E-Learning Platforms with Personalized AI-Based Recommendations

The rapid expansion of e-learning platforms has improved access to education but has also highlighted limitations of one-size-fits-all instructional models. Personalized AI-based recommendation systems address this challenge by tailoring learning content, sequencing, and assessments according to individual learner needs. This paper presents a comprehensive study of AI-driven personalization in e-learning platforms using secondary data collected from published research, industry reports, and platform analytics. Adoption trends from 2019 to 2024, comparative performance metrics between traditional and AI-based systems, and commonly used recommendation techniques are analyzed. The paper further proposes a hybrid system architecture integrating knowledge tracing, deep learning, and reinforcement learning. Results from secondary data indicate significant improvements in course completion rates, learner engagement, and knowledge retention when AI-based personalization is employed. Ethical concerns, evaluation metrics, and future research directions are also discussed.