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AI-generated multiple-choice questions in health science education: Stakeholder perspectives and implementation
Matthew Reid1, Michelle French2, Stavroula Andreopoulos3
1University of Toronto, School of Continuing Studies, 158 St George St, Toronto, ON M5S 2V8, Canada.
Current Research in Physiology
|August 13, 2025
Summary
Artificial intelligence (AI), specifically large language models (LLMs), can rapidly generate high-quality multiple-choice questions (MCQs) for health science education. This discussion explores AI-MCQ benefits, drawbacks, and implementation guidelines.
Area of Science:
- Health professions education
- Artificial intelligence in education
Background:
- Multiple-choice questions (MCQs) are essential for assessing knowledge and clinical reasoning in health sciences.
- Creating high-quality MCQs is resource-intensive, requiring significant time and expertise.
Purpose of the Study:
- To explore the potential of AI, particularly LLMs, for generating MCQs in health science education.
- To discuss the benefits and challenges associated with AI-driven MCQ development.
- To provide practical guidelines for implementing AI-generated MCQs.
Main Methods:
- Discussion of AI and LLM capabilities in educational content generation.
- Analysis of potential benefits, including efficiency and consistency.
- Examination of drawbacks such as accuracy, fairness, ethical, and privacy concerns.
Main Results:
- AI and LLMs show promise for efficient, consistent, and customized MCQ generation.
- Key challenges include ensuring question accuracy, fairness, and addressing technical/ethical considerations.
- Guiding principles for AI-MCQ implementation are proposed.
Conclusions:
- AI offers a transformative potential for MCQ creation in health science education.
- Careful consideration of accuracy, fairness, and ethical implications is crucial for successful AI integration.
- Further research is needed to evaluate the impact of AI-generated MCQs on student learning and educational outcomes.
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