大脑与机器人:区分由人类撰写的推信与人工智能相比
Carl Preiksaitis1, Christopher Nash2, Michael Gottlieb3
1Department of Emergency Medicine Stanford School of Medicine Stanford California USA.
AEM education and training
|December 4, 2023
概括
学术医生无法可靠地区分人类和人工智能撰写的推信 (LOR). 生成性AI显示出起草LOR的潜力,但需要指导方针来确保学术晋升的公平性.
科学领域:
- 医学教育 医学教育
- 人工智能在医学中的应用
- 学术进步 学术进步
背景情况:
- 推信 (LOR) 对于医学学术进步的决定至关重要.
- 为学者准备LOR是耗时和劳动密集的.
- 生成型人工智能 (AI) 为学术写作任务提供了潜在的解决方案.
研究的目的:
- 评估使用人工智能的可行性,特别是ChatGPT,为教师晋升起草LOR.
- 确定学术医生是否能够区分人类撰写的和人工智能生成的LOR.
- 为了比较人工智能生成的LOR与人类撰写的LOR的感知质量和说服力.
主要方法:
- 一项准实验性,单盲研究涉及32名学术医生审查LOR的晋升.
- 参与者试图确定提交的LOR的作者 (人类或人工智能).
- 统计分析评估了作者识别的准确性,并比较了感知到的LOR质量.
主要成果:
- 在区分人类和人工智能撰写的LOR中,平均准确率为59.4%,表明可靠性低.
- 被怀疑是人类作者的信件在质量和说服力方面获得了更高的评分.
- 人类撰写的LOR包含了更多与女性相关的单词,而人工智能撰写的LOR使用了更多与男性相关的单词.
结论:
- 学术医生无法可靠地区分人类和人工智能生成的LOR.
- 像ChatGPT这样的AI工具可能会减轻LOR准备的负担.
- 新的政策是必要的,以整合AI在学术促进,同时保持完整性.
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