aspg-27-4324-1794016644 20261107015724 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group International Journal of Artificial Intelligence and Education Technology IJAIET 2835-2432 2025 4 1 Emad Bashkail Food Industries Polytechnic, Al Kharj, Saudi Arabia Nesrin Merhi Jeddah International School, Jeddah, Saudi Arabia The detection of students who may face academic difficulties or withdraw early gives universities a limited window in which to provide effective support. This study evaluates four machine-learning classifiers on the Open University Learning Analytics Dataset (OULAD), which contains data from 32,593 students across 22 distance-learning courses. The classifiers—logistic regression, decision tree, random forest, and gradient boosting—use demographic and contextual variables together with virtual learning environment (VLE) engagement features. VLE behaviour dominates Random Forest feature importance: total clicks, active VLE days, average daily clicks, and first-assessment score account for 92.8% of total importance. Random Forest achieves strong held-out test performance (AUC = 0.998, F1 = 0.978, accuracy = 98.2%), while the Decision Tree achieves an AUC of 0.959, illustrating the performance–interpretability trade-off. At-risk students generate an average of 49.0 VLE clicks, compared with 203.0 for not-at-risk students (t = 104.0, p < 0.001). The study describes an end-to-end prediction pipeline and discusses its implications for early-alert systems and the ethical deployment of predictive analytics in higher education. 2025 33 41 4324 https://creativecommons.org/licenses/by/4.0/ 10.54216/IJAIET.040105 https://americaspg.com/journal/27/article/4324 https://americaspg.com/storage/articles/manuscripts/01M5X8M0X2X51WWR9P8KAN32Y0.pdf