International Journal of Scientific Research and Engineering Development

In many educational institutions, students don’t fail overnight—they gradually fall behind without anyone noticing early enough. By the time intervention happens, it is often too late to make a meaningful difference. This paper presents an AI-Based Academic Risk Prediction & Intervention System that combines modern web technologies with machine learning to continuously monitor student performance. Instead of relying only on final exam results, the system considers everyday academic factors such as attendance, internal marks, assignment completion, and behavioral patterns. Using this data, it predicts the likelihood of a student facing academic difficulties and assigns a risk level—Low, Medium, or High. The Random Forest classifier achieved an F1 score of 0.87 overall and 0.83 on the High Risk class, substantially outperforming single-indicator threshold systems. Role-based dashboards for students, faculty, advisors, and administrators ensure that the right people receive the right information at the right time, enabling timely and effective academic support.

K.Vijayabalan, Dr.T.Amalraj Victoire,"AI-Based Academic Risk Prediction & Intervention System" International Journal of Scientific Research and Engineering Development, V9(3): Page(1250-1255) May-June 2026. ISSN: 2581-7175. www.ijsred.com. Published by Scientific and Academic Research Publishing.