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Using Pretrained Large Language Models for AI-Driven Assessment in Medical Education.

Jacob Cole, Joshua Duncan, Rebekah Cole

    Academic Medicine : Journal of the Association of American Medical Colleges
    |August 27, 2025
    PubMed
    Summary
    This summary is machine-generated.

    Large language models (LLMs) show promise in assessing medical students' understanding of moral injury, offering more objective and efficient evaluations. This AI-powered approach can improve feedback and educational outcomes in medical ethics training.

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    Area of Science:

    • Medical Education
    • Artificial Intelligence in Healthcare
    • Ethics in Medicine

    Background:

    • Assessing medical students in competency-based programs is challenging due to time constraints and the need for objective feedback.
    • Traditional assessment methods may lack consistency and targeted feedback, especially in complex subjects like moral injury.

    Purpose of the Study:

    • To pilot the use of large language models (LLMs) with retrieval-augmented generation for assessing student understanding of moral injury.
    • To evaluate the efficiency, objectivity, and effectiveness of LLM-based assessment in a medical ethics course.

    Main Methods:

    • LLMs (Google Gemini 1.5 Pro, Google Gemini 2.0 Flash, OpenAI ChatGPT-4o) were used to generate a grading rubric from seminal articles on moral injury.
    • 165 student responses were assessed by three LLMs using the generated rubric.
    • LLM scoring accuracy was compared against two experienced educators to establish validity evidence.

    Main Results:

    • Google Gemini 1.5 Pro successfully generated a nuanced grading rubric for moral injury.
    • OpenAI ChatGPT-4o achieved the highest interrater reliability (0.77 and 0.68) when compared to human reviewers, exceeding inter-reviewer agreement (0.57).

    Conclusions:

    • LLMs offer a promising tool for enhancing the efficiency and objectivity of medical student assessment, particularly in specialized ethical topics.
    • Faculty oversight is crucial to ensure ethical accountability and mitigate potential biases in AI-driven assessments.