Generative AI as a Catalyst for Sustainable Learning: Proposing an Adaptive Pedagogical Strategy

Abstract

This paper proposes an innovative adaptive pedagogical strategy that leverages Generative AI to catalyse sustainable learning outcomes in higher education. As educational technology continues to evolve, the integration of AI presents unprecedented opportunities to enhance long-term knowledge retention and skill development. However, current pedagogical approaches often fall short of achieving genuinely sustainable learning. This research addresses this gap by exploring the potential of Generative AI to create personalised, adaptive learning experiences that promote deep understanding and continuous growth. The study develops a conceptual framework synthesising sustainable learning theories, adaptive learning models, and AI-enhanced personalised learning concepts. Building on this foundation, we present a detailed adaptive pedagogical strategy that integrates Generative AI capabilities to identify learning patterns, generate tailored content, and adapt to individual student needs in real-time. Theoretical implementation scenarios across STEM education, language learning, and critical thinking skills development demonstrate the strategy's versatility and potential impact. Expected outcomes include improved long-term retention, enhanced transfer of knowledge, and increased learner autonomy. The paper discusses implications for educational practice, including necessary shifts in teaching methodologies and professional development needs. While acknowledging current technological constraints and the need for empirical validation, this research contributes to the growing field of AI in education. It offers a promising approach to fostering sustainable learning outcomes in the digital age.