Mapping Artificial Intelligence for Personalised Learning based on Learning Style: Scopus-Based Bibliometric Analysis (2006-2026)
DOI:
https://doi.org/10.63158/journalisi.v8i5.1752Keywords:
Learning Styles, Personalized Learning, AI in education, bibliometric analysis, ScopusAbstract
The aims of this study is to conduct a bibliometric analysis of the development of learning style-based personalisation. The rapid advancement of AI-based learning personalisation has been accompanied by growing research attention to the role of learning style in the modern learning ecosystem. The research methodology involves a bibliometric analysis of 604 Scopus-indexed publications from the period 2006 to 2026 that were analyzed and visualized using Biblioshiny, VOSviewer and Open Refine. The results of the study indicate a significant increase in publications beginning in 2020, reflecting growing research attention to AI-based personalisation., the keywords patterns indicate an increasing association between learning style and machine learning analytics. Random Forest is one of the most commonly reported machine learning methods in the literature to identify learning styles. The findings further indicate that AI and learning style are increasingly discussed with the same research landscape. This study contributes by mapping the evolution of this field and demonstrating the convergence of educational theory and computational approaches. Further research should be extended beyond the Scopus database, combining bibliometric analysis and systematic literature review, and investigating the integration of learning styles with learner behavioral and cognitive data in AI-powered personalised learning systems.
Downloads
References
[1] M. Alshammari, R. Anane, and R. J. Hendle, “An E-Learning Investigation into Learning Style Adaptivity,” in 2015 48th Hawaii Int. Conf. on System Sciences, Hawaii: IEEE, Jan. 2015, pp. 11–20. doi: 10.1109/HICSS.2015.13.
[2] Q. Cai and Q. Liu, “Exploration and Practice of Personalized Education based on Adaptive Learning Systems,” in ACM Int. Conf. Proc. Series, Association for Computing Machinery, Nov. 2024, pp. 54–58. doi: 10.1145/3700297.3700307.
[3] M. T. Alshammari and A. Qtaish, “Effective adaptive e-learning systems according to learning style and knowledge level,” J. of Inf. Tech. Edu: Research, vol. 18, pp. 529–547, 2019, doi: 10.28945/4459.
[4] M. Alshammari, R. Anane, and R. J. Hendley, “Adaptivity in E-Learning Systems,” in The Eighth Int. Conf. on Complex, Intelligent, and Software Intensive Systems, Birmingham, United Kingdom, Jul. 2014.
[5] M. Alshammari, R. Anane, and R. J. Hendley, “The impact of learning style adaptivity in teaching computer security,” in Annual ITiCSE, Assoc. for Computing Machinery, Jun. 2015, pp. 135–140. doi: 10.1145/2729094.2742614.
[6] M. Alshammari, R. Anane, and R. J. Hendley, “Students’ Satisfaction in Learning Style-Based Adaptation,” in 2015 IEEE 15th Int. Conf. on Advanced Learning Technologies, United Kingdom: IEEE, Jul. 2015, pp. 55–57. doi: 10.1109/ICALT.2015.56.
[7] S. G. Essa, T. Celik, and N. E. Human-Hendricks, “Personalized Adaptive Learning Technologies Based on Machine Learning Techniques to Identify Learning Styles: A Systematic Literature Review,” IEEE Access, vol. 11, pp. 48392–48409, 2023, doi: 10.1109/ACCESS.2023.3276439.
[8] M. F. Begam and G. Ganapathy, “Personalized learning management system using semantic web based learning style detection,” J. of Chemical and Pharmaceutical Sciences, vol. 9, no. 4, Oct. 2016, [Online]. Available: www.jchps.com
[9] M. Khanian Najafabadi, K. Kritharides, C. Choi, S. Shojae Chaeikar, and H. Salarian, “Bridging the Implementation Gap in AI-Powered Personalized Education: A Systematic Review of Learning Style Prediction and Recommendation Systems,” Feb. 01, 2026, MDPI. doi: 10.3390/ai7020041.
[10] E. du Plooy, D. Casteleijn, and D. Franzsen, “Personalized adaptive learning in higher education: A scoping review of key characteristics and impact on academic performance and engagement,” Heliyon, vol. 10, no. 21, Nov. 2024.
[11] F. Abomelha and P. Newbury, “A VARK Learning Style-Based Recommendation System for Adaptive E-Learning – Pilot Study,” in 2025 ICETC, IEEE, Sep. 2025, pp. 268–276. doi: 10.1109/ICETC66579.2025.11387272.
[12] M. Normann, J. Haug, Y. Valencia, J. Abke, and G. Hagel, “Adaptive Learning Path Sequencing Based on Learning Styles within N-dimensional Spaces,” in ACM Int. Conf. Proc. Series, Assoc. for Computing Machinery, Jun. 2023, pp. 56–64. doi: 10.1145/3593663.3593676.
[13] K. J. Laak and J. Aru, “AI and Personalized Learning: Bridging The Gap With Modern Educational Goals,” J. Ed. Tech. and Society, vol. 28, pp. 133–50, 2025, doi: https://doi.org/10.48550/arXiv.2404.02798.
[14] S. Saleng, M. Yaumi, and Sukmawati, “Personalized Learning and Adaptive Systems in Education: A Comprehensive Bibliometric Analysis,” Int. J. of Learning, Teaching and Educational Research, vol. 25, no. 1, pp. 29–59, Jan. 2026, doi: 10.26803/ijlter.25.1.2.
[15] K. C. Li and B. T. M. Wong, “Artificial intelligence in personalised learning: a bibliometric analysis,” Interactive Technology and Smart Education, vol. 20, no. 3, pp. 422–445, Sep. 2023, doi: 10.1108/ITSE-01-2023-0007.
[16] H. H. Nguyen and V. A. Nguyen, “Personalized Learning in the Online Learning from 2011 to 2021: A Bibliometric Analysis,” Int. J. of Inf. and Edu. Tech., vol. 13, no. 8, pp. 1261–1272, Aug. 2023, doi: 10.18178/ijiet.2023.13.8.1928.
[17] N. S. Raj and V. G. Renumol, “A systematic literature review on adaptive content recommenders in personalized learning environments from 2015 to 2020,” J. of Computers in Education, vol. 9, no. 1, pp. 113–148, Mar. 2022, doi: 10.1007/s40692-021-00199-4.
[18] R. I. Fariani, K. Junus, and H. B. Santoso, “A Systematic Literature Review on Personalised Learning in the Higher Education Context,” Technology, Knowledge and Learning, vol. 28, no. 2, pp. 449–476, Jun. 2023, doi: 10.1007/s10758-022-09628-4.
[19] I. Adam, T. P. N. T. Shariman, and Z. Siran, “Development and Efficacy of Adaptive Personalised Learning Environments: A Systematic Review and Meta-analysis,” Pak. J. Life Soc. Sci., vol. 22, no. 2, pp. 7223–7244, 2024, doi: 10.57239/PJLSS-2024-22.2.00546.
[20] S. Akash, V. Dinesh, S. Muthamil Selvan, and R. Srikanth, “Systematic Review on Real-Time Students Behavior Monitoring using Machine Learning,” in 6th ICICT 2023 - Proceedings, IEEE., 2023, pp. 233–237. doi: 10.1109/ICICT57646.2023.10134519.
[21] J. Melesko and E. Kurilovas, “Semantic technologies in e-learning: Learning analytics and artificial neural networks in personalised learning systems,” in ACM Int. Conf. Proc. Series, Association for Computing Machinery, Jun. 2018. doi: 10.1145/3227609.3227669.
[22] F. Amastini, D. Kinanti, S. Sekarhati, and M. Puspitasari, “Advancing Adaptive and Personalized E-Learning Systems: A Systematic Literature Review,” J. ASSET. vol. 7, no. 2, 2025, doi: 10.26877/asset.v7i2.1117.
[23] A. L. Kalua, M. A. Wibowo, and L. A. Latumakulita, “Mapping the Global Landscape of Electronic Supply Chain Management (e-SCM): A Bibliometric and Visual Analysis,” J. of Information Systems and Informatics, vol. 8, no. 2, pp. 2480–2493, Apr. 2026, doi: 10.63158/journalisi.v8i2.1525.
[24] M. Aria and C. Cuccurullo, “bibliometrix: An R-tool for comprehensive science mapping analysis,” J. Informetr., vol. 11, no. 4, pp. 959–975, Nov. 2017, doi: 10.1016/j.joi.2017.08.007.
[25] N. J. van Eck and L. Waltman, “Software survey: VOSviewer, a computer program for bibliometric mapping,” Scientometrics, vol. 84, no. 2, pp. 523–538, 2010, doi: 10.1007/s11192-009-0146-3.
[26] Y. Ikawati, M. U. H. Al Rasyid, and I. Winarno, “Student Behavior Analysis to Predict Learning Styles Based Felder Silverman Model Using Ensemble Tree Method,” EMITTER Int. J. of Engineering Technology, vol. 9, no. 1, pp. 92–106, Mar. 2021, doi: 10.24003/emitter.v9i1.590.
[27] A. Angeioplastis, J. Aliprantis, M. Konstantakis, D. Varsamis, and A. Tsimpiris, “The Learning Style Decoder: FSLSM-Guided Behavior Mapping Meets Deep Neural Prediction in LMS Settings,” Computers MDPI, vol. 14, no. 9, pp. 1–29, Sep. 2025, doi: 10.3390/computers14090377.
[28] B. Zhang, Y. Shi, L. Hou, Z. Yin, and C. Chai, “TSMG: A Deep Learning Framework for Recognizing Human Learning Style Using EEG Signals,” Brain Sci., vol. 11, no. 11, p. 1397, Oct. 2021, doi: 10.3390/brainsci11111397.
[29] F. L. Gambo, G. M. Wajiga, L. Shuib, E. J. Garba, A. A. Abdullahi, and D. B. Bisandu, “Performance Comparison of Convolutional and Multiclass Neural Network for Learning Style Detection from Facial Images,” EAI Endorsed Transactions on Scalable Information Systems, vol. 9, no. 35, 2022, doi: 10.4108/eai.20-10-2021.171549.
[30] F. Rasheed and A. Wahid, “Learning style detection in E-learning systems using machine learning techniques,” Expert Syst. Appl., vol. 174, Jul. 2021, doi: 10.1016/j.eswa.2021.114774.
[31] S. Ruan and K. Lu, “Adaptive deep reinforcement learning for personalized learning pathways: A multimodal data-driven approach with real-time feedback optimization,” Comp. and Edu.: AI vol. 9, Dec. 2025, doi: 10.1016/j.caeai.2025.100463.
[32] F. Ma, C. Zhu, P. Lei, and P. Yuan, “Enhanced Learning Behaviors and Ability Knowledge Tracing,” Applied Sciences (Switzerland), vol. 15, no. 2, Jan. 2025, doi: 10.3390/app15020883.
[33] C. Agbonkhese, O. Gambo, T. Akande, I. Gambo, and A. Adewuyi, “Design and Implementation of an AI-Driven Study Assistant Mobile Application for Personalized Learning and Academic Management,” in ICT for Intelligent Systems, J. Choudrie,: Springer Nature Singapore, 2026, pp. 615–634.
[34] E. Kasneci et al., “ChatGPT for good? On opportunities and challenges of large language models for education,” Learn. Individ. Differ., vol. 103, p. 102274, 2023, doi: https://doi.org/10.1016/j.lindif.2023.102274.
[35] M. Gligora Marković, N. Kadoić, and B. Kovačić, “A Novel ANP-DEMATEL Framework for Multi-Criteria Decision-Making in Adaptive E-Learning Systems,” Mathematics, vol. 13, no. 22, Nov. 2025, doi: 10.3390/math13223714.
[36] G.-J. Hwang and S. J. H. Yang, “Development of an Adaptive Learning System with Multiple Perspectives based on Students’ Learning Styles and Cognitive Styles,” Edu. Tech. and Soc., vol. 14, no. 4, pp. 185–200, Nov. 2013.
[37] G. Vrettas and M. Sanderson, “Conferences versus journals in computer science,” J. Assoc. Inf. Sci. Technol., vol. 66, no. 12, pp. 2674–2684, Dec. 2015, doi: https://doi-org.proxy.undip.ac.id/10.1002/asi.23349.
[38] N. Donthu, S. Kumar, D. Mukherjee, N. Pandey, and W. M. Lim, “How to conduct a bibliometric analysis: An overview and guidelines,” J. Bus. Res., vol. 133, pp. 285–296, Sep. 2021, doi: 10.1016/J.JBUSRES.2021.04.070.
[39] S. Graf, Q. Zhang, P. Maguire, and V. Shtern, “An Architecture For Dynamic Student Modelling of Learning Style In Learning System and Its Application For Adaptivity,” in IADIS Int. Conf. on Cognition and Exploratory Learning in Digital Age, 2010, pp. 103–110.
[40] D. Bittner, V. K. Nadimpalli, L. Grabinger, T. Ezer, F. Hauser, and J. H. Mottok, “Uncovering Learning Styles through Eye Tracking and Artificial Intelligence,” in J. ETRA, Jun. 2024. doi: 10.1145/3649902.3653940.
[41] P. A. Fernando and H. K. S. Premadasa, “Game-based Activity Design in Primary School Students’ Learning Style Detection,” in Proc. Comp. Sci., Elsevier B.V., 2024, pp. 356–363. doi: 10.1016/j.procs.2024.06.182.
[42] S. T. Sianturi and U. L. Yuhana, “Student Behaviour Analysis To Detect Learning Styles Using Decision Tree, Naïve Bayes, And K-Nearest Neighbor Method In Moodle Learning Management System,” IPTEK J. for Technology and Science, vol. 33, no. 2, p. 94, Aug. 2022, doi: 10.12962/j20882033.v33i2.13665.
[43] M. S. Hasibuan, “A Proposed Model for Detecting Learning Styles Based on the Felder-Silverman Model Using KNN and LR with Electroencephalography (EEG),” J. of Applied Data Sciences, vol. 6, no. 2, pp. 1129–1139, May 2025, doi: 10.47738/jads.v6i2.659.
[44] J. P. B. Saputra, H. Prabowo, F. Lumban Gaol, and G. Fatwanto Hertono, “Identifying Student Learning Styles using Support Vector Machine in The Felder-Silverman Learning Style Model,” in 2023 15th IIAI-AAI-Winter, IEEE, Dec. 2023, pp. 149–154. doi: 10.1109/IIAI-AAI-Winter61682.2023.00036.
[45] C. Lwande, L. Muchemi, and R. Oboko, “Behaviour Prediction in a Learning Management System,” in 2019 IST-Africa, IEEE, May 2019, pp. 1–10. doi: 10.23919/ISTAFRICA.2019.8764877.
[46] B. Arsovic and N. Stefanovic, “E-learning based on the adaptive learning model: case study in Serbia,” J. Indian Academy of Science, vol. 266, no. 45, pp. 1–13, 2020, doi: 10.1007/s12046-020-01499-8S.
[47] Y. P. Valencia Usme, M. Normann, I. Sapsai, J. Abke, A. Madsen, and G. Weidl, “Learning Style Classification by Using Bayesian Networks Based on the Index of Learning Style,” in ACM Int. Conf. Proc. Series, Jun. 2023, pp. 73–82. doi: 10.1145/3593663.3593685.
[48] H. A. Sianipar, U. A. Chaeruman, I. Tarjiah, and B. R. Suteja, “Perancangan Index Learning Style untuk Pengembangan Personalisasi Learning Management System berbasis Moodle,” J. Teknik Informatika dan Sistem Informasi, vol. 11, no. 1, pp. 132–146, Apr. 2025, doi: 10.28932/jutisi.v11i1.10418.
[49] A. Rashad Sayed, M. Helmy Khafagy, M. Ali, and M. Hussien Mohamed, “Predict student learning styles and suitable assessment methods using click stream,” J. Egyptian Informatics. vol. 26, Jun. 2024, doi: 10.1016/j.eij.2024.100469.
[50] H. Tang and Y. Bao, “Self-regulated learner profiles in MOOCs: A cluster analysis based on the item response theory,” Interactive Learning Environments, vol. 32, pp. 1–17, Oct. 2022, doi: 10.1080/10494820.2022.2129394.
[51] J. Wang, H. Xie, O. T. S. Au, D. Zou, and F. L. Wang, “Attention-Based CNN for Personalized Course Recommendations for MOOC Learners,” ISET 2020, IEEE Inc., Aug. 2020, pp. 180–184. doi: 10.1109/ISET49818.2020.00047.
[52] H. Zhang et al., “A learning style classification approach based on deep belief network for large-scale online education,” J. of Cloud Computing, vol. 9, no. 1, Dec. 2020, doi: 10.1186/s13677-020-00165-y.
[53] Imamah, U. L. Yuhana, A. Djunaidy, and M. H. Purnomo, “Enhancing students performance through dynamic personalized learning path using ant colony and item response theory (ACOIRT),” Comp. and Edu: AI, vol. 7, Dec. 2024, doi: 10.1016/j.caeai.2024.100280.
[54] B. Zhang, Y. Shi, L. Hou, Z. Yin, and C. Chai, “Tsmg: A deep learning framework for recognizing human learning style using eeg signals,” Brain Sci., vol. 11, no. 11, Nov. 2021, doi: 10.3390/brainsci11111397.
[55] R. Yuvaraj et al., “A Machine Learning Framework for Classroom EEG Recording Classification: Unveiling Learning-Style Patterns,” Algorithms, vol. 17, no. 11, Nov. 2024, doi: 10.3390/a17110503.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Journal of Information Systems and Informatics
This work is licensed under a Creative Commons Attribution 4.0 International License.
Author's Declaration
- The Authors certify that they have read, understood, and agreed to the Journal of Information Systems and Informatics (JournalISI) submission guidelines, policies, and submission declaration. The submission has been prepared using the provided template.
- The Authors certify that all authors have approved the publication of this manuscript and that there is no conflict of interest.
- The Authors confirm that the manuscript is their original work, has not received prior publication, is not under consideration for publication elsewhere, and has not been previously published.
- The Authors confirm that all authors listed on the title page have contributed significantly to the work, have read the manuscript, attest to the validity and legitimacy of the data and its interpretation, and agree to its submission.
- The Authors confirm that the manuscript is not copied from or plagiarized from any other published work.
- The Authors declare that the manuscript will not be submitted for publication in any other journal or magazine until a decision is made by the journal editors.
- If the manuscript is finally accepted for publication, the Authors confirm that they will either proceed with publication immediately or withdraw the manuscript in accordance with the journal’s withdrawal policies.
- The Authors agree that, upon publication of the manuscript in this journal, they transfer copyright or assign exclusive rights to the publisher, including commercial rights
