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Large Language Models in Medical Education: Comparing ChatGPT- to Human-Generated Exam Questions.

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    Medical educators created better exam questions than artificial intelligence. Human-written questions had higher discriminatory power than large language model (LLM) questions, despite similar difficulty.

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    Area of Science:

    • Medical Education
    • Artificial Intelligence in Education

    Background:

    • Creating high-quality medical exam questions is labor-intensive.
    • Test-enhanced learning improves student outcomes.
    • Automated question generation using large language models (LLMs) could be beneficial but lacks comparative studies.

    Purpose of the Study:

    • To compare student performance on multiple-choice questions (MCQs) generated by ChatGPT (LLM) versus those created by medical educators.
    • To evaluate the item difficulty and discriminatory power of LLM-generated versus human-generated MCQs.

    Main Methods:

    • Two sets of 25 MCQs each were created: one by a medical educator, the other by ChatGPT 3.5.
    • 161 students completed a formative test with 46 MCQs (25 human, 21 LLM) before their neurophysiology exam.
    • Students indicated their perceived source (human or LLM) for each question.

    Main Results:

    • No significant difference in item difficulty was observed between human and LLM questions.
    • Human-generated questions demonstrated statistically significantly higher discriminatory power (mean=0.36) than LLM-generated questions (mean=0.24).
    • Students correctly identified the source of questions only 57% of the time.

    Conclusions:

    • While LLMs can generate medical exam questions, human-created questions currently exhibit superior discriminatory power.
    • Further research is needed to explore LLM capabilities with different question types and in diverse educational contexts.