Development and Validation of an AI-based model to predict the assessment outcomes of pre-clinical MBBS/BDS students

Accurately predicting student performance is crucial in medical education, especially in the critical pre-clinical years when foundations are laid. This study employed artificial intelligence (AI) to develop a predictive model for assessment outcomes of 4th-year MBBS/BDS students, aiming to provide educators with a tool for proactive intervention. A quantitative, cross-sectional study design was employed, involving 144 students from two institutions in Rawalpindi, Pakistan. A comprehensive dataset of academic and demographic variables was analyzed using various machine learning algorithms, including Random Forest, AdaBoost, Logistic Regression, SVM, and XGBoost. The Random Forest model emerged as the most effective machine learning model while year 2 exam scores and weekly study hours as key predictors of student success. This model allows educators to shift from traditional reactive approaches to a proactive, data-driven approach to student support by providing a framework for AI driven student support system. By identifying at-risk students early, personalized interventions can be implemented, potentially improving overall success rates and nurturing a more supportive learning environment. This study highlights the potential of AI to revolutionize medical education by enabling personalized learning pathways, optimizing resource allocation, and enhancing teaching effectiveness. However, the ethical considerations of AI in education are also addressed to ensure responsible implementation that maximizes student success and creates a more inclusive learning environment.