大型语言模型可以促进反思性实践吗? 一个受训教育者迪亚德的对话式信息探索
Stewart Retnam1, Mucheli Sharavan Sadasiv1
1Department of Infectious Diseases, Tan Tock Seng Hospital, 11 Jalan Tan Tock Seng, Singapore, 308433 Singapore.
Medical science educator
|March 9, 2026
概括
大型语言模型 (LLM) 显示出在反思性实践中帮助医疗学员的潜力. 建议采用混合方法,将批判性LLM使用与人类指导相结合,以进行有效的反思.
科学领域:
- 医疗教育 医学教育
- 医疗保健中的人工智能
背景情况:
- 反射练习对于医学培训至关重要,但很难实施.
- 实习生和教育工作者的观点对于理解挑战至关重要.
研究的目的:
- 探索大型语言模型 (LLM) 在支持医学学员反思实践方面的潜力.
- 确定LLMs的负担能力和局限性,以反映在医学教育中.
主要方法:
- 评论由月亮的学习阶段和自决理论的信息.
- 对LLM有效性和伦理学的经验数据的审查.
- 从实习生和教育者-导师的角度进行分析.
主要成果:
- LLM提供了诸如反思的可访问性之类的费用.
- 限制包括隐私风险和表面接触的可能性.
- 建议采用一种混合模式,将LLM与人类指导相结合.
结论:
- 在医学反射实践中,LLM可以补充,而不是取代人类指导.
- 临界的LLM素养和强大的导师是关键.
- 需要评估LLM影响的策略.
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