一个研究路线图的AI机会在医疗教育的学生评估
Morteza Rezaei-Zadeh1, Magdalena Cerbin-Koczorowska2
1University of Leicester, Leicester, UK. mrz5@leicester.ac.uk.
BMC medical education
|November 27, 2025
概括
医疗教育评估中的人工智能 (AI) 提供了许多机会. 本研究绘制了这些AI评估机会的地图,并确定了有效实施的关键驱动因素,并提出了一个研究路线图.
科学领域:
- 医学教育 医学教育
- 人工智能的人工智能
- 教育技术的教育技术
背景情况:
- 人工智能 (AI) 在医学教育中的整合正在改变评估实践.
- 缺乏对AI在评估中的机会和相互依存性的全面理解.
研究的目的:
- 批判性地审查AI在医学教育评估中的作用.
- 将AI评估机会分类并探索它们的相互依赖性.
- 为人工智能驱动的评估策略提出研究路线图.
主要方法:
- 文献审查以确定AI评估机会.
- 将22个机会分为7个"巨大的机会".
- 解释性结构建模 (ISM) 的应用来分析相互依赖关系.
主要成果:
- 在AI中确定了七个主要的"巨大的机会"用于医学教育评估.
- "自动反和评估"和"数据驱动分析和课程改进"是关键的基础驱动因素.
- 揭示了大型机遇之间的复杂相互依存关系,指导了实施.
结论:
- 建议制定研究路线图,优先考虑人工智能和评估机会.
- 为采用人工智能驱动的评估策略提供了实际指南.
- 未来的研究应该侧重于现实世界的应用,纵向影响和教育公平.
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