The rapid evolution of digital technologies has transformed the landscape of education, enabling flexible and remote learning opportunities across the world. Despite these advancements, many existing e-learning platforms still rely on standardized content delivery models that fail to address individual learner differences in pace, preferences, and prior knowledge. This limitation often leads to reduced engagement, lower knowledge retention, and inconsistent learning outcomes. To address these challenges, this research proposes an artificial intelligence–driven personalized learning platform designed to provide adaptive, data-driven, and learner-centric educational experiences. The proposed platform integrates machine learning, educational data mining, and learning analytics to analyse learner behaviour, performance patterns, and engagement metrics. Based on these insights, the system dynamically recommends learning resources, adjusts learning paths, and provides real-time feedback tailored to individual needs. A hybrid recommendation engine combining collaborative filtering and content-based techniques is implemented to ensure relevant content delivery, while predictive analytics models identify at-risk learners and enable timely interventions. Additionally, an adaptive assessment module modifies question difficulty according to learner performance, ensuring accurate competency evaluation and improved learning motivation. The research follows a design science methodology involving prototype development, simulated dataset testing, and performance evaluation using metrics such as recommendation relevance, prediction accuracy, learner engagement, and user satisfaction. Experimental results indicate that personalized learning paths significantly improve learner performance, increase course completion rates, and enhance overall engagement compared to non-adaptive learning environments. The platform also provides interactive analytics dashboards that support educators in monitoring student progress and making data-driven instructional decisions. Overall, Vistara Learn demonstrates the potential of artificial intelligence to transform traditional e-learning systems into intelligent and adaptive ecosystems. By combining personalization, predictive insights, and interactive analytics within a unified framework, the platform contributes to improving learning efficiency, accessibility, and educational decision-making. The study highlights the importance of AI-driven personalization in modern education and provides a scalable foundation for future research and real-world deployment of intelligent learning systems.
Artificial Intelligence in Education, personalized learning, adaptive learning systems, machine learning, learning analytics, intelligent tutoring systems, recommendation engines, student performance prediction, educational data mining, digital learning platforms, real-time feedback
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