Artificial Intelligence for Energy Optimization in Educational Buildings in Saudi Arabia: A Systematic Review of Design Variables and Decision-Support Approaches in Hot-Arid Climates

This study systematically reviews the role of Artificial Intelligence (AI) and Machine Learning (ML) in supporting design decisions to improve energy efficiency in educational buildings, with particular emphasis on Saudi Arabia’s hot-arid climate. A PRISMA-based Systematic Literature Review was conducted using Google Scholar, ScienceDirect, ResearchGate, and the Saudi Digital Library for studies published between 2020 and 2025. Eligible studies included peer-reviewed articles and high-quality conference papers addressing AI/ML applications in building energy performance, optimization, or design decision-making in educational or comparable buildings. Studies published before 2020, non-peer-reviewed sources, irrelevant studies, papers focused solely on non-educational buildings without transferable findings, and studies lacking full-text access were excluded. The search identified 594 records, of which 37 studies met the eligibility criteria, resulting in a final sample of 37 reviewed sources. The review shows that ML models, hybrid methods, and multi-objective optimization techniques are increasingly used to improve energy performance and support early-stage design. The most influential variables include envelope properties, glazing, shading, lighting efficiency, HVAC systems, and renewable energy integration. However, major gaps remain, particularly the limited application of AI-driven optimization in Saudi educational buildings and the lack of real-world validation in hot-arid settings. This review provides a concise foundation for future AI-assisted design strategies aligned with sustainable educational building development and Saudi Vision 2030.