Explainable artificial intelligence for predictive modeling of student stress in higher education.

Student stress in higher education remains a pervasive problem, yet many institutions lack affordable, scalable, and interpretable tools for its detection and management. Existing methods frequently depend on costly physiological sensors and opaque machine learning models, limiting their applicability in resource-constrained settings. The objective of this research is to develop a cost-effective, survey-based stress classification model using multiple machine learning algorithms and eXplainable Artificial Intelligence (XAI) to support transparent and actionable decision-making in educational environments. Drawing on a dataset of university students, the research applies a supervised machine learning pipeline to classify stress levels and identify key contributing variables. Six classification algorithms-Logistic Regression, Support Vector Machine (SVM), Decision Tree, Random Forest, Gradient Boosting, and XGBoost-were employed and optimized using grid search and cross-validation for hyperparameter tuning. Evaluation metrics included precision, recall, F1-score, and overall accuracy. The Random Forest model achieved the highest classification accuracy of 0.89, followed by XGBoost at 0.87, Gradient Boosting at 0.85, Decision Tree at 0.83, SVM at 0.82, and Logistic Regression at 0.81. SHAP (SHapley Additive exPlanations) analysis was conducted to interpret model predictions and rank feature importance. The analysis revealed five principal predictors: blood pressure, perceived safety, sleep quality, teacher-student relationship, and participation in extracurricular activities. Results demonstrate that both physiological indicators and psychosocial conditions contribute meaningfully to stress prediction. The study concludes that institutional interventions targeting health monitoring, campus safety, behavioral support, relational pedagogy, and extracurricular engagement can effectively mitigate student stress. These findings provide an empirical foundation for the development of integrated policies in higher education aimed at promoting student well-being.