This report presents a comprehensive study on how AI enhances personalized elearning through tools such as recommendation engines, Natural Language Processing (NLP) chatbots, learning analytics, predictive modeling, and intelligent tutoring systems (ITS). The proposed framework analyzes student behavior— including performance, accuracy, activity logs, error patterns, and engagement metrics—to generate personalized content sequences tailored to each learner’s strengths and weaknesses. Findings show that AI-based personalized learning significantly improves learner engagement, retention, and performance, while reducing the workload on educators. The project also highlights the advantages, limitations, ethical concerns, and future opportunities associated with AI-driven education. Results indicate that adaptive learning systems increase learning efficiency by providing the right content at the right time, making education more accessible, flexible, and impactful.
Sakshi Yashwant Bhagwat (Tue,) studied this question.