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AI in radiography education: Evaluating multiple-choice questions difficulty and discrimination
Emre Emekli1, Betül Nalan Karahan1
1Department of Radiology, Eskişehir Osmangazi University, Faculty of Medicine, Eskişehir, Türkiye.
Journal of Medical Imaging and Radiation Sciences
|March 29, 2025
Summary
Artificial intelligence (AI) can generate multiple-choice questions (MCQs) comparable to those made by human experts for radiography education. While AI-generated MCQs show promise, slight improvements in discrimination and difficulty alignment are needed for full integration.
Area of Science:
- Medical Education
- Artificial Intelligence in Education
Background:
- High-quality multiple-choice questions (MCQs) are crucial for health education assessment.
- Manual MCQ creation is time-consuming and requires expertise.
- Large language models (LLMs) offer potential for automating MCQ generation.
Purpose of the Study:
- To compare the effectiveness of AI-generated versus faculty-authored MCQs in radiography education.
- To evaluate the quality of AI-generated MCQs based on difficulty and discrimination indices.
- To address the gap in understanding AI's role in health education assessment.
Main Methods:
- A quantitative study involving 56 radiography students in Turkey.
- Two 30-question MCQ exams were created: one by ChatGPT-4o, one by a faculty member.
- Exams covered radiographic anatomy and positioning; difficulty levels were categorized. Student performance and feedback were analyzed.
Main Results:
- Mean scores for AI-generated and human-expert exams were similar (p=0.089).
- A moderate positive correlation (r=0.628) was found between exam scores.
- AI questions had a slightly lower discrimination index (73.33%) compared to human experts (86.67%).
Conclusions:
- LLMs can produce MCQs of comparable quality to those created by human experts.
- AI-generated MCQs show potential for supplementing assessment in health education.
- Further refinement is needed for optimal difficulty and discrimination alignment.
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