IJIET 2026 Vol.16(2): 436-445
doi: 10.18178/ijiet.2026.16.2.2516
doi: 10.18178/ijiet.2026.16.2.2516
Intelligent Learning Pathways: A Customizable LMS Framework for Modern Education
Ean H. Lim1,*, Manoranjitham Muniandy2, Kah W. Cheang3, Tong Y. Chai1,
Tien F. Yong3, and
Hui M. Kok4
1. Department of Computer Science, Faculty of Information and Communication Technology,
Universiti Tunku Abdul Rahman, Kampar, Malaysia
2. Department of Computer and Information Science, Faculty of Science and Information Technology, Universiti Teknologi, Petronas, Tronoh, Malaysia
3. Department of Information Systems, Faculty of Information and Communication Technology, Universiti Tunku Abdul Rahman, Kampar, Malaysia
4. Department of Advertising, Faculty of Arts and Social Science, Universiti Tunku Abdul Rahman, Kampar, Malaysia
Email: ehlim@utar.edu.my (E.H.L.); manoranjitham@utp.edu.my (M.M.); cheangkw@utar.edu.my (K.W.C.); chaity@utar.edu.my (T.Y.C.); yongtf@utar.edu.my (T.F.Y.); kokhm@utar.edu.my (H.M.K.)
*Corresponding author
2. Department of Computer and Information Science, Faculty of Science and Information Technology, Universiti Teknologi, Petronas, Tronoh, Malaysia
3. Department of Information Systems, Faculty of Information and Communication Technology, Universiti Tunku Abdul Rahman, Kampar, Malaysia
4. Department of Advertising, Faculty of Arts and Social Science, Universiti Tunku Abdul Rahman, Kampar, Malaysia
Email: ehlim@utar.edu.my (E.H.L.); manoranjitham@utp.edu.my (M.M.); cheangkw@utar.edu.my (K.W.C.); chaity@utar.edu.my (T.Y.C.); yongtf@utar.edu.my (T.F.Y.); kokhm@utar.edu.my (H.M.K.)
*Corresponding author
Manuscript received April 3, 2025; revised June 12, 2025; accepted August 8, 2025; published February 9, 2026
Abstract—The advancement of technology in education has opened new avenues for customized learning experiences, enhancing student engagement and learning outcomes. This research presents an “Intelligent Learning Pathways” model, a customizable Learning Management System (LMS) framework designed to meet the diverse needs of modern educational institutions. The problem addressed is the limitation of conventional LMS platforms, which often lack adaptive, student-centered pathways and real-time data insights to support individual learning journeys. The objective of the research is to develop a flexible, data-driven LMS framework that integrates Artificial Intelligence (AI) and machine learning algorithms to create tailored learning pathways, optimizing content delivery based on students’ performance, preferences, and learning styles. The methodology combines user-centric design principles with predictive analytics, enabling the system to adjust content dynamically. Testing involved deployment in a university setting with over 500 students, measuring engagement rates, learning progress, and satisfaction. Results indicate a significant increase in student engagement and learning outcomes, with 85% of participants reporting a more customized learning experience. The research concludes that incorporating intelligent algorithms in LMS can transform educational delivery, making it adaptive and customized, thereby meeting the unique needs of diverse students.
Keywords—Learning Management System (LMS), customized learning, adaptive, Artificial Intelligence (AI)-driven, student engagement
Copyright © 2026 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
Keywords—Learning Management System (LMS), customized learning, adaptive, Artificial Intelligence (AI)-driven, student engagement
Cite: Ean H. Lim, Manoranjitham Muniandy, Kah W. Cheang, Tong Y. Chai, Tien F. Yong, and Hui M. Kok, "Intelligent Learning Pathways: A Customizable LMS Framework for Modern Education," International Journal of Information and Education Technology, vol. 16, no. 2, pp. 436-445, 2026.
Copyright © 2026 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).