医学学习者对人工智能的道德和适当使用的关键见解
Brian Patrick Murray1, Darshan S Thota2, Carrie Baker1
1Wright State University Boonshoft School of Medicine, Dayton, OH 45324, USA.
Military medicine
|November 28, 2024
概括
大型语言模型 (LLM) 可以帮助医学学习者写作,但过度使用可能会阻碍技能发展. 教师必须指导负责任的人工智能使用,以确保批判性思维和分析技能的进步.
科学领域:
- 医学教育 医学教育
- 在学术界的人工智能.
- 学术写作 学术写作
背景情况:
- 大型语言模型 (LLM) 正在快速发展,并在各个学术领域被采用.
- 医学学习者在学术工作中使用LLMs需要仔细检查.
- 在学术写作中LLM的潜在好处可能会被研究人员学习过程的损害所抵消.
研究的目的:
- 辨别医学学习者使用大语言模型 (LLM) 的影响.
- 评估法学士对研究,构思和写作技能的发展的影响.
- 为医疗教育中负责任的生成人工智能使用提供框架.
主要方法:
- 对传统和灰色文学进行了环境扫描.
- 经过同行评审的期刊,开源网站和之前的出版物 (2015-2023年).
- 专注于医学教育中生成人工智能的最佳实践.
主要成果:
- 检测人工智能参与的建议策略包括直接调查,评估学习者能力的内容连贯性,识别洞察深度模式和使用检测工具.
- 突出显示伪造的引用作为LLMs的已知陷.
- 为教育工作者确定监控人工智能使用的方法.
结论:
- 未经控制的LLM使用可能会损害医学学习者的批判性思维和分析技能发展.
- 导师和研究人员负责指导学习者适当使用技术.
- 这项研究为负责任的生成人工智能使用和教育工作者最佳实践提供了基础框架.
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