Multi-Level Explainable AI for Predicting Student Depression Risk: Global, Subgroup, and Individual Insights

Depression among university students is a critical global problem that affects academic performance, well-being, and dropout rates. Although machine learning models offer strong predictive power, their opacity limits their adoption in educational and clinical contexts. Most existing approaches emphasize either global or individual explanations, leaving a gap in multi-level interpretability that can inform institutional policy and personalized support. This paper introduces a novel multi-level explainable AI (XAI) framework that unifies SHAP for global importance ranking, H-LIME for hierarchical subgroup explanations, and counterfactual reasoning for actionable individual recourse. By establishing a generalizable theory of hierarchical interpretability that bridges global, group, and local levels of explanation, the framework advances both methodological rigor and practical relevance. Using a dataset of 27,901 students with demographic, academic, lifestyle and psychosocial factors, Random Forest achieved a strong predictive performance (accuracy = 0.839, ROC–AUC = 0.916). The results consistently identified suicidal ideation, academic pressure, and financial stress as dominant factors, while subgroup and individual analyses revealed contextual and personalized nuances that global explanations alone obscure. Individual-level counterfactuals further suggested feasible workload or lifestyle adjustments, offering practical recourse for at-risk students. Limitations include dependence on self-reported survey data and the fact that the sample is from a single country, which may limit the generalizability of the findings. This unified approach offers a reusable methodological framework for achieving multi-level interpretability in sensitive AI applications. It also provides evidence-based guidance for the early identification of at-risk individuals, customized interventions, and the development of equity-based institutional policies.