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AI for Assessment in Medical Education in Post LLM Era: A Scoping Review
Puneet Agarwal1, Renu Agarwal1, Igor Iezhitsa1
1School of Medicine, IMU University, Kuala Lumpur, Malaysia.
Medical Science Educator
|May 25, 2026
Summary
Large language models (LLMs) show promise in medical education assessment, aiding in multiple-choice question generation and feedback analysis. However, AI tools currently serve as assistants, requiring expert oversight for accuracy and educational impact.
Area of Science:
- Medical Education
- Artificial Intelligence
- Educational Assessment
Background:
- Pre-large language model (LLM) AI in medical education assessment was limited, tool-intensive, and required significant expert input.
- The advent of LLMs has revolutionized AI's potential in rapidly generating and evaluating assessment content.
Purpose of the Study:
- To review and map evidence on AI, particularly LLMs, in designing and evaluating medical education assessments post-November 2022.
- To identify dominant application domains and limitations of AI in medical assessment.
Main Methods:
- A systematic scoping review adhering to PRISMA-ScR guidelines.
- Searched PubMed and Scopus for empirical studies from November 2022 to July 2025.
- Included studies on AI-supported assessment design or evaluation for medical learners.
Main Results:
- Twenty-five studies were included, revealing two main AI applications: MCQ generation/evaluation and narrative feedback analysis.
- AI-assisted MCQ creation showed efficiency but had limitations like factual inaccuracies and weak distractors, needing expert review.
- NLP and LLMs demonstrated maturity in analyzing narrative feedback for theme extraction, quality scoring, and risk prediction.
Conclusions:
- AI, especially LLMs, is currently an assistive tool in medical education assessment, not a replacement for human expertise.
- Future research should focus on standardizing human-in-the-loop processes, psychometric validation, multimodal assessments, and multi-institutional trials to ensure educational impact.
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