AI-Driven Personalized Education Platform: Design, Architecture, and Implementation

Abstract: Education is undergoing a quiet but consequential shift. For generations, classrooms have delivered the same lesson to every student at the same pace—a model that works well for some learners and poorly for many others. The rise of machine learning offers a practical way out of this impasse. This paper presents an AI-Driven Personalized Education Platform that collects fine-grained learner data, analyses it in real time, and continuously adapts the content, assessments, and feedback each student receives. Rather than treating personalisation as a premium feature layered on top of a conventional system, we built it into the architecture from the ground up. The platform comprises five tightly coupled modules: a data collection layer, an AI analysis engine, a recommendation module, an adaptive assessment component, and a teacher-facing dashboard. A semester-long pilot with undergraduate Computer Science students showed improved quiz performance, faster identification of weak topics, and more targeted teacher interventions compared with a static content pathway. The codebase is modular and cloud-deployable, making it straightforward to extend or integrate with existing institutional infrastructure.

Keywords: Personalized Learning; Artificial Intelligence; Machine Learning; Adaptive Assessment; Recommendation System; Educational Data Mining; E-Learning.