对医学院大语言模型表现的评估 论文风格概念评估问题:探索性研究研究
Seysha Mehta1, Eliot N Haddad1, Indira Bhavsar Burke2
1Cleveland Clinic Lerner College of Medicine, School of Medicine, Case Western Reserve University, 9500 Euclid Ave, G10, Cleveland, OH, 44195, United States, 1 2164456512, 1 2164451007.
JMIR medical education
|June 16, 2025
概括
微软的Copilot,一个ChatGPT 4.0大型语言模型,在论文问题上与医学学生相似. 评估人员发现很难区分人工智能生成的文本和人类的响应,强调需要在教育中做好人工智能准备.
科学领域:
- 医学教育 医学教育
- 人工智能的人工智能
背景情况:
- 人工智能 (AI) 融入学术环境带来了新的挑战和机遇.
- 像ChatGPT 4.0这样的大型语言模型 (LLM) 越来越能够生成类似人类的文本.
研究的目的:
- 评估微软Copilot (基于ChatGPT 4.0) 与医学学生在论文式概念评估中的性能.
- 评估人类评估人员区分人工智能生成的答案和学生写的答案的能力.
主要方法:
- 一个散文式的概念评估任务被管理.
- 答案由人类评估者评估,他们不知道文本的来源 (AI与人类).
主要成果:
- 微软Copilot的表现与医学学生的表现相当.
- 评估人员在区分人工智能生成的答案和人类撰写的答案方面遇到了重大困难.
结论:
- 像微软Copilot这样的AI工具在学术任务中展示了先进的功能,与人类的表现相美.
- 教育机构必须通过强调学生和教育工作者批判性思维和反思性学习来适应人工智能的兴起.
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