Twelve tips for using educational data mining and machine learning to predict performance in high-stakes health professions education exams.

Integrating artificial intelligence (AI) and educational data mining in health professions education is poised to revolutionize high-stakes examination performance prediction. This study provides a practical guide with 12 essential tips to enhance predictive accuracy, offering actionable insights for educators, stakeholders, and policymakers. Key focus areas include assembling interdisciplinary teams, effective data collection and preprocessing, selecting appropriate AI models, ensuring methodological rigor, and implementing robust validation practices. The guide also addresses data accessibility, ethical considerations, and model interpretability. By leveraging AI-driven techniques like neural networks, decision trees, and ensemble learning, educators can make data-informed decisions to improve educational outcomes. Grounded in real-world examples, this resource equips health professions educators, data scientists, and policymakers with the strategies to develop ethically sound and effective models, ultimately contributing to student success and improving healthcare quality.