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在医学教育评估中利用生成式人工智能生成高质量多项选择题的十条建议。

Ten tips to harnessing generative AI for high-quality MCQS in medical education assessment.

作者信息

Magzoub Mohi Eldin, Zafar Imran, Munshi Fadi, Shersad Fouzia

机构信息

Department of Medical Education, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain, United Arab Emirates.

National Institute for Health Sciences, United Arab Emirates University, Al Ain, United Arab Emirates.

出版信息

Med Educ Online. 2025 Dec;30(1):2532682. doi: 10.1080/10872981.2025.2532682. Epub 2025 Jul 17.

Abstract

Generating high quality MCQs is time consuming and expensive. Many strategies are applied to produce high quality items including sharing of item banks, training of item writers and automatic item generation (AIG). Generative AI, when used with precision, has proven to reduce significantly both cost and time without compromising quality. Medical educators encounter numerous obstacles when using AI to generate MCQs of good quality. We searched the fast and recent growing medical education literature for articles related to the use of AI in generating high quality MCQs. Additionally, the development of these tips was guided by our own institutional experience. We created 10 tips for MCQ generation using AI to assist MCQ item writers in both undergraduate and graduate medical education.

摘要

生成高质量的多项选择题既耗时又昂贵。人们应用了许多策略来制作高质量的题目,包括共享题库、培训题目编写人员以及自动生成题目(AIG)。生成式人工智能若使用得当,已被证明能在不影响质量的情况下显著降低成本和时间。医学教育工作者在使用人工智能生成高质量的多项选择题时会遇到诸多障碍。我们在快速发展且最新的医学教育文献中搜索了与使用人工智能生成高质量多项选择题相关的文章。此外,这些建议的制定还借鉴了我们自己机构的经验。我们创建了10条使用人工智能生成多项选择题的建议,以帮助本科和研究生医学教育中的多项选择题编写人员。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1330/12273594/dbf884259606/ZMEO_A_2532682_F0001_OC.jpg

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