Generative AI as an Enabler of Sustainable Education: Theoretical Perspectives and Future Directions

Abstract

This theoretical research paper explores Generative Artificial Intelligence (AI) as a transformative force in sustainable education within the digital era. Through a comprehensive literature review of peer-reviewed articles, conference proceedings, and policy documents in sustainable education, AI in education, and learning theories, we propose a novel conceptual framework: Generative AI-Enabled Sustainable Education (GAISE). This framework synthesises principles from sustainable education theories, AI in education, constructivism, connectivism, and transformative learning. The GAISE model elucidates how Generative AI's capabilities in content generation, personalisation, adaptive learning, and natural language processing can enhance sustainability literacy and promote transformative learning experiences. Our analysis reveals the framework's potential to integrate Generative AI into curriculum design, teaching methodologies, assessment strategies, and teacher professional development for sustainable education. Critical ethical considerations include data privacy, equity, and human-AI collaboration in educational contexts. The paper identifies key challenges in implementing Generative AI for sustainable education and proposes future empirical research directions and policy recommendations. This work contributes to the intersection of AI and sustainable education, offering theoretical insights and practical pathways for educators and policymakers to leverage Generative AI in promoting sustainability competencies in education.