Personalized Learning Through AI: From Rule-Based Systems to AI Mentors

Abstract

Artificial intelligence (AI) has the potential to greatly facilitate student learning by providing personalized instruction tailored to each student’s individual needs and knowledge level. Although personalized learning is widely considered as one of the most effective teaching methods, it has been difficult to implement in traditional classrooms. In this context, AI offers a viable alternative. Various AI tools now perform key pedagogical functions, such as diagnosing learners’ knowledge levels, recommending content and learning paths, and providing feedback and personalized learning based on learners’ profiles. Since these tools differ in their capabilities and personalize learning in different ways, they should be applied differently according to pedagogical purposes and learner variables when implemented in educational settings. This study introduces five stages of AI-powered personalization grounded in learning theories. These stages take into account various factors, including technological capabilities, learner characteristics, and curricular structures at the elementary, secondary, and college levels. We further illustrate how these five stages can be applied to language education at different school levels. Finally, we present the pedagogical implications of using AI in education and address the challenges that must be considered for its effective and equitable implementation.