Artificial Intelligence as a Catalyst for Generative Learning: Educational Applications, Pedagogical Foundations, and Meaning-Making Outcomes

Abstract

The integration of Artificial Intelligence (AI) into education has expanded opportunities for teaching, learning, and knowledge construction. Generative learning a constructivist-based pedagogical approach emphasizing active engagement, elaboration, and meaning-making supports deeper comprehension and long-term retention by encouraging learners to create internal connections between new information and prior knowledge. This paper synthesizes theoretical foundations of generative learning with contemporary AI-driven pedagogical innovations to examine how AI can strengthen active knowledge construction. Drawing on empirical evidence, the study highlights how AI facilitates personalized learning, adaptive feedback, and scaffolding of complex cognitive tasks. It also critically evaluates ethical, equity, and accessibility concerns, including algorithmic bias, data privacy, and technological disparities. The article proposes an integrated framework illustrating how AI can operationalize generative learning principles at scale while emphasizing the enduring role of educators in mediating AI-enhanced learning experiences. Ultimately, AI can serve as a transformative catalyst for generative learning when applied thoughtfully, ethically, and contextually.