Detecting and Mitigating Algorithmic Bias in AI-Based Student Performance Prediction Systems: A Fairness-Aware Deep Learning Approach

Abstract

The rapid integration of Artificial Intelligence (AI) and deep learning in higher education has enabled automated student performance prediction systems; however, these systems frequently inherit and amplify biases present in historical educational data, leading to inequitable outcomes for students across gender, socioeconomic, geographic, and linguistic dimensions. Existing machine learning approaches for academic performance prediction predominantly optimize for accuracy, largely disregarding fairness constraints, which risks systematically disadvantaging already marginalized learner groups. This paper proposes FairEduNet, a fairness-aware deep learning framework that integrates adversarial debiasing, fairness-regularized training, and SHAP-based explainability to simultaneously detect and mitigate bias in student performance prediction. The proposed architecture employs a multi-task neural network with a dedicated adversarial classifier that penalizes predictions correlated with protected attributes, supported by demographic parity and equalized odds constraints. Experimental evaluation on two publicly available educational datasets—the Open University Learning Analytics Dataset (OULAD) and the UCI Student Performance Dataset—demonstrates that FairEduNet achieves competitive predictive accuracy (F1: 0.87) while reducing demographic parity difference by 34% and equalized odds gap by 28% compared to state-of-the-art baselines. The framework further provides interpretable, per-group feature attribution via SHAP, enabling educators and policymakers to audit AI systems for fairness. The results establish that fairness and accuracy are not inherently at odds in educational prediction contexts, and that responsible AI design can produce both equitable and performant academic decision-support systems.