<b>Fairness-Aware Machine Learning for Ethical Student Performance Prediction in Education</b><b></b>

The integration of artificial intelligence (AI) and machine learning (ML) into education has transformed how student performance is predicted and monitored. Despite these advances, concerns regarding fairness, transparency, interpretability, and potential demographic bias remain significant challenges in educational prediction systems. This study developed ethically aligned and interpretable ML models for predicting student academic performance using only behavioural engagement and academic context variables. The open-access xAPI-Edu-Data dataset containing 480 student records was obtained from Kaggle. Four supervised algorithms, Multinomial Logistic Regression, Decision Tree, Random Forest, and XGBoost, were implemented in Python 3.11 using Scikit-learn, SHAP, and LIME frameworks. To minimise data leakage, all preprocessing operations, including standardisation and categorical encoding, were embedded within a Scikit-learn Pipeline fitted exclusively on the training folds. A stratified train/validation/test split (70%/15%/15%) with random_state = 42 was employed, while hyperparameters were optimised using five-fold cross-validation on the training and validation sets only. Model performance was evaluated using accuracy, macro F1-score, ROC-AUC, Brier Score, and Expected Calibration Error (ECE). Results showed that Random Forest achieved the best overall performance and calibration, while Logistic Regression provided superior interpretability. Across all models, visited_resources and raisedhands consistently emerged as the strongest predictors of academic achievement, emphasising the importance of student engagement behaviours. The study demonstrates that fairness-by-design prediction systems that exclude demographic variables can support equitable and actionable early-warning interventions. The novelty of the study lies in its ethically grounded and deployment-oriented framework for interpretable educational prediction in resource-constrained contexts.