IJIET 2026 Vol.16(1): 117-125
doi: 10.18178/ijiet.2026.16.1.2488
doi: 10.18178/ijiet.2026.16.1.2488
Methodological Framework for Designing AI-Based Distance Learning Platforms
Maxot Rakhmetov1, Elmira Abdykerimova2, Gadilbek Alzhanov1, Balaussa Orazbayeva3,
and Bayan Kuanbayeva1,*
1. Department of Computer Science, Faculty of Physics, Mathematics and Information technology, Kh. Dosmukhamedov Atyrau University, Atyrau, Kazakhstan
2. Department of Fundamental Sciences, Caspian University of Technology and Engineering Named after Sh.Yessenov, Aktau, Kazakhstan
3. Department of Computer Science, L.N. Gumilyov Eurasian National University, Astana, Kazakhstan
Email: maksot.raxmetov.96@gmail.com (M.R.); elmira.abdykerimova@yu.edu.kz (E.A.); alzhanov82@gmail.com (G.A.); orazbayeva.balausa@gmail.com (B.O.); bayan.kuanbayeva69@gmail.com (B.K.)
*Corresponding author
2. Department of Fundamental Sciences, Caspian University of Technology and Engineering Named after Sh.Yessenov, Aktau, Kazakhstan
3. Department of Computer Science, L.N. Gumilyov Eurasian National University, Astana, Kazakhstan
Email: maksot.raxmetov.96@gmail.com (M.R.); elmira.abdykerimova@yu.edu.kz (E.A.); alzhanov82@gmail.com (G.A.); orazbayeva.balausa@gmail.com (B.O.); bayan.kuanbayeva69@gmail.com (B.K.)
*Corresponding author
Manuscript received July 18, 2025; revised August 18, 2025; accepted September 1, 2025; published January 13, 2026
Abstract—Artificial Intelligence (AI) opens up new perspectives for the transformation of distance learning; however, there is still no clearly structured methodological framework for the development of effective AI-based learning platforms. This study proposes the framework model “learning behavior – AI-oriented scenarios”, which aims to identify key pedagogical and technological factors that contribute to effective learning in a digital environment. Based on an analysis of the scientific literature in educational technologies and the use of AI in teaching, the main characteristics of learning behavior are identified: personalized goal setting, adaptive response to feedback, and autonomous engagement. These characteristics correspond to constructivist and experiential approaches to learning. The study examines how intelligent recommendation systems, adaptive content, and AI-based learning behavior analytics contribute to the development of these characteristics and enhance educational outcomes. As an example, a case study is presented on the development of a prototype AI platform for distance learning, whose effectiveness is supported by survey results demonstrating increased student motivation and engagement. The findings address three key research questions and contribute to the theoretical and methodological foundation for integrating AI into the design of distance education.
Keywords—artificial intelligence, distance learning, learning behavior, adaptive scenarios, educational technologies
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—artificial intelligence, distance learning, learning behavior, adaptive scenarios, educational technologies
Cite: Maxot Rakhmetov, Elmira Abdykerimova, Gadilbek Alzhanov, Balaussa Orazbayeva, and Bayan Kuanbayeva, "Methodological Framework for Designing AI-Based Distance Learning Platforms," International Journal of Information and Education Technology, vol. 16, no. 1, pp. 117-125, 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).