AI-Based Personalized Learning System for Students: An Adaptive Framework for Learner Modeling and Content Recommendation

Abstract: Conventional classroom instruction and generic e-learning platforms tend to deliver the same content, pace, and sequence to every learner, even though students differ widely in prior knowledge, learning speed, motivation, and preferred mode of engagement. This gap between uniform delivery and diverse learner needs often results in disengagement, uneven learning outcomes, and higher dropout rates in online courses. This paper presents the design of an AI-based personalized learning system that continuously observes a student's interaction data — quiz performance, time-on-task, click patterns, and self-reported preferences — to build and update a dynamic learner profile. The profile drives a hybrid recommendation engine that combines content-based filtering with a knowledge-tracing model to select the next best learning resource, adjust difficulty, and time interventions such as hints or remedial material. A prototype covering the learner-modeling and recommendation modules was implemented using a Python-based machine learning pipeline with a web front end, and its behavior was evaluated on a simulated cohort of learners against a static, non- adaptive baseline. The adaptive pipeline produced a noticeably better fit between recommended difficulty and learner ability while reducing the number of redundant exercises presented to already-competent students. The paper also discusses the practical barriers to deploying such a system at scale, including cold-start profiling, algorithmic bias, and data privacy, and outlines directions for future work such as affect-aware adaptation and teacher-in-the-loop oversight.

Keywords: personalized learning, adaptive e-learning, artificial intelligence in education, learner modeling, knowledge tracing, recommendation systems, intelligent tutoring