在医学和牙科教育中以人工智能为基础的基于问题和案例的学习:系统审查和元分析
Hongxia Wei1, Yuguo Dai2, Kaiting Yuan2
1Department of Stomatology, Liuzhou Workers' Hospital, Liuzhou Guangxi, P. R. China; Division of Restorative Dental Sciences, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China.
International dental journal
|June 27, 2025
概括
医学教育中的人工智能 (AI) 显著提升了46%的知识获取,并提高了学生对基于问题的学习 (PBL) 和基于案例的学习 (CBL) 的满意度,而不是传统的方法.
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
- 教育心理学教育心理学
背景情况:
- 传统的基于问题的学习 (PBL) 和基于案例的学习 (CBL) 在医疗和牙科教育中面临挑战.
- 人工智能 (AI) 为增强这些教学方法提供了潜在的解决方案.
- 评估人工智能驱动的PBL/CBL对学生学习成果的影响至关重要.
研究的目的:
- 评估人工智能驱动的PBL/CBL对学生知识获取的有效性.
- 评估人工智能驱动的PBL/CBL对学生临床推理能力的影响.
- 与传统方法相比,确定学生对人工智能驱动的PBL/CBL的满意度.
主要方法:
- 在主要数据库 (PubMed,MEDLINE,Cochrane,Web of Science) 进行系统的电子文献搜索.
- 在PBL/CBL中包括英语出版的随机对照试验 (RCT),对医学/牙科学生的AI进行评估.
- 对知识获取和学生满意度进行的元分析,使用RoB 2进行偏差风险评估.
主要成果:
- 由人工智能驱动的干预措施显著改善了知识获取,46% (P = .001) 低异质性.
- 学生满意度得分与人工智能驱动的PBL/CBL (SM = 0.7,P < .00001) 显著更高,具有中等异质性.
- 对临床推理能力的统计分析是不可行的,因为研究方法的异质性.
结论:
- 由人工智能驱动的PBL/CBL展示了增强医学和牙科学生知识获取的潜力.
- 通过将AI整合到PBL/CBL方法中,学习者满意度显著提高.
- 需要进一步的研究来解决局限性,包括研究数量和偏差风险的担忧.
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