Ten tips for utilizing AI to generate high quality OSCE stations in medical education - Zafar, Imran; Aboueisha, Hadeel; Elhassan, Ibrahim; Shersad, Fouzia; Caliskan, Suleyman Ayhan; Syeda, Asma Fatima; Al-Houqani, Mohammed; Magzoub, Mohi Eldin (Frontiers in Medicine, 2026)
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.