Trends and challenges in personalizing learning content for students using artificial intelligence

DOI: https://doi.org/10.3846/ntcs.2024.23699

Abstract

Artificial intelligence-based solutions are widely used in different areas. With the advent of chatGPT, education has faced many challenges, such as student cheating by generating text in various practical and assessment tasks. As the number of large language models increases, it becomes difficult to control their use, and their capabilities increase over time as well. However, large language models do not only provide a negative aspect, but when used properly, they can be applied to useful and meaningful solutions. One of these is the personalization of learning, which would help to direct learning to the right needs without much human intervention, for example, when there are certain difficulties, knowledge gaps or lack of motivation. This manuscript, using a systematic analysis of the scientific literature, reviews technological solutions that are already currently used in personalizing learning. It also reviews the latest trends and challenges that would allow this area to be raised to a higher level.

First published online 09 May 2025

Keywords:

personalized learning, educational content, students, artificial intelligence, NLP, LLMs

How to Cite

Blaževičiūtė, L., Matyjaškoit, N., Leščinskij, R., & Stefanovič, P. (2024). Trends and challenges in personalizing learning content for students using artificial intelligence. New Trends in Computer Sciences, 2(2), 117–127. https://doi.org/10.3846/ntcs.2024.23699

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December 31, 2024
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References

Abas, M. A., Arumugam, S. E., Yunus, M. M., & Rafiq, K. R. M. (2023). ChatGPT and personalized learning: Opportunities and challenges in higher education. International Journal of Academic Research in Business and Social Sciences, 13(12), 3536–3545. https://doi.org/10.6007/IJARBSS/v13-i12/20240> https://doi.org/10.6007/IJARBSS/v13-i12/20240

Alasadi, E. A., & Baiz, C. R. (2023). Generative AI in education and research: Opportunities, concerns, and solutions. Journal of Chemical Education, 100(8), 2965–2971. https://doi.org/10.1021/acs.jchemed.3c00323> https://doi.org/10.1021/acs.jchemed.3c00323

Alruwais, N., & Zakariah, M. (2023). Evaluating student knowledge assessment using machine learning techniques. Sustainability, 15(7), Article 6229. https://doi.org/10.3390/su15076229> https://doi.org/10.3390/su15076229

Brown, G. T. (2022, November). The past, present and future of educational assessment: A transdisciplinary perspective. Frontiers in Education, 7, Article 1060633. https://doi.org/10.3389/feduc.2022.1060633> https://doi.org/10.3389/feduc.2022.1060633

Chang, W. L., & Sun, J. C. Y. (2024). Evaluating AI’s impact on self-regulated language learning: A systematic review. System, 126, Article 103484. https://doi.org/10.1016/j.system.2024.103484> https://doi.org/10.1016/j.system.2024.103484

Diwan, C., Srinivasa, S., Suri, G., Agarwal, S., & Ram, P. (2023). AI-based learning content generation and learning pathway augmentation to increase learner engagement. Computers and Education: Artificial Intelligence, 4, Article 100110. https://doi.org/10.1016/j.caeai.2022.100110> https://doi.org/10.1016/j.caeai.2022.100110

El-Sabagh, H. A. (2021). Adaptive e-learning environment based on learning styles and its impact on development students’ engagement. International Journal of Educational Technology in Higher Education, 18(1), Article 53. https://doi.org/10.1186/s41239-021-00289-4> https://doi.org/10.1186/s41239-021-00289-4

Ezzaim, A., Dahbi, A., Aqqal, A., & Haidine, A. (2024). AI-based learning style detection in adaptive learning systems: a systematic literature review. Journal of Computers in Education. https://doi.org/10.1007/s40692-024-00328-9> https://doi.org/10.1007/s40692-024-00328-9

Fahad Mon, B., Wasfi, A., Hayajneh, M., Slim, A., & Abu Ali, N. (2023). Reinforcement learning in education: A literature review. Informatics, 10(3), Article 74. https://doi.org/10.3390/informatics10030074> https://doi.org/10.3390/informatics10030074

Gao, X., Li, P., Shen, J., & Sun, H. (2020). Reviewing assessment of student learning in interdisciplinary STEM education. International Journal of STEM Education, 7, Article 24. https://doi.org/10.1186/s40594-020-00225-4> https://doi.org/10.1186/s40594-020-00225-4

Guizani, S., Mazhar, T., Shahzad, T., Ahmad, W., Bibi, A., & Hamam, H. (2025). A systematic literature review to implement large language model in higher education: Issues and solutions. Discover Education, 4, Article 35. https://doi.org/10.1007/s44217-025-00424-7> https://doi.org/10.1007/s44217-025-00424-7

Ipinnaiye, O., & Risquez, A. (2024). Exploring adaptive learning, learner-content interaction and student performance in undergraduate economics classes. Computers & Education, 215, Article 105047. https://doi.org/10.1016/j.compedu.2024.105047> https://doi.org/10.1016/j.compedu.2024.105047

Jain, D. K., Neelakandan, S., Vidyarthi, A., Mishra, A., & Alkhayyat, A. (2025). A knowledge-Aware NLP-Driven conversational model to detect deceptive contents on social media posts. Computer Speech & Language, 90, Article 101743. https://doi.org/10.1016/j.csl.2024.101743> https://doi.org/10.1016/j.csl.2024.101743

Johri, A. (2022). Lifelong and lifewide learning for the perpetual development of expertise in engineering. European Journal of Engineering Education, 47(1), 70–84. https://doi.org/10.1080/03043797.2021.1944064> https://doi.org/10.1080/03043797.2021.1944064

Jørgensen, M. T., & Brogaard, L. (2021). Using differentiated teaching to address academic diversity in higher education: Empirical evidence from two cases. Learning and Teaching, 14(2), 87–110. https://doi.org/10.3167/latiss.2021.140206> https://doi.org/10.3167/latiss.2021.140206

Kabudi, T., Pappas, I., & Olsen, D. H. (2021). AI-enabled adaptive learning systems: A systematic mapping of the literature. Computers and Education: Artificial Intelligence, 2, Article 100017. https://doi.org/10.1016/j.caeai.2021.100017> https://doi.org/10.1016/j.caeai.2021.100017

Kanchon, M. K. H., Sadman, M., Nabila, K. F., Tarannum, R., & Khan, R. (2024). Enhancing personalized learning: AI-driven identification of learning styles and content modification strategies. International Journal of Cognitive Computing in Engineering, 5, 269–278. https://doi.org/10.1016/j.ijcce.2024.06.002> https://doi.org/10.1016/j.ijcce.2024.06.002

Martin, A. J., & Dominic, M. M. (2021). Personalization of learning objects according to the skill set of the learner using knowledge graph. Turkish Journal of Computer and Mathematics Education, 12(6), 3974–3987.

Minn, S. (2022). AI-assisted knowledge assessment techniques for adaptive learning environments. Computers and Education: Artificial Intelligence, 3, Article 100050. https://doi.org/10.1016/j.caeai.2022.100050> https://doi.org/10.1016/j.caeai.2022.100050

Osakwe, I., Chen, G., Fan, Y., Rakovic, M., Li, X., Singh, S., Molenaar, I., Bannert, M., & Gašević, D. (2023). Reinforcement learning for automatic detection of effective strategies for self-regulated learning. Computers and Education: Artificial Intelligence, 5, Article 100181. https://doi.org/10.1016/j.caeai.2023.100181> https://doi.org/10.1016/j.caeai.2023.100181

Owoseni, A., Kolade, O., & Egbetokun, A. (2024). Applications of generative AI in summative assessment. In Generative AI in Higher Education: Innovation Strategies for Teaching and Learning (pp. 97–122). Palgrave Macmillan. https://doi.org/10.1007/978-3-031-60179-8_4> https://doi.org/10.1007/978-3-031-60179-8_4

Palimkar, S. S., Kunnathully, K., Thota, S., Joshi, M., Akkalkot, A. I., & Al Said, N. (2025, February). Using machine learning to enhance personalization in e-learning. In 2025 International Conference on Pervasive Computational Technologies (ICPCT) (pp. 411–415). IEEE. https://doi.org/10.1109/ICPCT64145.2025.10939236> https://doi.org/10.1109/ICPCT64145.2025.10939236

Peng, H., Ma, S., & Spector, J. M. (2019). Personalized adaptive learning: An emerging pedagogical approach enabled by a smart learning environment. Smart Learning Environments, 6, Article 9. https://doi.org/10.1186/s40561-019-0089-y> https://doi.org/10.1186/s40561-019-0089-y

Pliuskuvienė, B., Radvilaitė, U., Juodagalvytė, R., Ramanauskaitė, S., & Stefanovič, P. (2024). Educational data mining and learning analytics: Text generators usage effect on students’ grades. New Trends in Computer Sciences, 2(1), 19–30. https://doi.org/10.3846/ntcs.2024.21318> https://doi.org/10.3846/ntcs.2024.21318

Riley-Lepo, E., Barnes, N., & Fives, H. (2024). Formative assessment in focus: An exploration of theory and practice. The Teacher Educator. https://doi.org/10.1080/08878730.2024.2436417> https://doi.org/10.1080/08878730.2024.2436417

Sayed, W. S., Noeman, A. M., Abdellatif, A., Abdelrazek, M., Badawy, M. G., Hamed, A., & El-Tantawy, S. (2023). AI-based adaptive personalized content presentation and exercises navigation for an effective and engaging E-learning platform. Multimedia Tools and Applications, 82, 3303–3333. https://doi.org/10.1007/s11042-022-13076-8> https://doi.org/10.1007/s11042-022-13076-8

Shaik, T., Tao, X., Li, Y., Dann, C., McDonald, J., Redmond, P., & Galligan, L. (2022). A review of the trends and challenges in adopting natural language processing methods for education feedback analysis. IEEE Access, 10, 56720–56739. https://doi.org/10.1109/ACCESS.2022.3177752> https://doi.org/10.1109/ACCESS.2022.3177752

Stefanovič, P., Pliuskuvienė, B., Radvilaitė, U., & Ramanauskaitė, S. (2024). Machine learning model for ChatGPT usage detection in students’ answers to open-ended questions: Case of Lithuanian language. Education and Information Technologies, 29(14), 18403–18425. https://doi.org/10.1007/s10639-024-12589-z> https://doi.org/10.1007/s10639-024-12589-z

Sugano, S. G. C., & Mamolo, L. A. (2021). The effects of teaching methodologies on students’ attitude and motivation: A meta-analysis. International Journal of Instruction, 14(3), 827–846. https://doi.org/10.29333/iji.2021.14348a> https://doi.org/10.29333/iji.2021.14348a

Svensäter, G., & Rohlin, M. (2023). Assessment model blending formative and summative assessments using the SOLO taxonomy. European Journal of Dental Education, 27(1), 149–157. https://doi.org/10.1111/eje.12787> https://doi.org/10.1111/eje.12787

Taylor, D. L., Yeung, M., & Bashet, A. Z. (2021). Personalized and adaptive learning. In Innovative learning environments in STEM higher education: Opportunities, Challenges, and Looking Forward (pp. 17–34). Springer. https://doi.org/10.1007/978-3-030-58948-6_2> https://doi.org/10.1007/978-3-030-58948-6_2

Trindade, M. A., Edirisinghe, G. S., & Luo, L. (2025). Teaching mathematical concepts in management with generative artificial intelligence: The power of human oversight in AI-driven learning. The International Journal of Management Education, 23(2), Article 101104. https://doi.org/10.1016/j.ijme.2024.101104> https://doi.org/10.1016/j.ijme.2024.101104

Vittorini, P., Menini, S., & Tonelli, S. (2021). An AI-based system for formative and summative assessment in data science courses. International Journal of Artificial Intelligence in Education, 31(2), 159–185. https://doi.org/10.1007/s40593-020-00230-2> https://doi.org/10.1007/s40593-020-00230-2

Wang, X. J., Lee, C., & Mutlu, B. (2025). LearnMate: Enhancing online education with LLM-powered personalized learning plans and support. preprint arXiv:2503.13340. arXiv. https://doi.org/10.48550/arXiv.2503.13340> https://doi.org/10.48550/arXiv.2503.13340

Zheng, W. (2024). Intelligent e-learning design for art courses based on adaptive learning algorithms and artificial intelligence. Entertainment Computing, 50, Article 100713. https://doi.org/10.1016/j.entcom.2024.100713> https://doi.org/10.1016/j.entcom.2024.100713

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2024-12-31

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How to Cite

Blaževičiūtė, L., Matyjaškoit, N., Leščinskij, R., & Stefanovič, P. (2024). Trends and challenges in personalizing learning content for students using artificial intelligence. New Trends in Computer Sciences, 2(2), 117–127. https://doi.org/10.3846/ntcs.2024.23699

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