AI-Powered Personalized Education System for Adaptive, Interactive and Student-Centric Learning

Abstract

Conventional digital learning platforms often present a largely uniform sequence of lessons and exercises, even though students differ in prior knowledge, pace, interests and performance. This paper describes the design of an AI-powered personalized education system intended to adapt learning materials and assessments to individual learner needs. The proposed workflow collects learning preferences and activity data, prepares performance records for analysis, identifies strengths and areas requiring support, and recommends relevant study resources, quizzes and follow up activities. Immediate feedback is intended to help learners understand errors, while a teacher facing dashboard supports progress monitoring and instructional planning. The system concept includes user authentication, profile management, adaptive learning, assessment, recommendation, reporting and database services. The supplied Phase II presentation identifies Python, machine-learning libraries, web technologies and a database as candidate implementation components it lists more than one possible stack, so the final deployed configuration must be confirmed before replication. This presents a design oriented account of the system and a validation plan. The source presentation does not provide a dataset, quantitative model results or completed user study findings therefore no numerical improvement in accuracy, engagement or academic performance is claimed. The paper also outlines privacy, usability, scalability and fairness considerations for future evaluation.