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Generative AI vs. human expertise: a comparative analysis of case-based rational pharmacotherapy question generation.
Muhammed Cihan Güvel1, Yavuz Selim Kıyak2, Hacer Doğan Varan3
1Department Medical Pharmacology, Gazi University Faculty of Medicine, Ankara, Turkey.
European Journal of Clinical Pharmacology
|April 9, 2025
Summary
Generative AI models can create pharmacology assessment questions, but expert review is crucial for quality. A hybrid approach combining AI efficiency with expert validation optimizes medical education.
Area of Science:
- Medical Education Technology
- Artificial Intelligence in Healthcare
- Pharmacology Education
Background:
- Developing high-quality assessment questions is time-consuming for educators.
- Generative AI offers potential solutions for accelerating content creation.
Purpose of the Study:
- To evaluate three generative AI models (ChatGPT-4o, Gemini 1.5 Advanced Pro, Claude 3.5 Sonnet) in generating case-based rational pharmacology questions.
- To compare AI-generated questions with those created by expert educators.
Main Methods:
- Sixty case-based pharmacology questions were generated using one-shot prompting across three AI models.
- Questions covered essential hypertension and type 2 diabetes, then assessed for usability by a multidisciplinary panel.
- AI-generated and expert-created questions were administered to 103 medical students to analyze performance metrics.
Main Results:
- No statistically significant differences were observed between AI-generated and expert-created questions in student performance.
- AI questions achieved mean correct response rates over 50% and discrimination indices >= 0.20.
- Claude 3.5 Sonnet had the highest proportion of error-free items (12/20), while ChatGPT-4o had the fewest unusable items (5/20).
Conclusions:
- Large language models can significantly expedite the creation of medical assessment questions.
- Expert review is essential to ensure the reliability and validity of AI-generated questions.
- A hybrid model integrating AI efficiency with expert validation is recommended for optimal medical education outcomes.
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