This study investigates the creation and application of an artificial intelligence (AI)driven personalized learning system intended to improve learning outcomes by customizing training to each student's needs. The system creates personalized learning paths by analyzing students' learning patterns, preferences, and performance metrics through the use of sophisticated machine learning algorithms. Using a mixed-methods approach, the study combines qualitative input from educators and learners with quantitative data from learning analytics. Preliminary results show notable enhancements in academic achievement, retention, and engagement for system users. The study also addresses ethical issues, the consequences for scalability, and the role of teachers in an increasingly automated learning environment. This study adds to the continuing discussion about the future of education in the digital era by addressing the complexity of personalized learning. Artificial intelligence (AI) has advanced so quickly that it has transformed many industries, most notably education, where individualized learning systems are being used more and more. In order to improve student engagement and learning outcomes, this article investigates the creation and application of an AI-based personalized learning system. The suggested system makes use of machine learning algorithms to assess each user's learning preferences, styles, and performance indicators in order to provide customized learning opportunities. Through the use of real-time feedback, predictive analytics, and adaptive content delivery, the system creates a dynamic learning.
Artificial Intelligence, Personalized Learning, Adaptive Learning Systems, Individual Learning Styles, Learning Outcomes
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