the spring's inherent stiffness. Additionally, a blocking mechanism is designed to provide fully rigid feedback to enlarge the rendering range. Compared to active controllers, iGripper offers a broad range of force and stiffness feedback without requiring high-power actuators. Unlike many passive controllers, which provide only braking force, iGripper, as a semi-active controller, delivers controllable elastic force feedback. We present the iGripper’s design, performance evaluation, and user studies, comparing its realism with a commercial impedance-type grip device.2025KSKe Shi et al.Southeast University, School of Instrument Science and Engineering; National University of Singapore, Department of Biomedical EngineeringForce Feedback & Pseudo-Haptic WeightShape-Changing Interfaces & Soft Robotic MaterialsCHI
Chorus of the Past: Toward Designing a Multi-agent Conversational Reminiscence System with Digital Artifacts for Older AdultsReminiscence has been shown to provide benefits for older adults, but traditionally relies on personal photos as memory cues and interactions with real people who may not always be available. We present ReminiBuddy, a novel LLM-powered multi-agent conversational system, which allows older adults to engage with two distinct agents—one embodying an older identity and the other a younger identity—while using not only personal photos but also 3D models of generic nostalgic objects as memory cues. Our study, with older adult participants, found that the conversational approach both enjoyable and beneficial for reminiscence. While the younger agent was perceived as more emotionally engaging, the older one fostered greater resonance in content. Personal photos prompted autobiographical memories, whereas 3D generic nostalgic objects evoked shared memories of an era, contributing to a more multifaceted reminiscence experience. We further present design implications for better supporting older adults in reminiscing with LLM-powered conversational agents.2025JSJingwei Sun et al.Lenovo ResearchHuman-LLM CollaborationElderly Care & Dementia SupportCHI
LumaDreams: Designing Positive Dream Meaning-Making for Daily EmpowermentDreams contribute to cognitive and emotional health, yet tools for everyday dream engagement remain largely underexplored outside clinical settings. In this paper, we introduce LumaDreams, a mobile application designed to foster daily empowerment through positive dream transformation using generative AI. Informed by meaning-making theories, LumaDreams enables users to journal dreams through sketches and text, which are then transformed into positive images and stories for users to revisit and reflect on. We conducted a mixed-method study with 14 participants over 14 days. Our findings show that LumaDreams strengthened participants’ daily empowerment through cognitive and emotional shifts that arise from the positive meaning-making process. Qualitative insights further revealed how users’ perceptions and trust of AI-driven dream transformation were shaped through their interactions. In conclusion, we propose an inspiring approach that enables users to co-create positive meanings in dream experiences with generative AI, promoting cognitive and emotional shifts, fostering positive mindsets, and ultimately strengthening daily empowerment.2025BLBolin Lyu et al.Southeast University, School of Computer Science and EngineeringGenerative AI (Text, Image, Music, Video)Mental Health Apps & Online Support CommunitiesCHI
Integrating Equity in Public Sector Data-Driven Decision Making: Exploring the Desired Futures of Underserved StakeholdersPublic sectors aim to innovate not just for efficiency but also to enhance equity. Despite the growing adoption of data-driven decision-making systems in the public sector, efforts to integrate equity as a primary goal often fall short. This typically arises from inadequate early-stage involvement of the underserved stakeholders and prevalent misunderstandings concerning the authentic meaning of equity from these stakeholders' perspectives. Our research seeks to address this gap by actively involving undersevered stakeholders in the process of envisioning the integration of equity within public sector data-driven decisions, particularly in the context of a building department in a Northeastern mid-sized U.S. city. Applying a speed dating method with storyboards, we explore diverse equity-centric futures within the realm of local business development, a domain where small businesses, particularly women- and minority-owned businesses, historically confront inequitable distribution of public services. We explored three essential aspects of equity: monitoring equity, resource allocation prioritization, as well as information and equity. Our findings illuminate the complexities of integrating equity into data-driven decisions, offering nuanced insights about the needs of stakeholders. We found that attempts to monitor and incorporate equity goals into public sector decision-making can unexpectedly backfire, inadvertently sparking community apprehension and potentially exacerbating existing inequities. Small business owners, including those identifying as women- and minority-owned, advocated against the use of demographic-based data in equity-focused data-driven decision-making in the public sector, instead emphasizing factors like community needs, application complexity, and inherent small business uncertainties. Drawing from these insights, we propose design implications to assist designers of public sector data-driven decision-making systems better accommodate equity considerations.2024SKSeyun Kim et al.Session 2e: Data, Power, and JusticeCSCW