AI-POWERED PERSONALIZED LEARNING: PROSPECTS, DIFFICULTIES, AND FUTURE PATHS

Abstract

The digital transformation in education has opened up great opportunities to improve the effectiveness of learning through intelligent technology. One prominent approach is AI-powered personalized learning, which is an adaptive learning system based on artificial intelligence designed to meet the individual needs of each learner. This study is a systematic literature review that aims to evaluate the prospects, challenges, and future directions of the implementation of AI-based personalized learning. Through a review of scientific literature from various leading databases during the period 2010–2024, five main themes are identified: technology design and AI models, instructional effectiveness, user acceptance, ethical issues and educational equity, and policy and infrastructure. The findings show that these systems have significant potential to accelerate learning outcomes and improve individual learning experiences, but also face serious challenges in terms of algorithmic bias, data privacy, the digital divide, and educational institution readiness. As a theoretical contribution, this study proposes an integrative conceptual framework to understand the dynamics and complexities of AI implementation in personalized learning. Practical implications and further research directions are also presented as an effort to encourage inclusive, ethical, and contextual technology integration in future education systems.

Keywords: Artificial Intelligence, Personalized Learning, Adaptive Education, Systematic Literature Review, Educational Technology