THE EFFECTIVENESS OF ARTIFICIAL INTELLIGENCE-BASED TUTORING SYSTEMS IN PERSONALIZED LEARNING

Abstract

The integration of Artificial Intelligence Based Tutoring Systems (AITS) has emerged as a transformative approach in personalized education, overcoming the limitations of traditional one-size-fits-all methodologies. Despite increasing adoption of AITS, there remains a critical gap in understanding its effectiveness across different learner profiles. This study aims to evaluate the effectiveness of AITS in improving personalized learning outcomes, focusing on variations in learning styles, initial abilities, and student demographic characteristics. A systematic literature review (SLR) was conducted, following PRISMA guidelines. Data were collected from leading academic databases, including Scopus and Web of Science, using a Boolean search strategy to identify relevant articles that were peer-reviewed and published between 2001 and 2024. Thematic analysis was applied to synthesize the findings of the selected studies. Analysis shows that AITS significantly improves academic performance and student engagement through adaptive learning mechanisms and real-time feedback. Specifically, the effectiveness of AITS varies based on individual learning preferences, with visual and kinesthetic learners showing the most substantial improvements. These findings emphasize the potential of AITS to foster an inclusive educational environment by accommodating the needs of diverse learners. This research contributes to the theoretical framework of personalized learning and offers practical insights for educators and policymakers in implementing AI-based educational interventions.