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

2026-01-27

AI Summary

This study addresses the challenges of uneven student engagement and insufficient guidance in oral case presentation (OCP) training, which hinder the development of clinical communication skills among medical students. To overcome these limitations, the authors designed and evaluated CaseMaster, an interactive probing tool powered by a large language model (LLM) that is deeply integrated into the OCP training workflow for the first time. CaseMaster generates structured, educationally appropriate clinical cases and provides personalized support through a user-centered interface. A controlled experiment demonstrated that CaseMaster significantly improves the quality of students’ OCPs while reducing their preparation burden. The tool received positive evaluations from medical education experts and offers a generalizable LLM-augmented instructional framework along with practical implementation guidelines.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval

Application Category

• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

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.

Problem

• oral case presentation
• medical education
• LLM assistance
• clinical communication
• student training

Innovation

• Large Language Models
• Oral Case Presentation
• Medical Education
• Interactive Probe
• AI-assisted Learning