使用人工智能为医学教育的学习机会提供"反转评估"方法
Samuel Birks1, James Gray1, Claire Darling-Pomranz1
1School of Medicine and Population Health, University of Sheffield, Sheffield, UK.
Medical teacher
|December 1, 2024
概括
医学学生使用生成性AI (人工智能) 来创建实践考试问题获得了信心. 这项研究探讨了他们的态度,并强调了评估人工智能产生的内容准确性的需要.
科学领域:
- 医学教育 医学教育
- 医疗保健中的人工智能
- 教育技术的教育技术
背景情况:
- 生成型人工智能为高效开发教育材料提供了潜力.
- 评估学生对人工智能工具的信心和态度对于有效的整合至关重要.
研究的目的:
- 评估医学学生对使用生成人工智能来创建实践考试问题的信心.
- 了解学生对在医学教育中使用生成AI的整体态度.
主要方法:
- 采用一种混合方法的方法,采用前后干预设计.
- 68名医生和医生助理学生参加了一场关于使用谷歌Bard (现在是Gemini) 的研讨会.
- 调查和重点小组被用来收集定量和定性数据.
主要成果:
- 观察到,学生对使用人工智能进行问题生成的信心在统计学上显著增加 (p < 0.001).
- 定性反揭示了AI在问题创建中的感知好处和缺点.
- 学生对更广泛的人工智能应用表示兴趣,并寻求机构对其使用的指导方针.
结论:
- 生成型人工智能可以增强学生对开发评估工具的信心.
- 需要进一步的研究来验证人工智能产生的问题的质量和准确性.
- 教导学生如何创建和批判性评估人工智能产生的内容,促进负责任的人工智能采用.
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