Exploring the evolution of artificial intelligence in education: from AI-guided learning to learner-personalized paradigms

Abstract

Advancements in computing and information technologies have significantly transformed educational practices. This transformation can be understood through three paradigms. The first paradigm (Artificial Intelligence Guided Learning) involves systems that model knowledge and guide cognitive processes, positioning learners as passive recipients. The second paradigm (Artificial Intelligence Interactive Learning) fosters collaboration between learners and intelligent systems, enabling interactive learning experiences. The third and most advanced paradigm (Artificial Intelligence Driven Personalized Learning) places learners at the center, using intelligent systems to personalize education and empower students to take charge of their learning journey. These paradigms illustrate a trajectory toward learner-centric approaches that emphasize personalization, adaptability, and agency. Intelligent systems in education increasingly support reflection and iterative learning, where students actively shape their educational experiences, and the systems evolve based on real-time learner data. This transformation promises to make education more flexible, responsive, and tailored, addressing diverse learning needs while advancing a data-driven, personalized approach to pedagogy.