Machine Learning for Personalized Learning Systems
DOI:
https://doi.org/10.63665/0pcg1863Keywords:
Machine Learning, Personalized Learning, Adaptive Education, Knowledge Tracing, Educational Data MiningAbstract
Machine Learning (ML) integration into the sphere of educational technologies has transformed the delivery,
pace, and evaluation of the content to a single learner. This paper explores how ML-driven personalized
learning systems (PLS) can be used to improve student engagement, performance prediction, and adaptive
content recommendation. The two main goals are to compare the relative accuracy of supervised ML algorithms
employed in PLS and to analyze quantifiable effects on the performance of learners. The study is based on a
secondary quantitative methodology to synthesise empirical data regarding 18 peer-reviewed studies on the
topic (2020-2025) on the topics of ASSISTments, OULAD, Coursera, and Udemy and controlled experiments on
Indian and global student groups. The hypothesis was that the ensemble and deep learning models are more
accurate and personalized compared to classical algorithms. Findings attest to the consistent 86 to 96 percent
accuracy of Random Forest, SVM (hyperparameter tuning) and Deep Knowledge Tracing (DKT) variants, and
post-test improvements of 15.8 to 24% and engagement improvements of 13 to 20 percent of AI-personalized
cohorts compared to control groups. These gains are discussed in the context of Indian K-12 and highereducation,
covering the areas of scalability, equity, and pedagogical alignment. The conclusion of the paper is
that ML-based PLS provide statistically significant learning advantages but should be trained by teachers and
ethically secured to be deployed sustainably.
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References
1. Ahmed, E., & Esmael, A. F. (2024). Student
performance prediction using machine learning
algorithms. Applied Computational
Intelligence and Soft Computing, 2024, Article
4067721.
https://doi.org/10.1155/2024/4067721
2. Akpen, C. N., Asaolu, S., Atobatele, S.,
Okagbue, H., & Sampson, S. (2024). Impact of
online learning on student's performance and
engagement: A systematic review. Discover
Education, 3(1), 205.
https://doi.org/10.1007/s44217-024-00253-0
3. Ali, W. A., Manasa, K. N., Bendechache, M.,
Aljunaid, M. F., & Sandhya, P. (2025). A
collaborative filtering recommender system:
Survey. Neurocomputing, 617, 128718.
https://doi.org/10.1016/j.neucom.2024.128718
4. Cu, T. A., & Hong Quan, V. D. (2024).
Increment of academic performance prediction
of at-risk student by dealing with data
imbalance problem. Applied Computational
Intelligence and Soft Computing, 2024, Article
4795606.
https://doi.org/10.1155/2024/4795606
5. Essa, S. G., Çelik, T., & Human-Hendricks, N.
E. (2023). Personalized adaptive learning
technologies based on machine learning
techniques to identify learning styles: A
systematic literature review. IEEE Access, 11,
48392–48409.
https://doi.org/10.1109/ACCESS.2023.327643
9
6. Gerlich, M. (2025). AI tools in society:
Impacts on cognitive offloading and the future
of critical thinking. Societies, 15(1), 6.
https://doi.org/10.3390/soc15010006
7. InsightAce Analytic. (2025). AI in
personalized learning and education
technology market report 2025–2034.
https://www.insightaceanalytic.com/report/aiin-
personalized-learning-and-educationtechnology-
market/2692
8. Jayachandran, A., & Joshi, A. (2024).
Improved support vector machine for
predicting post-graduation employability of
engineering students. Information Technology, 16(5), 3245–3256.
https://doi.org/10.1007/s41870-024-01839-5
9. Laak, K.-J., & Aru, J. (2025). AI and
personalized learning: Bridging the gap with
modern educational goals. Computers and
Education: Artificial Intelligence, 8, 100348.
https://doi.org/10.1016/j.caeai.2025.100348
10. Liu, K. (2025). Artificial intelligence-based
personalized learning in education: A
systematic literature review. Discover
Artificial Intelligence, 5(1), 198.
https://doi.org/10.1007/s44163-025-00598-x
11. Lyu, L., Wang, Z., Yun, H., Yang, Z., & Li, Y.
(2025). Research on personalized distance
education recommendation system based on
deep learning. Scientific Reports, 15(1), Article
26020. https://doi.org/10.1038/s41598-025-
26020-1
12. Lyu, L., Wang, Z., Yun, H., Yang, Z., & Li, Y.
(2023). DKT-STDRL: Spatial and temporal
representation learning enhanced deep
knowledge tracing for learning performance
prediction. arXiv.
https://arxiv.org/abs/2302.11569
13. Ma, W., Adesope, O. O., Nesbit, J. C., & Liu,
Q. (2014). Intelligent tutoring systems and
learning outcomes: A meta-analysis. Journal
of Educational Psychology, 106(4), 901–918.
https://doi.org/10.1037/a0037123
14. Manjushree, A. P., Aishwarya, K. M.,
Bhavyashree, B. S., & Lalitha, S. (2021). A
comparative analysis of machine learning
algorithms for student employability
prediction. Procedia Computer Science, 215,
422–431.
https://doi.org/10.1016/j.procs.2022.12.044
15. Patil, S., & Rao, N. (2024). The impact of AIdriven
personalized learning on mathematics
achievement and student engagement in rural
vs. urban schools in Karnataka, India.
International Journal of Innovative Research
and Scientific Studies, 7(3), 1102–1115.
https://doi.org/10.53894/ijirss.v7i3.3037
16. Pelánek, R. (2025). Do intelligent tutoring
systems benefit K-12 students? A metaanalysis
and evaluation of heterogeneity of
treatment effects in the U.S. arXiv.
https://arxiv.org/abs/2511.04997
17. Piech, C., Bassen, J., Huang, J., Ganguli, S.,
Sahami, M., Guibas, L. J., & Sohl-Dickstein, J.
(2015). Deep knowledge tracing. Advances in
Neural Information Processing Systems, 28,
505–513.
https://papers.nips.cc/paper/2015/hash/bac916
2b47c56fc8a4d2a519803d51b3-Abstract.html
18. Precedence Research. (2025). Hyperpersonalized
learning market size, report by
2034.
https://www.precedenceresearch.com/hyperpersonalized-
learning-market
19. Roy, D., & Dutta, M. (2022). A systematic
review and research perspective on
recommender systems. Journal of Big Data,
9(1), 59. https://doi.org/10.1186/s40537-022-
00592-5
20. Shemshack, A., & Spector, J. M. (2020). A
systematic literature review of personalized
learning terms. Smart Learning Environments,
7(1), Article 33.
https://doi.org/10.1186/s40561-020-00140-9
21. Steenbergen-Hu, S., & Cooper, H. (2014). A
meta-analysis of the effectiveness of intelligent
tutoring systems on college students' academic
learning. Journal of Educational Psychology,
106(2), 331–347.
https://doi.org/10.1037/a0034752
22. Strielkowski, W., Grebennikova, V.,
Lisovskiy, A., Rakhimova, G., & Vasilev, V.
(2025). AI-driven adaptive learning for
sustainable educational transformation.
Sustainable Development, 33(2), 1921–1947.
https://doi.org/10.1002/sd.3221
23. Thesen, T., & Park, S. H. (2025). Retrievalaugmented
generation for trustworthy AI
teaching assistants in medical education. npj
Digital Medicine, 8, 612.
https://doi.org/10.1038/s41746-025-01612-2
24. Wang, S., Christensen, C., Cui, W., Tong, R.,
Yarnall, L., Shear, L., & Feng, M. (2024).
When adaptive learning is effective learning:
Comparison of an adaptive learning system to
alternate conditions. Interactive Learning
Environments, 32(5), 1944–1956.
https://doi.org/10.1080/10494820.2022.206100
6
25. Wang, J., Li, Y., & Zhao, X. (2025). Artificial
intelligence in adaptive education: A
systematic review of techniques for
personalized learning. Discover Education,
4(1), 312. https://doi.org/10.1007/s44217-025-
00908-6
26. Yağcı, M. (2025). Using machine learning to
predict student outcomes for early intervention
and formative assessment. Scientific Reports,
15(1), Article 23409.
https://doi.org/10.1038/s41598-025-23409-w
27. Yanes, N., Mostafa, A. M., Ezz, M., &
Almuayqil, S. N. (2020). A machine learningbased
recommender system for improving
students learning experiences. IEEE Access, 8,
201218–201235.
https://doi.org/10.1109/ACCESS.2020.303633
6
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