Ten tips for utilizing AI to generate high quality OSCE stations in medical education

TL;DR

This paper provides 10 practical tips for using AI to create high-quality OSCE stations in medical education, ensuring they are valid, reliable, and aligned with learning goals.

Contribution

The paper introduces a structured framework of 10 evidence-based tips for safely and effectively using AI in OSCE station development.

Findings

AI can generate realistic and customizable OSCE stations efficiently.

Structured guidance and faculty oversight are essential for ethical and valid AI use in high-stakes exams.

The tips help maintain quality, fairness, and alignment with curricular outcomes.

Abstract

Artificial intelligence (AI) is reshaping medical education, offering novel solutions to long-standing challenges in clinical assessment design. One of the most resource-intensive components of assessment is the development of high-quality Objective Structured Clinical Examination (OSCE) stations that are valid, reliable, and aligned with curricular outcomes. Traditional approaches to OSCE case creation are time-consuming, vulnerable to inconsistency, and difficult to scale. AI, particularly large language models, has emerged as a powerful tool for generating realistic, diverse, and customizable clinical scenarios. However, its safe and effective use in high-stakes examinations requires structured guidance and faculty oversight. This manuscript presents 10 evidence-informed, practical tips for leveraging AI to generate OSCE stations that are pedagogically sound and clinically authentic. These tips draw on focused literature review, experiential insights from AI-assisted case development, and consensus from medical education experts. Together, they provide a framework for integrating AI into assessment workflows while ensuring quality, fairness, security, and ethical compliance. By following these recommendations, educators and examination committees can enhance efficiency, maintain validity, and prepare learners for the complexities of modern clinical practice.

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