健康科学教育中人工智能生成的多项选择题：利益相关者的观点及实施考量
AI-generated multiple-choice questions in health science education: Stakeholder perspectives and implementation considerations.
作者信息
Reid Matthew, French Michelle, Andreopoulos Stavroula, Wong Christine, Kee Nohjin
机构信息
University of Toronto, School of Continuing Studies, 158 St George St, Toronto, ON M5S 2V8, Canada.
University of Toronto, Department of Physiology, Temerty Faculty of Medicine, Medical Sciences Building 3rd Floor, 1 King's College Circle, Toronto, ON, M5S 1A8, Canada.
出版信息
Curr Res Physiol. 2025 Aug 1;8:100160. doi: 10.1016/j.crphys.2025.100160. eCollection 2025.
Multiple-choice questions (MCQs) are widely used in health science education because they are an efficient way to evaluate knowledge from simple recall to complex clinical reasoning. The creation of high-quality MCQs, however, can be time-consuming and requires expertise in question composition. Advancements in artificial intelligence (AI), especially large language models (LLMs), offer the potential to allow for the rapid generation of high-quality, consistent, and course-specific MCQs. Here we discuss the potential benefits and drawbacks of the use of this technology in the generation of MCQs, including ensuring the accuracy and fairness of questions, along with technical, ethical, and privacy considerations. We offer practical guiding principles for the implementation of AI-generated MCQs and outline future research areas related to their impact on student learning and educational quality.
多项选择题（MCQs）在健康科学教育中被广泛使用，因为它们是一种评估知识的有效方式，涵盖从简单回忆到复杂临床推理的各个方面。然而，高质量多项选择题的编写可能耗时且需要问题编写方面的专业知识。人工智能（AI）的进步，尤其是大语言模型（LLMs），为快速生成高质量、一致且针对特定课程的多项选择题提供了可能性。在此，我们讨论在多项选择题生成中使用这项技术的潜在益处和弊端，包括确保问题的准确性和公平性，以及技术、伦理和隐私方面的考量。我们为实施人工智能生成的多项选择题提供实用的指导原则，并概述与其对学生学习和教育质量的影响相关的未来研究领域。
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