改变医学教育:利用大型语言模型来增强PBL - 一项概念验证研究
Shoukat Ali Arain1, Shahid Akhtar Akhund2, Muhammad Abrar Barakzai1
1Department of Pathology, College of Medicine, Alfaisal University, Riyadh, Saudi Arabia.
Advances in physiology education
|February 7, 2025
概括
这项研究表明,Gemini Advanced是一个大型语言模型,可以通过更好地将临床细节与学习目标协调并增强导师指南来改进基于问题的学习 (PBL) 材料. 虽然人工智能看起来很有前途,但人类的专业知识仍然对教育的适当性和视觉内容至关重要.
科学领域:
- 医疗教育 技术 技术 医学教育
- 医疗保健中的人工智能
- 课程开发 课程开发
背景情况:
- 将学习材料与学习目标 (LO) 结合在一起,对于有效的基于问题的学习 (PBL) 课程至关重要.
- 大型语言模型 (LLM) 为开发教育内容提供了潜力,但它们在PBL材料创作中的有效性需要研究.
研究的目的:
- 评估Gemini Advanced在生成与PBL的LO一致的临床图片和导师指南方面的能力.
- 为了比较LLM生成的材料与教师创建的版本对齐,教育适当性和PBL设计.
主要方法:
- 使用了由教师编写的糖尿病临床图片和糖尿病导师指南.
- 双子座高级基于提供的LOs生成了维尼特和导师指南的新版本.
- 四名教职员工使用结构化问卷评估了原始和LLM生成的材料.
主要成果:
- 由LLM生成的图标显示与LOs (3.0对1.25) 的对齐明显更高.
- 由LLM生成的导师指南在流动性,内容全面性和问题质量方面获得了更高的分数.
- 最初的标签在教育水平的适当性和PBL设计的坚持方面获得了更高的评分.
结论:
- 双子座高级可以有效地调整和增强PBL学习材料,特别是在改进维尼特-LO调整和导师指南结构方面.
- LLM显示出创造创新的医学教育内容的潜力,但视觉元素和教育水平适当性的局限性需要人类监督.
- 将LLM与人类专业知识相结合,可以优化医学学生有效和有吸引力的PBL资源的开发.
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