arXiv subjects

Dongyijie Primo Pan

Publications and source records attributed to Dongyijie Primo Pan.

9 recordsLinked to original sources

arXiv2026

Dream-Butterfly: Configuring Embodied Conversational Guidance for Public Outdoor Mixed Reality Exhibitions

Public outdoor mixed reality (MR) exhibitions make guidance a spatial interaction problem: visitors need timely explanations while roaming open sites, attending to virtual artworks, hazards, bystanders, and route choices. We present Dream-Butterfly, a field-deployed embodied conversational guide for campus-scale outdoor MR exhibitions. Visitors explicitly summon the lightweight non-humanoid guide; it returns visibly to a hand-relative dialogue position and answers using retrieval-augmented responses scoped by the MR runtime to the artwork currently encountered. Within the resulting mixed guidance ecology, staff remain accountable for safety, wayfinding, device support, and contingencies. Dream-Butterfly was deployed with over 30 spatially anchored artworks across a 26,000 m$^2$ campus site, which we use as a stress case for public MR guidance under walking, field-of-view limits, route choice, bystander exposure, and safety constraints. In an in-the-wild study with 24 visitors, we compared two role arrangements: agent-default, where the embodied guide served as the default channel for artwork interpretation, and docent-default, where staff provided primary narration while the agent remained optional. Agent-default guidance was associated with more available, visitor-timed explanations, higher immersion/engagement, and stronger hedonic quality, while increasing visitors' self-curation burden around pacing, attention management, and question formulation. We argue that making interpretation available at visitors' chosen moments improves the timing of explanation in outdoor MR and shifts more work to visitors: deciding when to stop, what to ask, how deeply to engage, and when to move on.

cs.HC↗

arXiv2026

Can I Trust My Body? A Three-Year Autoethnography of ChatGPT's Place in My Support System for Panic Attacks

People increasingly seek mental health support from large language models, yet little is known about their use across years of recurrent panic. We present a three-year analytic autoethnography of the first author's ChatGPT use while living with panic disorder, drawing on conversations, personal records, and accounts from friends or family members and professionals. Narrative analysis traces how my questions shaped ChatGPT's roles and how earlier experiences influenced later responses to symptoms. Familiar explanations could make sensations less frightening, while changed symptoms renewed fears of serious illness. During sudden panic, advice could be difficult to follow, and some replies prompted further checking. Conversations could end while symptoms, checking, or help-seeking continued. We propose trajectory-level safety during and after panic: usable advice (Fit), a stopping point for repeated checking and reassurance seeking (Closure), and useful understanding and human support that remain available over time (Continuity).

cs.HC↗

arXiv2026

On Edge in the Dental Chair: Designing VR Support for Moments of Dental Anxiety

Dental anxiety can change as a procedure unfolds, yet dental virtual reality (VR) commonly provides continuous distraction or relaxation. We investigate how support can be coordinated with specific simulated dental events. Stakeholder interviews (N=36), participatory design with three returning dentists, and patient walkthroughs of a no-intervention prototype (N=12) informed five Anxiety Events and an intervention-module framework. Drawing on cognitive vulnerability and emotion regulation, we implemented a standardized event-contingent VR system with predefined event-module assignments and shared agency and safety controls. A randomized study (N=24) compared the intervention package with no-intervention VR. The adjusted intervention-minus-control difference averaged -12.83 VAS-A points across events (95% CI [-24.42, -1.70]). Physiological, behavioural, and qualitative measures contextualized participants' experiences. The findings inform timely, comprehensible support and reassuring social presence in simulated dental VR; they concern the complete package rather than individual modules or clinical effectiveness.

cs.HC↗

arXiv2026

Meflex: A Multi-agent Scaffolding System for Entrepreneurial Ideation Iteration via Nonlinear Business Plan Writing

Business plan (BP) writing plays a key role in entrepreneurship education by helping learners construct, evaluate, and iteratively refine their ideas. However, conventional BP writing remains a rigid, linear process that often fails to reflect the dynamic and recursive nature of entrepreneurial ideation. This mismatch is particularly challenging for novice entrepreneurial students, who struggle with the substantial cognitive demands of developing and refining ideas. While reflection and meta-reflection are critical strategies for fostering divergent and convergent thinking, existing writing tools rarely scaffold these higher-order processes. To address this gap, we present the Meflex System, a large language model (LLM)-based writing tool that integrates BP writing scaffolding with a nonlinear idea canvas to support iterative ideation through reflection and meta-reflection. We report findings from an exploratory user study with 30 participants that examined the system's usability and cognitive impact. Results show that Meflex effectively scaffolds BP writing, promotes divergent thinking through LLM-supported reflection, and enhances meta-reflective awareness while reducing cognitive load during complex idea development. These findings highlight the potential of non-linear LLM-based writing tools to foster deeper and coherent entrepreneurial thinking.

cs.HC↗

arXiv2026

MedEasy: Designing AI Standardized Patients for Clinical Consultation Training

AI standardized patients are becoming a setting for professional training in clinical consultation. This paper presents MedEasy, a multi-agent system that organizes virtual-patient practice through patient dialogue, clinical actions, decision submission, documentation, and feedback. We first conducted a formative study with 12 clinical-year medical students through interviews and three co-design workshops. The findings informed a staged workflow, structured case records, action-contingent findings, and trajectory-based review. We then conducted an evaluative user study with a separate cohort of 12 clinical-year medical students, with each participant completing two counterbalanced cases. Learners interpreted MedEasy as a connected consultation environment. They used patient responses, examination findings, available actions, and feedback together to judge whether the represented case remained coherent. They valued repeatable practice and recorded review, while questioning missing actions and feedback criteria. The paper contributes design implications for AI-supported professional training systems that use case-specific standards to connect situated practice.

cs.HC↗

arXiv2026

FAIR: Framing AIs Role in Programming Competitions -- Understanding How LLMs Are Changing the Game in Competitive Programming

This paper investigates how large language models (LLMs) are reshaping competitive programming. The field functions as an intellectual contest within computer science education and is marked by rapid iteration, real-time feedback, transparent solutions, and strict integrity norms. Prior work has evaluated LLMs performance on contest problems, but little is known about how human stakeholders -- contestants, problem setters, coaches, and platform stewards -- are adapting their workflows and contest norms under LLMs-induced shifts. At the same time, rising AI-assisted misuse and inconsistent governance expose urgent gaps in sustaining fairness and credibility. Drawing on 37 interviews spanning all four roles and a global survey of 207 contestants, as well as an API-based crawl of Codeforces contest logs (2022-2025) for quantitative analysis, we contribute: (i) an empirical account of evolving workflows, (ii) an analysis of contested fairness norms, and (iii) a chess-inspired governance approach with actionable measures -- real-time LLMs checks in online contests, peer co-monitoring and reporting, and cross-validation against offline performance -- to curb LLMs-assisted misuse while preserving fairness, transparency, and credibility.

cs.HC↗

arXiv2026

"It Talks Like a Patient, But Feels Different": Co-Designing AI Standardized Patients with Medical Learners

Standardized patients (SPs) play a central role in clinical communication training but are costly, difficult to scale, and inconsistent. Large language model (LLM) based AI standardized patients (AI-SPs) promise flexible, on-demand practice, yet learners often report that they talk like a patient but feel different. We interviewed 12 clinical-year medical students and conducted three co-design workshops to examine how learners experience constraints of SP encounters and what they expect from AI-SPs. We identified six learner-centered needs, translated them into AI-SP design requirements, and synthesized a conceptual workflow. Our findings position AI-SPs as tools for deliberate practice and show that instructional usability, rather than conversational realism alone, drives learner trust, engagement, and educational value.

cs.HC↗

arXiv2026

Prompting Destiny: Negotiating Socialization and Growth in an LLM-Mediated Speculative Gameworld

We present an LLM-mediated role-playing game that supports reflection on socialization, moral responsibility, and educational role positioning. Grounded in socialization theory, the game follows a four-season structure in which players guide a child prince through morally charged situations and compare the LLM-mediated NPC's differentiated responses across stages, helping them reason about how educational guidance shifts with socialization. To approximate real educational contexts and reduce score-chasing, the system hides real-time evaluative scores and provides delayed, end-of-stage growth feedback as reflective prompts. We conducted a user study (N=12) with gameplay logs and post-game interviews, analyzed via reflexive thematic analysis. Findings show how players negotiated responsibility and role positioning, and reveal an entry-load tension between open-ended expression and sustained engagement. We contribute design knowledge on translating sociological models of socialization into reflective AI-mediated game systems.

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arXiv2025

CGM-Led Multimodal Tracking with Chatbot Support: An Autoethnography in Sub-Health

Metabolic disorders present a pressing global health challenge, with China carrying the world's largest burden. While continuous glucose monitoring (CGM) has transformed diabetes care, its potential for supporting sub-health populations -- such as individuals who are overweight, prediabetic, or anxious -- remains underexplored. At the same time, large language models (LLMs) are increasingly used in health coaching, yet CGM is rarely incorporated as a first-class signal. To address this gap, we conducted a six-week autoethnography, combining CGM with multimodal indicators captured via common digital devices and a chatbot that offered personalized reflections and explanations of glucose fluctuations. Our findings show how CGM-led, data-first multimodal tracking, coupled with conversational support, shaped everyday practices of diet, activity, stress, and wellbeing. This work contributes to HCI by extending CGM research beyond clinical diabetes and demonstrating how LLM-driven agents can support preventive health and reflection in at-risk populations.

cs.HC↗