CaseMaster: Designing and Evaluating a Probe for Oral Case Presentation Training with LLM Assistance
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866912852101562368 |
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| author | Ouyang, Yang Xu, Yuansong Jiang, Chang Jin, Yifan Jiang, Haoran Li, Quan |
| author_facet | Ouyang, Yang Xu, Yuansong Jiang, Chang Jin, Yifan Jiang, Haoran Li, Quan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_19332 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | CaseMaster: Designing and Evaluating a Probe for Oral Case Presentation Training with LLM Assistance Ouyang, Yang Xu, Yuansong Jiang, Chang Jin, Yifan Jiang, Haoran Li, Quan Human-Computer Interaction 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. |
| title | CaseMaster: Designing and Evaluating a Probe for Oral Case Presentation Training with LLM Assistance |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2601.19332 |