Generative AI for Sustainable Education: A Systematic Review of Opportunities, Challenges and Future Directions

Abstract

The emergence of Generative Artificial Intelligence (GenAI) has stimulated a significant transformation in higher education, aligning with United Nations Sustainable Development Goals while simultaneously presenting a "sustainability trade-off." While GenAI offers emerging opportunities for individualized learning, enhanced accessibility, and the development of transferable skills such as critical thinking and creativity. The sustainable integration of GenAI remains challenging because the training and deployment of large language models require energy-intensive computational infrastructure, leading to increased carbon emission and water consumption, while simultaneously introducing ethical challenges such as academic integrity, transparency and algorithmic bias. This paper presents a Systematic Literature Review (SLR) conducted in accordance with the PRISMA 2020 guidelines, synthesizing findings from 32 recent scholarly works published between 2022 and 2026. The review employs the Population–Exposure–Outcome (PEO) framework to examine how GenAI restructures learning environments across dimensions of operational efficiency, pedagogy, and ideology. Key results identify five strategic processes for sustainable implementation: ethical appropriation, infrastructure management, faculty development, curricular transformation, and pedagogical innovation. Furthermore, the review addresses global power dynamics, highlighting a shift toward plurality while cautioning against algorithmic colonialism. We propose the Generative AI-Enabled Sustainable Education (GAISE) framework as a roadmap for institutional resilience. The study concludes that the long-term sustainability of GenAI in education depends on balancing technological innovation with environmental transparency and ethical stewardship, advocating for longitudinal research to monitor future cognitive and ecological impacts Keywords: education; Generative Artificial Intelligence; Sustainable Development Goals; GenAI