AI-Enabled Smart Infrastructure for Sustainable STEM Education Ecosystems: Leveraging Intelligent Systems to Support Renewable Energy, Resource Optimization, and Green Campuses

The increased digitization of educational systems and the worldwide need to be sustainable require the implementation of artificial intelligence (AI) into campus life. Laboratories, computing clusters, smart classrooms, internet of things enabled devices all consume a lot of energy and resources and traditional methods of management are having a hard time balancing demand and optimizing the use of resources and ensuring efficiency. The paper presents a smart infrastructure design that uses AI, integrates renewable energy systems, and green technologies to aid in sustainable STEM campuses. Based on this contract, the framework incorporates predictive analytics, long short-term memory (LSTM) forecast, reinforcement learning-based optimization and graph-structured digital twin models for load predicting, multi-resource coordination in real-time and adaptive utility management. Prototype implementation and hybrid simulation validation demonstrate the effectiveness of the proposed optimization in terms of energy consumption (21.4%), amount of renewable energy consumed (18.7%) and carbon emission (15.3%) over the optimization rules-based and smart-grid baselines. These quantified results validate the framework’s scalability and real-world relevance for sustainable AI-driven learning environments.