AI-Based Predictive Analytics for Student Academic Performance

Educational institutions generate large volumes of academic and behavioral data through Learning Management

Systems (LMS), attendance portals, and assessment platforms. Leveraging this data using Artificial Intelligence (AI) can

significantly improve academic planning and student outcomes. This research proposes an AI-based predictive analytics

framework to forecast student academic performance using machine learning techniques. The model analyzes historical

academic records, attendance, assignment submissions, and engagement metrics to predict student performance categories

(Excellent, Average, At-Risk). Experimental evaluation demonstrates that ensemble-based models outperform traditional

classifiers in accuracy and reliability. The proposed system can assist educators in early identification of at-risk students and

enable timely academic interventions.