AI-Driven E-Learning Platforms for Personalized Education

Abstract

Artificial Intelligence (AI) has become a disruptive force in the current field of education, which allows one to create intelligent e-learning and provide a learner with personalized, adaptive, and learner-centric educational experience. Conventional e-learning communication models mostly embrace one fit model and neglect the individual learner in terms of cognitive abilities, learning pace, background knowledge, motivation, as well as learning preferences. This disadvantage results in in optimal interaction, high turnover, and learning imbalances. To overcome these difficulties, AI-based e-learning systems are used, which is based on machine learning algorithms, learning analytics, natural language processing, recommendations systems, and relevant learning contents, assessments, and feedback is delivered to particular learners in real time. The following paper gives a detailed research of AI-based e-learning websites in personalized learning. It explores the theoretical basis of personalized learning, the history of intelligent tutoring systems, and how AI methods like supervised learning, reinforcement learning, deep learning, and knowledge tracing are applied in adaptive learning. The paper also examines the architectures, data streams, learner control strategies and evolutional decision making systems that support modern AI-driven learning systems. Some of the most essential issues regarding data privacy, bias in algorithms, interpretability, scalability, and morality are evaluated crucially. There is a comprehensive methodology that is proposed and includes system design, dataset preparation, feature engineering, personalization algorithms, and evaluation metrics. The results of the experiment are discussed based on the comparative performance analysis, indicators of the learner engagement, and the improvements in the learning outcomes. The results show that AI-based personalization benefits lead to substantial satisfaction with the learners, retention of knowledge, and academic results as opposed to conventional e-learning sites. The paper also ends by emphasizing subsequent research interests, which include explainable AI in learning, multimodal learning analytics, and lifelong personalized learning ecosystems.