AI-Enabled Transformation of Online Learning through Personalization

With rapid technological advancement, online education has become both a necessity and a growing demand for modern learners. The sector is increasingly moving toward personalization, driven by EdTech innovations and government initiatives leveraging Big Data analytics and Artificial Intelligence (AI). This paper presentsa comprehensive literature review to explore how personalized recommendation systems can enhance online learning and deliver individualized educational experiences. The review highlights key challenges such as low student engagement, insufficient support for personalized learning, and the digital divide. It introduces the fundamentals of personalized learning and theoretical frameworks that leverage data analytics for adaptive instruction. Using a mixed-approaches methodology, the study combines quantitative information on exam performance and course completion rates with qualitative insights from student interviews. Findings reveal that personalized recommendation systems significantly improve student engagement, retention, academic performance, and overall satisfaction. Case studies from leading institutions showcase effective implementations and the benefits of mobile-friendly, structured content delivery. This work adds to the growing conversation about online learning by showing how intelligent systems may create inclusive and revolutionary learning environments. Future research directions include assessing the long-term and demographic impacts of personalized learning systems.