CaseMaster: Designing and Evaluating a Probe for Oral Case Presentation Training with LLM Assistance

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Authors: Yang Ouyang, Yuansong Xu, Changhua Jiang, Yifan Jin, Haoran Jiang, Quan Li
Cited by: 1
References: 95

Abstract

Preparing an oral case presentation (OCP) is a crucial skill for medical students, requiring clear communication of patient information, clinical findings, and treatment plans. However, inconsistent student participation and limited guidance can make this task challenging. While Large Language Models (LLMs) can provide structured content to streamline the process, their role in facilitating skill development and supporting medical education integration remains underexplored. To address this, we conducted a formative study with six medical educators and developed CaseMaster, an interactive probe that leverages LLM-generated content tailored to medical education to help users enhance their OCP skills. The controlled study suggests CaseMaster has the potential to both improve presentation quality and reduce workload compared to traditional methods, an implication reinforced by expert feedback. We propose guidelines for educators to develop adaptive, user-centered training methods using LLMs, while considering the implications of integrating advanced technologies into medical education.

Fields of Study & Subjects

Artificial Intelligence in Healthcare and Education Simulation-Based Education in Healthcare Innovations in Medical Education

Publication details

Journal / source
Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems
Publisher
ACM
Pages
1–30
Publication date
13 April 2026