arXiv subjects

Jiahe Dong

Publications and source records attributed to Jiahe Dong.

4 recordsLinked to original sources

arXiv2026

From Scaffolding to Internalization: Enhancing CPR Training with In-Situ Visualization and Kinesthetic Feedback

CPR training requires learners to not only understand explicit procedural targets, such as compression depth and rate, but also to internalize these targets as stable psychomotor skills. However, existing CPR training systems often rely on feedback presented outside the action space, which divides learners' attention between performing compressions and monitoring external guidance. This separation weakens the coupling between action and bodily sensation and may lead to an over-reliance on external feedback, compromising skill retention once support is removed. To address this challenge, we conducted a formative study with novice trainees and certified BLS instructors, from which we derived three design goals: embedding feedback within the task space, providing active kinesthetic guidance, and gradually fading assistance based on learning phases. Informed by these insights, we designed Kinesthetic-CPR, a stage-adaptive multimodal mixed reality CPR training system, and evaluated it in a controlled user study across two sub-studies (N = 60). This work offers design implications for CPR training systems that aim to better support skill retention.

cs.HC↗

arXiv2026

When Seconds Count: Designing Real-Time VR Interventions for Stress Inoculation Training in Novice Physicians

Surgical emergencies often trigger acute cognitive overload in novice physicians, impairing their decision-making under pressure. Although Virtual Reality-based Stress Inoculation Training (VR-SIT) shows promise, current systems fall short in delivering real-time, effective support during moments of peak stress. To bridge this gap, we first conducted a formative study (N=12) to uncover the core needs of novice physicians for immediate assistance under acute stress and identified three key intervention strategies: self-regulation aids, procedure guidance, and emotional/sensory support. Building on these insights, we designed and implemented a novel VR-SIT system that incorporates a just-in-time adaptive intervention framework, dynamically tailoring support to learners' cognitive and emotional states. We then validated these strategies in a user study (N=26). Our findings provide empirical evidence and design implications for next-generation VR medical training systems, supporting physicians in sustaining cognitive clarity and accurate decision-making in critical situations.

cs.HC↗

arXiv2025

Advancing Problem-Based Learning with Clinical Reasoning for Improved Differential Diagnosis in Medical Education

Medical education increasingly emphasizes students' ability to apply knowledge in real-world clinical settings, focusing on evidence-based clinical reasoning and differential diagnoses. Problem-based learning (PBL) addresses traditional teaching limitations by embedding learning into meaningful contexts and promoting active participation. However, current PBL practices are often confined to medical instructional settings, limiting students' ability to self-direct and refine their approaches based on targeted improvements. Additionally, the unstructured nature of information organization during analysis poses challenges for record-keeping and subsequent review. Existing research enhances PBL realism and immersion but overlooks the construction of logic chains and evidence-based reasoning. To address these gaps, we designed e-MedLearn, a learner-centered PBL system that supports more efficient application and practice of evidence-based clinical reasoning. Through controlled study (N=19) and testing interviews (N=13), we gathered data to assess the system's impact. The findings demonstrate that e-MedLearn improves PBL experiences and provides valuable insights for advancing clinical reasoning-based learning.

cs.HC↗

arXiv2024

Medillustrator: Improving Retrospective Learning in Physicians' Continuous Medical Education via Multimodal Diagnostic Data Alignment and Representation

Continuous Medical Education (CME) plays a vital role in physicians' ongoing professional development. Beyond immediate diagnoses, physicians utilize multimodal diagnostic data for retrospective learning, engaging in self-directed analysis and collaborative discussions with peers. However, learning from such data effectively poses challenges for novice physicians, including screening and identifying valuable research cases, achieving fine-grained alignment and representation of multimodal data at the semantic level, and conducting comprehensive contextual analysis aided by reference data. To tackle these challenges, we introduce Medillustrator, a visual analytics system crafted to facilitate novice physicians' retrospective learning. Our structured approach enables novice physicians to explore and review research cases at an overview level and analyze specific cases with consistent alignment of multimodal and reference data. Furthermore, physicians can record and review analyzed results to facilitate further retrospection. The efficacy of Medillustrator in enhancing physicians' retrospective learning processes is demonstrated through a comprehensive case study and a controlled in-lab between-subject user study.

cs.HC↗