Beyond One-Size-Fits-All Learning: An AI-Driven Personalized Learning Pathway Framework
DOI:
https://doi.org/10.58524/oler.v6i2.1101Keywords:
Adaptive Systems, Artificial Intelligence, Educational Technology, Learning Analytics, Personalized LearningAbstract
Artificial intelligence has accelerated the evolution of personalized learning, yet most learning management systems continue to rely on static instructional pathways that inadequately accommodate learners’ cognitive and behavioral diversity. This study evaluated an AI-driven Personalized Learning Pathway (PLP) framework that integrates learner analytics, intelligent content recommendation, and adaptive assessment within a unified learning environment to enhance student engagement and academic achievement. A mixed-methods quasi-experimental design was conducted with 240 undergraduate students from four universities across Southeast Asia. Students in the experimental group learned through an AI-augmented learning management system, while the control group used a conventional platform. The findings demonstrate that the AI-driven PLP framework consistently improved student engagement, motivation, course completion, and academic achievement compared with traditional learning management systems. Students also exhibited stronger learning adaptability and more effective responses to personalized feedback, both of which emerged as key contributors to academic success. By integrating behavioral analytics with real-time instructional adaptation, the proposed framework moves beyond content personalization toward a responsive learning ecosystem. This study contributes a scalable AI-enabled instructional framework that supports learner-centered higher education and provides practical guidance for implementing adaptive digital learning environments
References
Ahda, N. V., Medriati, R., Purwanto, A., Putri, D. H., Jovani, D., Siregar, H., & Fajri, A. K. (2025). Training on improving artificial intelligence literacy in supporting 21st century learning for students at SMAN 6 Central Bengkulu. Jurnal Pengabdian Kepada Masyarakat, 3(3), 126–134. https://doi.org/10.58723/aktual.v3i3.474
Alalawi, K., Athauda, R., Chiong, R., & Renner, I. (2024). Evaluating the student performance prediction and action framework through a learning analytics intervention study. Education and Information Technologies, 30(3), 2887–2916. https://doi.org/10.1007/s10639-024-12923-5
Alalawi, K., Athauda, R., Chiong, R., & Renner, I. (2025). Evaluating the student performance prediction and action framework through a learning analytics intervention study. Education and Information Technologies, 30(3), 2887–2916. https://doi.org/10.1007/S10639-024-12923-5
Alenezi, A. (2023). Teacher perspectives on ai-driven gamification: impact on student motivation, engagement, and learning outcomes. Information Technologies and Learning Tools, 97(5), 138–149. https://doi.org/10.33407/itlt.v97i5.5437
Aljawad, A., Intelligence, A. G., & Intelligence, H. A. (2025). Toward human-level artificial foundations and trajectories intelligence : Technological. International Journal of Advances in Artificial Intelligence and Machine Learning, 2(3), 149–159. https://doi.org/10.58723/ijaaiml.v2i3.487
Amin, M. R. M., Ismail, I., & Sivakumaran, V. M. (2025). Social sciences & humanities open revolutionizing education with artificial intelligence ( AI )? challenges , and implications for open and distance learning ( ODL ). Social Sciences & Humanities Open, 11(December 2023), 101308. https://doi.org/10.1016/j.ssaho.2025.101308
Arif, M., Kasturi, M., Abd, N., Hassan, H., Fikri, Y., Zekkari, M., & Rismawati, B. V. (2025). Artificial Intelligence in Islamic Religious Education: A PRISMA-Based Review and the SEI-CHAT Framewor. Online Learning in Educational Research, 6(1), 65–85. https://doi.org/10.58524/oler.v6i1.993
Bernacki, M. ., Lobczowski, N. G., & Greene, M. J. (2021). A systematic review of research on personalized learning: personalized by whom, to what, how, and for what purpose(s)? Educational Psychology Review, 33(4), 1675–1715. https://doi.org/10.1007/S10648-021-09615-8
Brahim, G. Ben. (2022). Predicting student performance from online engagement activities using novel statistical features. Arabian Journal for Science and Engineering, 47(8), 10225–10243. https://doi.org/10.1007/s13369-021-06548-w
Cao, W., Tam, N., & Guo, W. (2025). Personalized learning and adaptive systems: AI-Driven educational innovation and student outcome enhancement. International Journal of Education and Humanities, 20(2), 173–182. https://doi.org/10.54097/tc5k6825
Chattaraj, D., & Parakkate, A. (2021). Why learning space matters : A script approach to the phenomena of learning in the emergency remote learning scenario. Journal of Computers in Education, 8(3), 343–364. https://doi.org/10.1007/s40692-021-00182-z
Chi, M., Wang, N., Wu, Q., Cheng, M., Zhu, C., Wang, X., & Hou, Y. (2022). Implementation of the flipped classroom combined with problem-based learning in a medical nursing course : A quasi-experimental design. Healthcare, 10(12), 1–11. https://doi.org/10.3390/healthcare10122572
Dahlan, S. A. (2026). AI in english language learning : Balancing innovation , opportunity , and human connection. Indonesian Journal of Pedagogy and Teacher Education, 4(1), 1–9. https://doi.org/10.58723/ijopate.v4i1.586
Datu, J. A. D., & Buenconsejo, J. U. (2021). Academic Engagement and Achievement Predict Career Adaptability. The Career Development Quarterly, 69(March), 34–48. https://doi.org/10.1002/cdq.12247
Du, J., & Alm, A. (2024). Education sciences the impact of ChatGPT on english for academic purposes ( EAP ) Students ’ language learning experience : A Self-Determination Theory Perspective. Education Sciences, 14(7), 726. https://doi.org/10.3390/educsci14070726
Dutta, I., Foster, J., Mishra, A., & Nandeibam, S. (2024). Identifying the vulnerable: Concepts and measurement. Review of Income and Wealth, 71(1). https://doi.org/10.1111/roiw.12722
Dutta, S., Ranjan, S., Mishra, S., Sharma, V., Hewage, P., & Iwendi, C. (2024). Enhancing educational adaptability: A review and analysis of AI-driven adaptive learning platforms. 2024 4th International Conference on Innovative Practices in Technology and Management (ICIPTM). https://doi.org/10.1109/ICIPTM59628.2024.10563448
Dziuban, C. D., Howlin, C., Moskal, P., Johnson, C., Parker, L., & Campbell, M. (2018). Adaptive Learning: A Stabilizing Influence Across Disciplines and Universities. Online Learning, 22(3). https://doi.org/10.24059/olj.v22i3.1465
Española, R. P., & Ouano, J. A. (2024). Understanding motivation loss and behavioral disengagement of tertiary students in flexible learning: A self-determination theory perspective. Malaysian Journal of Learning and Instruction, 21(1), 217–247. https://doi.org/10.32890/mjli2024.21.1.8
Hartono, R., Ridho, A. F. A., Zaki, F. R., & Ghifari, M. F. Al. (2024). The Influence of AI Chat GPT Technology on the Mindset of Educational Technology Students at Ibn Khaldun University , Bogor. Jurnal Ilmiah Teknologi Pendidikan, 3(2), 73–79. https://doi.org/10.58723/finger.v3i2.242
Hernández-Herrera, J. R., Ortiz-Bejar, J., & Ortiz‐Bejar, J. (2026). Adaptive and Personalized Learning in Higher Education: An Artificial Intelligence-Based Approach. Education Sciences, 16(1), 109. https://doi.org/10.3390/educsci16010109
Hill, J., & Smith, K. (2023). Visions of blended learning : Identifying the challenges and opportunities in shaping institutional approaches to blended learning in higher education. Technology, Pedagogy and Education, 32(3), 289–304. https://doi.org/10.1080/1475939X.2023.2176916
Jannah, D. M., Huda, S., Yasin, M., Sanatang, Fakhri, M. M., Wahid, S. N., Amukune, A. B. K., Soeharto, & Stephen. (2025). AI Literacy, technical skills, and ethical awareness in predicting students’ learning performance. Online Learning in Educational Research, 6(1), 1–12. https://doi.org/10.58524/oler.v6i1.565
Jena, M., & Dehuri, S. (2022). An Integrated novel framework for coping missing values imputation and Classification. IEEE Access, 10(July), 69373–69387. https://doi.org/10.1109/ACCESS.2022.3187412
Johar, N. A., Kew, S. N., Tasir, Z., & Koh, E. (2023). Learning analytics on student engagement to enhance students ’ learning performance : A Systematic review. Sustainability, 15, 1–25. https://doi.org/10.3390/su15107849
Khalil, M., Topali, P., Ortega, A., & Erkan, A. (2024). Video analytics in digital learning environments : exploring student behaviour across different learning Contexts. Technology, Knowledge and Learning, 29(4), 1877–1905. https://doi.org/10.1007/s10758-023-09680-8
Kurkure, P. N. (2025). Assessing the impact of knewton on developing teaching effective and interactive lessons of b ed college students. Journal of East-West Thought, 15(2), 9–15.
Lavrova, E., Primakov, S., Salahuddin, Z., & Beuque, M. (2023). Precision-medicine-toolbox : An open-source python package for the quantitative medical image analysis. Software Impacts, 16(February), 100508. https://doi.org/10.1016/j.simpa.2023.100508
Liu, Y., Xiong, W., Xiong, Y., & Wu, Y.-F. (2023). Generating timely individualized feedback to support student learning of conceptual knowledge in Writing-To-Learn activities. Journal of Computers in Education, 11(2), 367–399. https://doi.org/10.1007/s40692-023-00261-3
Mainwaring, H. E. (2021). Exploring using NVivo software to facilitate inductive coding for thematic narrative synthesis. British Journal of Midwifery, 29(11), 628–632. https://doi.org/10.12968/BJOM.2021.29.11.628
Morrow, M., & Lee, D. (2019). Implementing individualized learning in a legacy learning management system. International Journal of Designs for Learning, 10(1), 131–144. https://doi.org/10.14434/ijdl.v10i1.22500
Mulenga, R., & Shilongo, H. (2025). Hybrid and blended learning models : Innovations , challenges , and future directions in education. Acta Pedagogia Asiana, 4(1), 1–13. https://doi.org/10.53623/apga.v4i1.495
Purwanto, A., Asbari, M., Santoso, T. I., Sunarsi, D., & Ilham, D. (2021). Education Research quantitative analysis for little respondents : Comparing of Lisrel , Tetrad , GSCA , Amos , SmartPLS, WarPLS and SPSS. Jurnal Studi Guru dan Pembelajaran, 4(2), 335–350. https://doi.org/10.30605/jsgp.4.2.2021.1326
Qadir, H. M., Khan, R., Rasool, M., Sohaib, M., Shah, M. A., & Hasan, M. J. (2025). An adaptive feedback system for the improvement of learners. Scientific Reports, 15(1), 17242. https://doi.org/10.1038/s41598-025-01429-w
Riady, Y., Arisanty, M., & Kuswanti, E. (2025). The role of open and distance education in digital ecosystem transformation : A qualitative study on enhancing educational accessibility. Indonesian Journal of Elearning and Multimedia, 4(2), 81–89. https://doi.org/10.58723/ijoem.v4i2.386
Rusmansyah, Syahmani, Erika, F., Kusuma, A. E., & TienTien, L. (2025). Integrating AR–AI–STEAM for 6C skill development in wetland learning: Trends and knowledge mapping from 2019–2025. Online Learning in Educational Research, 5(2), 457–477. https://doi.org/10.58524/oler.v5i2.945
Sajja, R., Sermet, Y., Cwiertny, D. M., & Demir, İ. (2025). Integrating AI and Learning Analytics for Data-Driven Pedagogical Decisions and Personalized Interventions in Education. Technology Knowledge and Learning. https://doi.org/10.1007/s10758-025-09897-9
Saleem, S., Aziz, M. U., Iqbal, M. J., & Abbas, S. (2025). AI in education : Personalized learning systems and their impact on student performance and engagement. The Critical Review of Social Sciences Studies, 3(1), 2445–2459. https://doi.org/10.59075/c35qa453
Seo, K., Tang, J., Roll, I., Fels, S., & Yoon, D. (2021). The impact of artificial intelligence on learner–instructor interaction in online learning. International Journal of Educational Technology in Higher Education, 18(1). https://doi.org/10.1186/s41239-021-00292-9
Siska, J., & Franzhardi, D. (2025). The effect of conducting practical hand-on activities in SMK Negeri 4 Muko-Muko. Jurnal Pendidikan Teknologi Informasi dan Komunikasi, 4(1), 50–57. https://doi.org/10.58723/jentik.v4i1.434
Tretow-fish, T. A. B., & Khalid, S. (2023). Methods for evaluating learning analytics and learning analytics dashboards in adaptive learning platforms : A systematic review. The Electronic Journal of e-Learning (EJEL), 21(3), 430–449. https://doi.org/10.34190/ejel.21.5.3088
Ulfah, M., Syamsi, A., Dewi, E. W., Luhanarky, A., & Hadiana, D. (2026). Does transformative digital learning management enhance the sustainability of inclusive islamic education ? Evidence from diaspora children in australia. Kharisma: Jurnal Administrasi Dan Manajemen Pendidikan, 5(1), 32–47. https://doi.org/10.59373/kharisma.v5i1.151
Vlahou, A., Hallinan, D., Apweiler, R., Argiles, A., Beige, J., Benigni, A., Bischoff, R., Black, P. C., Boehm, F., Céraline, J., Chrousos, G. P., Delles, C., Evenepoel, P., Fridolin, I., Glorieux, G., Gool, A. J. Van, Heidegger, I., Ioannidis, J. P. A., Jankowski, J., … Vanholder, R. (2021). Data sharing under the general data protection regulation time to harmonize law and research ethics? Antonia. Hypertension Downloaded, 22, 1029–1035. https://doi.org/10.1161/HYPERTENSIONAHA.120.16340
Wang, G., & Wang, Z. (2025). Utilising an interactive deep learning framework to enhance the effectiveness of intelligent teaching practices. Decision Making : Applications in Management and Engineering, 8(1), 238–255.
Willis, V. (2024). The role of artificial intelligence (ai) in personalizing online learning. Journal of Online and Distance Learning, 3(1), 1–13. https://doi.org/10.47941/jodl.1689
Wu, X. (2024). Heliyon unveiling the dynamics of self-regulated learning in project-based learning environments. Heliyon, 10(5), e27335. https://doi.org/10.1016/j.heliyon.2024.e27335
Yagin, F. H., Pinar, A., & Fernanded, M. S. D. S. (2024). Statistical effect sizes in sports science. Journal of Exercise Science & Physical Activity Reviews, 2(1), 164–171. Yakubu, M. A., Sain, Z. H., Lawal, U. S., Arief, S., & Permana, D. (2025). Analysis of Online Academic Writing Tools Usage among Postgraduate Students in North-west Nigeria. Kharisma: Jurnal Administrasi Dan Manajemen Pendidikan, 4(1), 1–13. https://doi.org/10.59373/kharisma.v4i1.69
Yakubu, M. A., Sain, Z. H., Lawal, U. S., Arief, S., & Permana, D. (2025). Analysis of online academic writing tools usage among postgraduate students in North-west Nigeria. Kharisma: Jurnal Administrasi Dan Manajemen Pendidikan, 4(1), 1–13. https://doi.org/10.59373/kharisma.v4i1.69
Yoon, J., Lee, H., & Son, H. (2023). Effects of an interactive coaching intervention on quality of life and psychological factors for colorectal cancer survivors : A single group pre and posttest design. European Journal of Oncology Nursing, 66(May), 102413. https://doi.org/10.1016/j.ejon.2023.102413
Zakharova, N., Frumina, S., Lobuteva, L., & Alwaely, S. (2024). The specifics of integrating distance learning technologies with traditional classroom instruction : How to design educational curricula in modern education? Heliyon, 10(20), e38740. https://doi.org/10.1016/j.heliyon.2024.e38740
Zainuddin, Z., Misbah, M., Mastuang, M., Qamariah, Q., Zaidi, M., Amiruddin, B., Farahwahidah, N., & Rahman, A. (2025). Mapping a decade of research on artificial intelligence and augmented reality in physics education: A bibliometric analysis (2016–2025). Online Learning in Educational Research, 5(2), 537–557. https://doi.org/10.58524/oler.v5i2.901
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Eko Risdianto, Joseline Santos, Noel Lomerio, Rita Sinthia, Tri Basuki Kurniawan, Deshinta Arrova Dewi, Laura Mahendratta Tjahjono, Mona Ardina, Desvi Wahyuni
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Online Learning in Educational Research is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with Online Learning in Educational Research agree to the following terms:
Copyright Retention: Authors retain the copyright of their work without any restrictions.
Publishing Rights: Authors retain the right to publish and distribute their work without any restrictions.
License Agreement: By publishing with Online Learning in Educational Research, authors agree that their work will be licensed under the Creative Commons Attribution-ShareAlike 4.0 International License (CC BY-SA). This license allows others to share and adapt the work, provided that appropriate credit is given, any changes are indicated, and the new creations are licensed under the same terms.
