医学学生对人工智能产生的实践问题作为学习工具的看法
Hudson Tate1, Brenton T Bicknell1, Paul Fiore1
1Marnix E. Heersink School of Medicine, University of Alabama at Birmingham, AL, USA.
概括
生成型人工智能 (AI) 工具可以增强医学教育. 人工智能生成的实践问题得到了医学学生的好评,高年级的学生表现更好,这表明人工智能.
科学领域:
- 医学教育 医学教育
- 教育中的人工智能
- 健康 职业 教育 教育 专业
背景情况:
- 生成型人工智能 (AI) 工具,包括大型语言模型 (LLM),显示出增强医学教育中学生学习的希望.
- 形成性评估对于医学学生的发展至关重要,人工智能为创建实践材料提供了新的方法.
- 美国医学执照考试 (USMLE) 风格的实践问题是评估医学知识的标准工具.
研究的目的:
- 为了评估人工智能生成的感知实用性,USMLE风格的实践问题作为临床前医学学生的形成性学习工具.
- 用人工智能生成的多选择题 (MCQ) 来评估医学学生的绩效结果.
- 根据学生的一年和反来探索绩效和感知的差异.
主要方法:
- 聊天GPT 4.0被用来生成15个微生物学和内分泌学讲座的MCQ.
- 共有386名临床前医学专业的学生 (198名第一年 (MS1) 和188名第二年 (MS2)) 参加.
- 490个人工智能生成的问卷被选择性地完成,学生反收集了94.9%的问卷.
主要成果:
- 大多数学生认为人工智能生成的问题有帮助 (82.8%) 或有点有帮助 (16.1%).
- 与一年级学生 (MS1) (78.6%) 相比,医学二年级学生 (MS2) 的正确答案率 (84.4%) 显着更高.
- 根据学生的反或对问题的评分,没有发现平均得分的显著差异.
结论:
- 人工智能生成的MCQ被医学学生广泛接受,作为有价值的补充性形成性学习工具.
- 在AI产生的问题上的表现在学生队伍和讲座主题之间有所不同,这表明有针对性的学习的潜在领域.
- 需要进一步的研究来探索人工智能工具在医学教育中的更广泛可行性和影响.
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