AI-Driven Personalized Learning Analytics Platform

In the rapidly evolving landscape of educational technology, traditional one-size-fits-all approaches are increasingly inadequate for addressing diverse student needs. This paper proposes an AI-driven platform for personalized learning analytics that collects, processes, and analyzes student data from multiple sources to provide customized learning paths and realtime insights. The system leverages advanced data analytics, machine learning, and streaming data technologies to monitor student engagement, identify learning patterns, and deliver personalized recommendations. By integrating data from learning management systems (LMS) and real-time student activity logs, the platform enables educators to make data-informed decisions and provide timely interventions. The system architecture is designed with scalability in mind, ensuring it can handle large datasets while maintaining compliance with data privacy regulations such as GDPR and ISO/IEC 27001. Through real-time analytics and personalized recommendations, the platform aims to improve student outcomes, enhance teaching strategies, and enable proactive interventions. This paper details the system’s architecture, methodologies, implementation challenges, and potential impact on educational institutions. Experimental results demonstrate significant improvements in student engagement, learning outcomes, and educator effectiveness when compared to traditional approaches.