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ABSTRACT

This study maps 417 peer reviewed publications (2022-2025) at the intersection of sustainability education (SE) and artificial intelligence (AI) using bibliometric methods. We chart venues, co authorship, keyword evolution, and technique usage. The results reveal that “ChatGPT” and “generative AI” are becoming the most popular terms after 2022. Outputs are still mostly from North America and Europe, although contributions from Saudi Arabia, India, and Malaysia are growing. Institutional networks are broken, which means that institutions don’t cooperate together very often. Supervised learning predominates, and neural networks are the most used single technique. We synthesize scattered findings into three practical principles–personalization–protection, competence alignment, and multi-level synchronization–that link AI uses to core SE competencies and support course to institution coordination. The study also shows a dual sustainability lens: AI can help fight climate change, but it also has implications for privacy and the environment. This shows the need for energy reporting and bias safeguards. We suggest causal and longitudinal assessments, collaborative datasets and rubrics, and capacity enhancement for resource-limited environments. Some of the problems are a short citation window (2022-2025), a bias against English speakers, and the possibility of missing databases. Overall, the subject is growing swiftly, but it requires more proof, common standards, and more environmentally friendly ways of doing things to turn AI into lasting educational value.

Keywords: sustainability education, artificial intelligence, generative AI, bibliometric analysis