Framework for fair and transparent classroom analytics using explainable AI

Student engagement prediction plays an essential role in improving the learning experience for better educational outcomes. The study investigates the utilization of machine learning models, such as Gradient Boosting Regressor, in predicting students' engagement levels along with Random Forest Regressor. To establish a performance baseline, a Random Forest Regressor was employed as a benchmark model. As a result of this, the Gradient Boosting model beat the benchmark with a lower Mean Absolute Error (MAE) of 2.49 after cross-validation and hyperparameter tuning. To improve model explainability, the author employed SHAP (Shapley Additive Explanations) and LIME (Local Interpretable ModelAgnostic Explanations). Moreover, fairness analysis was performed to assess potential biases in the model's predictions. The findings demonstrated the necessity for bias mitigation strategies by showing that skill gaps and emotion score did not induce bias, while frustration level showed slight variations in engagement predictions. The results suggest that explainable AI can provide educators with actionable insights to identify at-risk students and tailor learning interventions. Future research should focus on bias reduction techniques, adaptive learning strategies, and model generalization to ensure fair and effective student engagement prediction in AI-driven educational platforms.