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通过LLM加强医学教育:整合生成GPT进行评估和反

Suraj K Sebastian¹, Rekha Chakravarthi², Jayadevan Sreedharan^3,4

¹General Education Department, Gulf Medical University, Ajman, United Arab Emirates.

Medical science educator

|March 27, 2025

概括

一个定制的人工智能模型有效地对物理治疗学生的解剖学作业进行了分级,减少了教育工作者的工作量并保持了高质量. 这证明了AI.

科学领域:

医学教育医学教育
医疗保健中的人工智能
解剖学教育解剖学教育

背景情况:

物理治疗学生的解剖学教育需要一致和高质量的分级.
在大型队伍中,教育工作者的工作负担可能是一个挑战.
人工智能为教育评估提供了潜在的解决方案.

研究的目的:

开发和评估一个定制的GPT-3.5模型来对解剖学作业进行分级.
为了比较AI评分表现与主题专家.
评估人工智能分级对工作量,一致性和质量的影响.

主要方法:

开发一个定制的GPT-3.5模型,适用于解剖学分配评估.
对人工智能生成的成绩与人类专家评分的比较分析.
评价分级效率,一致性和质量指标的评估.

主要成果:

人工智能模型的一致性和质量与人类专家的评分相提并论.
人工智能分级显著减少了与分配评估相关的工作量.
这项研究强调了人工智能在解剖评估中的可行性.

结论:

定制的人工智能模型可以高效准确地对解剖学作业进行分级.
将人工智能整合到医学教育中可以增强评估过程.
这项技术显示了个性化和高效的学习体验的前景.

关键词:

评估评估的方法聊天GPT 聊天在GPT 聊天有关反的意见反.法学士学位 (LLM) 的课程.医学教育医学教育

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