Intelligent Systems for Students: Enhancing Online Learning Experiences and Improving Outcomes through Personalized Education

With technology rapidly changing, online education has emerged as both a demand and necessity for the modern-day student. Through a systematic literature review, this paper aims to investigate the impact of personalized recommendation systems on supporting online learning and providing students with customized educational experiences. We examine these questions by conducting a wide-reaching literature review, which reveals issues such as low student engagement or poor support for personalised learning and the digital divide. After that, we present the basics of personalized learning and theoretical frameworks of personalization, which depend on data analytics for adaptive learning. Our study uses a mixed-methods research design, combining quantitative data on exam performance and course completion rates with qualitative findings from student interviews. The results demonstrated that personalized recommendation systems have a positive effect on student engagement, retention and academic performance and led to higher satisfaction among Students as well. Top-trending use cases from the most innovative institutions are used to illustrate successful implementations and the impact having a more streamlined and mobile-friendly content organization framework has had on student engagement. This study adds to the corpus of work in online education, particularly around how technology can enable inclusive and transformative learning environments. Finally, the paper suggests future directions in exploring the long-term effects and demographic implications of personalized learning systems.