Integrating artificial intelligence and data envelopment analysis for sustainable efficiency assessment in higher education

Abstract

The rapid digitalization of higher education has exposed inconsistencies between increased investments in technological infrastructure, teaching resources, and energy consumption, and the expected improvements in academic performance. This gap highlights the need for predictive and explainable approaches that can evaluate course-level efficiency while incorporating sustainability principles. In this study, Data Envelopment Analysis (DEA) under the output-oriented BCC formulation was applied to fifteen university courses to estimate their relative efficiency based on multiple inputs, including teaching staff, technological investment, digital resource usage, and energy consumption, and outputs such as passing, retention, completion, satisfaction, and learning outcomes. Only three courses achieved full efficiency ($h=1.0$), whereas the remaining ones showed efficiency gaps ranging from 5.4% to 20.2% relative to the frontier. Energy consumption and learning outcomes emerged as the most influential determinants of efficiency. Artificial intelligence models were then trained under DEA supervision to predict efficiency scores with high fidelity ($R^2>0.90$ for Random Forest and Gradient Boosting), replicating DEA behavior and enabling prospective analysis. Finally, indicators aligned with Sustainable Development Goals (SDGs 4 and 12) demonstrated that equitable digital access management reduces dropout rates. At the same time, optimized energy use enhances sustainability metrics by up to twelve points, reinforcing the relevance of AI-driven learning analytics for efficient and sustainable educational management.