The digital transformation of education has led to the widespread adoption of e-learning platforms across schools, universities, and professional training environments. While these platforms provide accessibility and flexibility, most of them rely on static and generalized content delivery models that fail to address individual learner needs. Learners differ in terms of prior knowledge, learning pace, preferences, and cognitive abilities, making uniform instructional methods ineffective for many users. This research investigates the application of Artificial Intelligence (AI) in e-learning websites to enable real-time content recommendation and adaptive learning customization. The proposed AI-based system continuously analyzes learner behavior, performance metrics, and interaction data to deliver personalized learning pathways. Machine learning algorithms, recommendation techniques, and data analytics are employed to dynamically suggest relevant learning materials and adjust course difficulty levels. A quantitative research methodology was adopted, supported by user surveys and system evaluation. The findings indicate that AI-driven personalization significantly improves learner engagement, knowledge retention, and course completion rates compared to traditional e-learning systems. The study concludes that AI has the potential to revolutionize digital education by making learning more adaptive, learner-centric, and data-driven.
Mr. Rushikesh Ashok Pathade (Sat,) studied this question.
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