基于人工智能的物理治疗教育方法在发展临床推理技能的影响:一项随机对照试验
Gizem Ergezen Sahin1, Gulay Aras Bayram2, Alberto Sanchez Sierra3,4
1Faculty of Health Sciences, Physiotherapy and Rehabilitation, Istanbul Medipol University, Beykoz, Istanbul, 34810, Türkiye. gergezen@medipol.edu.tr.
BMC medical education
|October 10, 2025
概括
与传统方法相比,人工智能辅助基于问题的学习 (AI-PBL) 显著改善了物理治疗学生的知识保留和人工智能自我有效性. 这种结构化的人工智能集成增强了学习,而不会增加互联网成的风险.
科学领域:
- 健康 职业 教育 教育 专业
- 数字学习技术数字学习技术
- 物理治疗培训 物理治疗培训
背景情况:
- 有限的证据存在于人工智能 (AI) 工具,如物理治疗教育中的ChatGPT.
- 这项研究解决了评估人工智能对物理治疗学生学习和数字技能的影响的需要.
研究的目的:
- 将人工智能辅助基于问题的学习 (AI-PBL) 与传统PBL的有效性进行比较.
- 评估对理论知识,临床能力,AI自我效能,互联网成和阅读动机的影响.
主要方法:
- 一个随机对照试验,涉及本科生理疗法学生.
- 参与者被分配到AI-PBL或传统的PBL组.
- 结果措施包括知识测试,Mini-CEX,AI自我有效性,互联网成和阅读动机尺度,评估干预前,干预后立即和两周后.
主要成果:
- 两组AI-PBL和PBL都显示出更好的知识和阅读动机.
- 人工智能-PBL组在两周内显示出明显更好的知识保留 (科恩d=3.14) 和人工智能自我有效性更大的收益.
- 在AI-PBL组中,临床能力得分 (Mini-CEX) 较高,但在统计学上并不显著;没有观察到互联网成的增加.
结论:
- 在物理治疗教育中对生成人工智能的监督,结构化的使用可以增强持续的学习和数字自我有效性.
- 人工智能-PBL方法促进积极反思,自主学习和学术参与.
- AI-PBL为物理治疗教育的数字创新提供了一个有前途的途径,需要对长期结果和人工智能增强策略进行进一步调查.
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