构建知识:人工智能在医学学习中的作用
1Department of Neurosurgery, Jena University Hospital-Friedrich Schiller University Jena, 07747 Jena, Germany.
概括
大型语言模型 (LLM) 为医学教育提供了潜在的好处,但它们对学习的影响和道德考虑需要进一步研究. 仔细实施对于公平的准入和培养合格专业人员至关重要.
科学领域:
- 医疗教育 技术 技术 医学教育
- 人工智能在医疗保健教育中的应用
- 医学中的数字学习
背景情况:
- 大型语言模型 (LLM) 在医学教育中提出了新的机遇和挑战.
- 与建设主义学习理论的结合表明了提高批判性思维和解决问题的能力的潜力.
- 人工智能的快速发展需要对其在医疗培训中的作用进行审查.
研究的目的:
- 探索将LLMs纳入医学教育的潜在好处和挑战.
- 评估LLMs对教育成果和学习有效性的影响.
- 为了应对人工智能集成,需要修订课程和评估方法.
主要方法:
- 关于医学教育中的LLMs和建设主义学习理论的当前文献的综述.
- 分析LLMs对基于调查的学习的潜在应用.
- 识别与教育中的AI相关的伦理考虑和潜在偏见.
主要成果:
- 通过基于调查的学习,LLM显示出提高批判性思维和解决问题的潜力.
- 需要实证研究来验证LLMs对教育成果的有效性.
- 必须解决重要的伦理问题,包括人工智能偏见和公平访问.
结论:
- 在医学教育LLMs的整合需要仔细考虑教学对齐和经验验证的验证.
- 重新设计课程和新的评估策略对于有效利用人工智能工具至关重要.
- 解决人工智能偏见和确保公平获取对于培训医疗专业人员的负责任部署至关重要.
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