Machine Learning Systems to Optimize Teaching of Sustainable Development and Renewable Energy

Abstract

The integration of renewable energy into sustainable development strategies is a global challenge. Teaching these concepts requires innovative pedagogical approaches that combine educational effectiveness with technical complexity. This paper explores the use of machine learning (ML) systems to enhance the teaching of renewable energy and sustainable development. We propose a pedagogical architecture using adaptive learning algorithms to analyze student performance and interactions, personalizing learning paths. The system adjusts teaching modules in real-time, providing individualized support. We also implement predictive models simulating the impact of renewable energy on ecosystems and infrastructure through interactive simulations, helping students understand sustainability challenges. Preliminary results show improved knowledge retention and better understanding of energy transition concepts. We discuss the pedagogical implications of ML systems in higher education and suggest directions for their large-scale implementation