AI-Enhanced Personalized Learning in Higher Education: Integrating Machine Learning With Moodle

Learning Management Systems (LMSs) have been rapidly adopted in higher education. The emergence of the COVID-19 pandemic not only disrupted normal, traditional teaching and learning but also revolutionized pedagogical approaches and learning methodologies. However, most LMS platforms, such as Moodle, utilize static content delivery methods that are not sufficiently catered to the diverse learning needs and abilities of students. The study, motivated by the results from our previous article based on a systematic literature review, aimed to design and evaluate an AI-enabled adaptive learning framework to enhance Moodle’s ability to provide personalized learning experiences. This paper used a quantitative experimental research design and a data set that contained demographic, behavioral, engagement, and academic performance data from 213,000 students. The proposed framework involved the use of Extreme Gradient Boosting (XGBoost) models for learner performance prediction and at-risk student identification, along with a hybrid recommendation system for personalized content delivery. The results showed a remarkable predictive power, with the at-risk detection model achieving 97.7% accuracy and the learner performance prediction model achieving 97.3% accuracy. The feature importance analysis suggested that the top predictors of academic outcomes included learner engagement, peer interaction, attendance, time spent on the platform, and academic skills. Moreover, the hybrid recommendation system was able to generate personalized learning resources based on learners’ performance, deficiencies, and learning preferences. The research results indicate that the combination of AI and machine learning techniques in Moodle can greatly enhance adaptive learning, learner engagement, and early intervention strategies, resulting in improved educational outcomes in higher education settings.

Adaptive learning systems; Artificial intelligence; Higher education; Machine learning

1. Adewale, O. A., Rane, N. L., Ogbonna, M. O., & Rane, J. (2026). Inclusive education through artificial intelligence: Opportunities, challenges, and ethical considerations. International Journal of Applied Resilience and Sustainability, 2(2), 455-472, https://doi.org/10.70593/deepsci.0202018 [Google Scholar] [Crossref]

2. AlBlooshi, S. (2026). Artificial intelligence in higher education, opportunities, and challenges: A review. Frontiers in Education, 10, Article 1683968. https://doi.org/10.3389/feduc.2025.1683968 [Google Scholar] [Crossref]

3. Angeioplastis, A., Konstantakis, M., Aliprantis, J., Ordoumpozanis, K., Varsamis, D., & Tsimpiris, A. (2026). AI for All: Adaptive, Accessible, and Inclusive Learning Experiences in the Age of Intelligent LMSs. Information, 17(2), 216., https://doi.org/10.3390/info17020216 [Google Scholar] [Crossref]

4. Arslan, A. (2026). The Ethical Compass of Digital Classrooms: Teachers’ Perspectives on AI Ethics in Education. Türk Akademik Yayınlar Dergisi (TAY Journal), 10(1). https://doi.org/10.29329/tayjournal.2026.1427.04 [Google Scholar] [Crossref]

5. Baimukhambetova, K., Ybyraimzhanov, K., Moldabek, K., Akhatayeva, U. B., Zhetkizgenova, A., & Uaidullakyzy, E. (2025). Evaluating the Relationship Between Pre-Service Teachers’ Artificial Intelligence Readiness and Professional Self-Efficacy. Education Sciences, 16(1), 43. https://doi.org/10.3390/educsci16010043 [Google Scholar] [Crossref]

6. Boninger, F., & Nichols, T. P. (2025). Fit for Purpose? How Today's Commercial Digital Platforms Subvert Key Goals of Public Education. National Education Policy Center, http://nepc.colorado.edu/publication/digital-platforms [Google Scholar] [Crossref]

7. Chan, C. K. Y. (2025). AI as the therapist: Student insights on the challenges of using generative AI for school mental health frameworks. Behavioral Sciences, 15(3), Article 287. https://doi.org/10.3390/bs15030287 [Google Scholar] [Crossref]

8. Dube, S, Madzore, A, Dube, S, P and Mpande, B (2026). Enhancing Personalized E-Learning Systems in Higher Education Through Artificial Intelligence: A Rapid Systematic Literature Review. , 10(2), https://doi.org/10.47772/IJRISS.2026.10200008. [Google Scholar] [Crossref]

9. Evangelista, E. D. L., & Sy, B. D. (2022). An approach for improved students’ performance prediction using homogeneous and heterogeneous ensemble methods. International Journal of Electrical and Computer Engineering, 12(5), 5226, https://doi.org/10.11591/ijece.v12i5.pp5226-5235 [Google Scholar] [Crossref]

10. Gökçe, A. T. (2026). Revolutionizing Education with AI: Ethical Considerations in K-12 Settings. IntechOpen. https://www.intechopen.com/online-first/1230172 [Google Scholar] [Crossref]

11. Gravino, C., Iannella, A., Marras, M., Pagliara, S. M., & Palomba, F. (2024). Teachers interacting with generative artificial intelligence: a dual responsibility. In CEUR Workshop Proceedings (Vol. 3762, pp. 83-88). https://ceur-ws.org/Vol-3762/579.pdf [Google Scholar] [Crossref]

12. Injadat, M., Moubayed, A., Nassif, A. B., & Shami, A. (2020). Systematic ensemble model selection approach for educational data mining. Knowledge-Based Systems, 200, 105992. [Google Scholar] [Crossref]

13. Jafarov, J. (2026). Core Quality Components in Contemporary Teacher Education Systems. TOJET: The Turkish Online Journal of Educational Technology, 25(1). https://files.eric.ed.gov/fulltext/EJ1497809.pdf [Google Scholar] [Crossref]

14. Joshi, A., Saggar, P., Jain, R., Sharma, M., Gupta, D., & Khanna, A. (2021). CatBoost—An ensemble machine learning model for prediction and classification of student academic performance. Advances in Data Science and Adaptive Analysis, 13(03n04), 2141002, https://doi.org/10.1142/S2424922X21410023 [Google Scholar] [Crossref]

15. Kyrou F, Vergis P, Rogari G, Saiti A and Manousou E (2026) Crisis management in distance education in the age of artificial intelligence: opportunities, challenges and ethical dimensions. Front. Educ. 11:1791475. doi: 10.3389/feduc.2026.1791475, https://doi.org/10.3389/feduc.2026.1791475 [Google Scholar] [Crossref]

16. Lee, H., Atif, A., & Kang, K. (2026). Analysing AI utilisation in education through learner question types: A constructivist approach. Australasian Journal of Educational Technology, 42(1), 79-96. [Google Scholar] [Crossref]

17. Long, D. Y., Wang, S., Md Rashid, S., & Lu, X. T. (2026, February). Artificial intelligence in higher education: a systematic review of its impact on student engagement and the mediating role of teaching methods. In Frontiers in Education (Vol. 10, p. 1648661). Frontiers, https://doi.org/10.3389/feduc.2025.1648661 [Google Scholar] [Crossref]

18. Marcos, L. T. A Systematic Review on Artificial Intelligence in Education: Opportunities, Challenges, and Ethical Implications. Preprints 2026, 2026010448. https://doi.org/10.20944/preprints202601.0448.v1 [Google Scholar] [Crossref]

19. Mariyono D, Nur Alif Hd A (2025), "AI’s role in transforming learning environments: a review of collaborative approaches and innovations". Quality Education for All, Vol. 2 No. 1 pp. 267–290, doi: https://doi.org/10.1108/QEA-08-2024-0071 [Google Scholar] [Crossref]

20. Muhanguzi, N. (2025). AI in Education: Navigating Fears, Embracing Possibilities, and Charting a Path Forward. RUFORUM BLOG. https://news.ruforum.org/wp-content/uploads/2025/12/Nimrod-AI-in-Education-.pdf [Google Scholar] [Crossref]

21. Murillo-Jiménez, H., Centeno-Alarcón, M., Buele, J., & Yumbla, F. (2025). Analyzing barriers to the effective implementation of technological tools in inclusive education: a scoping review. In Frontiers in Education (Vol. 10, p. 1687664). Frontiers Media SA, https://doi.org/10.3389/feduc.2025.1687664 [Google Scholar] [Crossref]

22. Nai, R., Zheng, J., Wang, M., & Yang, Y. (2024). Leveraging process mining and XGBoost for predictive learning analytics based on student web interactions. International Journal of Distance Education Technologies, 22(1), 1–18. https://doi.org/10.4018/IJDET.346740 [Google Scholar] [Crossref]

23. Nassreddine, G., Saleh, L., Al Majzoub, M., & El Arid, A. (2025, July). SHAP Explainability: An Ensemble Learning Approach for Student Performance Prediction. In 2025 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT) (pp. 432-438). IEEE, https://ieeexplore.ieee.org/document/11100707 [Google Scholar] [Crossref]

24. Ncube. (2023). The impact of artificial intelligence on human resource management practices: An investigation. SA Journal of Human Resource Management. https://doi.org/10.4102/sajhrm.v21i0.2960 [Google Scholar] [Crossref]

25. Niyazova, G. Z., Duisekeyeva, B. M., Berdi, D. K., Usembayeva, I. B., & Mindetbayeva, A. A. (2026, April). The effects of gamified AI-supported digital learning environments on personalized learning and student engagement in school education: a systematic review and meta-analysis. In Frontiers in Education (Vol. 11, p. 1754080). Frontiers Media SA, https://doi.org/10.3389/feduc.2026.1754080 [Google Scholar] [Crossref]

26. Ntorukiri, T. B., Kirugua, J. M., & Kirimi, F. (2022). Policy and infrastructure challenges influencing ICT implementation in universities: a literature review. Discover Education, 1(1), 19, https://doi.org/10.1007/s44217-022-00019-6 [Google Scholar] [Crossref]

27. Ordaya-Gonzales, K., Cortez Restuccia, J. C., Cossio Bolaños, W. J., & Arriola-Montenegro, J. (2024). From crisis to connectivity: Exploring the role of information and communication technologies in medical education during the COVID-19 pandemic. Cureus, 16(5), Article e60302. https://doi.org/10.7759/cureus.60302 [Google Scholar] [Crossref]

28. Qiu, X., & Qiu, N. (2026). Cognitive load and pedagogical tension in multi-platform online learning: Evidence from Chinese higher education. PloS one, 21(4), e0347566. [Google Scholar] [Crossref]

29. Ramroop, N., & Reddy, K. (2026). Challenges in the use of e-learning technologies for teaching and learning at universities of technology. African Journal of Inter/Multidisciplinary Studies, 7(2), Article 2656. https://doi.org/10.51415/ajims.v7i2.2656 [Google Scholar] [Crossref]

30. Saini, B. K. (2025). Optimizing student academic performance prediction using heterogeneous ensemble learning. European Journal of Artificial Intelligence and Machine Learning, 4(4), 1-6, https://pmc.ncbi.nlm.nih.gov/articles/PMC13098912/ [Google Scholar] [Crossref]

31. Sangwa, S., Murungu, E., Iradukunda, A., Umutoni, J., Hackman, C., Ssekiziyivu, N., ... & Lugero, T. (2026). Toward a Systems-Level Theory of AI-Experiential Learning Orchestration (AIELO). Open Research Africa, 9, 9, https://doi.org/10.12688/openresafrica.14652.1 [Google Scholar] [Crossref]

32. Sofianos, K. C., Kaponis, A., Stefanidakis, M., Maragoudakis, M., Pappas, T., & Bukauskas, L. (2026). Artificial Intelligence and Moodle: Advancing Intelligent Learning and Digital Marketing in Education. Engineering, Technology & Applied Science Research, 16(2), 32978-32988, https://doi.org/10.48084/etasr.13756 [Google Scholar] [Crossref]

33. Tunduny T, Shibwabo B. Explainable AI Approaches in Federated Learning: Systematic Review. JMIR AI. 2026 Feb 3;5:e69985. doi: 10.2196/69985. PMID: 41632959; PMCID: PMC12914235, https://doi.org/10.2196/69985 [Google Scholar] [Crossref]

34. Villegas-Ch W, Palacios P, Jaramillo-Alcázar A and Guevara V (2026). Modeling informal learning as a dynamic interaction process in digital learning environments. Front. Educ. 11:1776945. doi: 10.3389/feduc.2026.1776945Wang, Y., Zhang, Z. J., & Zhou, H. (2026). Artificial Intelligence in Language Learning: A Twenty-Year Scoping Review of Applications, Research Methods, and Outcomes. Research Synthesis in Applied Linguistics, 1-38, https://doi.org/10.1080/29984475.2026.2647961 [Google Scholar] [Crossref]

35. Yermaganbetova M, Ashimbekova A, Kaibassova D, Akhitova R and Dyussembina E (2026) Evaluating the impact of an AI-integrated learning platform on student performance: a quasi-experimental study. Front. Educ. 11:1792353. doi: 10.3389/feduc.2026.1792353, https://doi.org/10.3389/feduc.2026.1792353 [Google Scholar] [Crossref]

36. Zaid, T., & Garai, S. (2024). Emerging trends in cybersecurity: a holistic view on current threats, assessing solutions, and pioneering new frontiers. Blockchain in healthcare today, 7, 10-30953, https://doi.org/10.30953/bhty.v7.302 [Google Scholar] [Crossref]

37. Zakiah, D., Kurniadi, B., Wahyunanda, I., Sijabat, P. S., Utama, Y. P., & Ahmadi, P. V. (2025). Technological Advancements in Maritime Education: A Collaborative Approach. Jurnal Penelitian Sekolah Tinggi Transportasi Darat, 16(1), 97-107, http://jurnal.ptdisttd.net/ [Google Scholar] [Crossref]

38. Zhao, S., Zhou, D., Wang, H., Chen, D., & Yu, L. (2025). Enhancing Student Academic Success Prediction Through Ensemble Learning and Image-Based Behavioral Data Transformation. Applied Sciences, 15(3), 1231. https://doi.org/10.3390/app15031231 [Google Scholar] [Crossref]

39. Zhou, Q., Hashim, H., & Sulaiman, N. (2025). Integrating AI chatbots in informal digital English learning: Impacts on listening competencies in Chinese higher education. Education and Information Technologies, 30(18), 27031–27059. https://doi.org/10.1007/s10639-025-13811-2 [Google Scholar] [Crossref]

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