A Hybrid Explainable AI Model for Accurate and Transparent Prediction of Student Academic Performance

Predicting student academic performance accurately is essential and allows for timely interventions and data-supported educational decision-making. However, the black-box nature of state-of-the-art machine learning models hinders their use in policy-sensitive educational settings, where interpretability and accountability are paramount. We propose a structured hybrid explainable artificial intelligence (XAI) framework that combines three tree-based ensemble models–namely, random forest, XGBoost, and CatBoost–using a prediction-level soft-voting strategy and couples them with complementary post-hoc interpretation methods at the explanation level (SHAP and LIME). Instead of proposing a new base algorithm, this study systematically unifies heterogeneous boosting and bagging approaches with strict validation as well as dual-layer explanation consistency evaluation. Experiments on the UCI Student Performance dataset show that the proposed framework can achieve a competitive predictive performance (91.8% accuracy, ROC–AUC = 0.953) together with transparent and actionable interpretability. The robustness of the interpretability layer is further supported by a quantitative assessment of explanation stability, fidelity, and agreement across various methods. The presented framework harmonizes the trade-off between accuracy and interpretability to provide a deployable, policy-aware decision-support solution that is aligned with responsible AI principles for adoption within educational contexts.