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Personalized Course Recommendation Engine for Adaptive E-Learning Environments Using Machine Learning

Abdullah Mohammed
College of Computer Science and Information Technology, University of Karbala, Iraq Sara Mohammed
College of Computer Science and Information Technology, University of Karbala, Iraq Zaid Hussein
Department of Computer Engineering, Al-Iraqia University, Iraq
Vol 1 No 1 (2026): Volume 1, Issue 1, 2026 (July-August) Pages: 25-35 Type: Research Article Published: 10-08-2026 DOI: 10.47001/IJAIDA/2026.101004

Abstract

The rapid growth of online education platforms has resulted in an enormous volume of learning resources being available to students worldwide. While the abundance of educational content provides numerous learning opportunities, it also creates challenges for learners in identifying courses that align with their interests, skills, career objectives, and learning preferences. Traditional recommendation methods often rely on manual searches and generic course rankings, which may not effectively address individual learner needs [1]. To overcome these limitations, this research proposes an Intelligent E-Learning Course Recommendation Engine that utilizes machine learning, learning analytics, and personalized recommendation techniques to provide customized course suggestions. The proposed framework analyzes learner profiles, academic performance, interests, behavioral patterns, and course characteristics to generate relevant recommendations. Collaborative filtering, content-based filtering, and hybrid recommendation approaches are integrated to improve recommendation accuracy and learner satisfaction. Experimental analysis demonstrates that the proposed system enhances course discovery, increases learner engagement, improves completion rates, and supports personalized learning pathways. The study highlights the significance of intelligent recommendation systems in modern digital education environments and discusses future opportunities for adaptive and AI-driven learning platforms.

Keywords

E-Learning, Recommendation System, Machine Learning, Learning Analytics, Personalized Learning, Educational Data Mining, Artificial Intelligence, Hybrid Recommendation

Citation of this Article

Abdullah Mohammed, Sara Mohammed, & Zaid Hussein. (2026). Personalized Course Recommendation Engine for Adaptive E-Learning Environments Using Machine Learning. International Journal of Artificial Intelligence and Data Analytics, 1(1), 25-35. DOI: https://doi.org/10.47001/IJAIDA/2026.101004

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License: This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0).