A Hybrid AI Framework for Academic Advising: Integrating Ensemble-Based Grade Prediction and a Rule-Based Expert System

The paper presents a hybrid AI framework for academic advising that combines an ensemble-based grade prediction model with a rule‑based expert system. Students from the University of Birjand were clustered using Gaussian Mixture Models, and a Stacking Ensemble of Random Forest, Gradient Boosting, and MLP was trained per cluster, achieving an aggregated RMSE of 2.35. The expert system then uses these predictions alongside institutional regulations to give real‑time feedback such as GPA forecasts, probation warnings, and course recommendations.

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