AI-Based Early Prediction and Intervention for Student Academic Performance in Higher Education

Accurately identifying at-risk students in higher education is crucial for timely interventions. This study presents an AI-based solution for predicting student performance using machine learning classifiers. A dataset of 208 student records from the past two years was preprocessed, and key predictors such as midterm grades, previous semester GPA, and cumulative GPA were selected using information gain evaluation. Multiple classifiers, including Support Vector Machine (SVM), Decision Tree, Naive Bayes, Artificial Neural Networks (ANN), and k-Nearest Neighbors (k-NN), were evaluated through 10-fold crossvalidation. SVM demonstrated the highest performance with an accuracy of 85.1% and an F2 score of 94.0%, effectively identifying students scoring below 65% (GPA < 2.0). The model was implemented in a desktop application for educators, providing both class-level and individual-level predictions. This userfriendly tool enables instructors to monitor performance, predict outcomes, and implement timely interventions to support struggling students. The study highlights the effectiveness of machine learning in enhancing academic performance monitoring and offers a scalable approach for AI-driven educational tools.