由人工智能指导的掌握学习优于灵活支气管镜训练中的指导自我调节学习:一个RCT
Kristoffer Mazanti Cold1,2, Wei Wei1,3, Kaladerhan Agbontaen4
1Copenhagen Academy for Medical Education and Simulation (CAMES), Rigshospitalet, Capital Region of Denmark, Copenhagen, Denmark.
Respiration; international review of thoracic diseases
|October 17, 2024
概括
与自我调节学习相比,掌握学习 (ML) 培训显著加快了新手灵活支气管镜技能. 这种高效的培训方法可以实现更快的手术时间,而不会增加整体培训时间,因此非常适合未来的支气管镜师.
科学领域:
- 医学教育 医学教育
- 手术模拟器手术模拟器
- 呼吸系统医学 呼吸系统医学
背景情况:
- 基于模拟的培训可以提高灵活的支气管镜检查技能.
- 掌握学习 (ML) 标准以前没有对支气管镜训练有效性进行评估.
- 本研究将ML与新手表现的定向自我调节学习 (DSRL) 进行比较.
研究的目的:
- 为了评估掌握学习 (ML) 与指导自我调节学习 (DSRL) 的有效性,对于初学者灵活的支气管镜师.
- 根据已确定的能力标准,评估培训结束时的表现.
- 为了比较培训组之间的程序时间,检查的段落和结构化的进展.
主要方法:
- 新手支气管镜师被随机分为ML和DSRL组,使用人工智能引导的模拟系统.
- ML组接受了针对熟练程度目标的培训 (18段,18个进展,<120秒).
- DSRL组接受训练,直到感知到的好处停止;两组都在没有AI指导的情况下进行了最后的测试.
主要成果:
- 两组检查的细分数量相似 (ML=17.2,DSRL=17.3) 并且具有相似的结构化进展 (ML=15.5,DSRL=14.8).
- 在ML组显示显著更快的程序时间 (107s对180s,p<0.001).
- 训练时间在两组之间是相似的 (ML=114分钟,DSRL=109分钟,p=0.84).
结论:
- 掌握学习 (ML) 是一种有效的培训方法,用于培养彻底,系统和快速的灵活支气管镜技能.
- 与DSRL相比,ML培训不需要更长的培训时间.
- 由于其效率和有效性,建议ML用于培训未来的支气管镜师.
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