Acta Informatica Pragensia 2025, 14(2), 261-271 | DOI: 10.18267/j.aip.2781018

Student Perceptions and Preferences in Personalized AI-driven Learning

Marta Slepankova1, Kristyna Kilianova ORCID...1, Petra Kockova ORCID...1, Katerina Kostolanyova ORCID...1, Martin Kotyrba ORCID...2, Hashim Habiballa ORCID...2
1 Department of Information and Communication Technologies, Faculty of Education, University of Ostrava, Ostrava, Czech Republic
2 Department of Informatics and Computers, Faculty of Science, University of Ostrava, Ostrava, Czech Republic

Background: The use of artificial intelligence (AI) in education opens new possibilities for personalized learning. AI-driven systems allow students to progress at their own space, receive real-time feedback and have learning materials adapted to their individual needs. However, questions remain regarding studentsʹ perceptions of this approach and its effectiveness compared to traditional teaching methods.

Objective: This study aimed to analyse university studentsʹ attitudes and preferences towards AI-driven personalized learning and identify key factors influencing its effectiveness and adoption.

Methods: A mixed-method approach was employed, combining quantitative and qualitative data collection through a questionnaire survey conducted among students at the University of Ostrava. The data were collected in two phases during the winter semesters of 2023 and 2024, involving a total of 270 respondents.

Results: The findings indicate that 64.1% of students perceived AI-generated and adapted chapters as more helpful and effective than traditional study materials. The most valued aspects were content adaptability, real-time feedback and increased motivation to learn. However, 18.1% of respondents viewed AI-driven instruction as less beneficial, citing limited interactivity, a lack of detailed feedback and insufficient customization for advanced learners as the main drawbacks.

Conclusion: The research confirmed that AI-driven personalized learning can offer students a range of benefits, particularly in terms of adapting instructional content to individual needs, providing immediate feedback, and enabling self-paced study. However, certain challenges remain, especially regarding limited interactivity and insufficient depth of feedback, which may negatively affect students’ acceptance of such systems. To enhance the effectiveness and broader implementation of AI in educational practice, it is essential to focus on the development of interactive features, the improvement of analytical feedback, and the thoughtful integration of AI with traditional pedagogical approaches.

Keywords: AI-personalized learning; AI-driven learning; Artificial intelligence; Personalized learning; Student perception.

Received: February 11, 2025; Revised: May 21, 2025; Accepted: June 24, 2025; Prepublished online: July 7, 2025; Published: July 26, 2025  Show citation

ACS AIP APA ASA Harvard Chicago Chicago Notes IEEE ISO690 MLA NLM Turabian Vancouver
Slepankova, M., Kilianova, K., Kockova, P., Kostolanyova, K., Kotyrba, M., & Habiballa, H. (2025). Student Perceptions and Preferences in Personalized AI-driven Learning. Acta Informatica Pragensia, 14(2), 261-271. doi: 10.18267/j.aip.278
Download citation
  • BibTeX (.bib)
  • Bookends (.ris)
  • EasyBib (.ris)
  • EndNote (.enw)
  • EndNote 8 (.xml)
  • ISI WoS (.isi)
  • Medlars (.medlars)
  • Mendeley (.ris)
  • MODS (.xml)
  • Papers (.ris)
  • RefWorks (.txt)
  • RefManager (.ris)
  • RIS (.ris)
  • MS Word (.xml)
  • Zotero (.ris)
    • Open full article

    References

    1. Ayeni, O. O., Al Hamad, N. M., Onyebuchi, N. C., Osawaru, B., & Adewusi, O. E. (2024). AI in education: A review of personalized learning and educational technology. GSC Advanced Research and Reviews, 18(2), 261-271. https://doi.org/10.30574/gscarr.2024.18.2.0062 Go to original source...
    2. Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77-101. https://doi.org/10.1191/1478088706qp063oa Go to original source...
    3. Creswell, J. W. (2014). Research Design: Qualitative, Quantitative, and Mixed Methods. 4th ed. Thousand Oaks: Sage.
    4. Elliott, A. (2019). The Culture of AI: Everyday Life and the Digital Revolution. Routledge. https://doi.org/10.4324/9781315387185 Go to original source...
    5. Graneheim, U. H., Lindgre, B. M., & Lundman, B. (2017). Methodological challenges in qualitative content analysis: A discussion paper. Nurse education today, 56, 29-34. https://doi.org/10.1016/j.nedt.2017.06.002 Go to original source...
    6. Harkut, D. G., & Kasat, K. N. (2019). Introductory Chapter: Artificial Intelligence - Challenges and Applications. In Artificial Intelligence - Scope and Limitations. Intechopen. https://doi.org/10.5772/intechopen.84624 Go to original source...
    7. Mekni, M., Baani, Z., & Sulieman, D. (2020). A Smart Virtual Assistant for Students. In Proceedings of the 3rd International Conference on Applications of Intelligent Systems, (pp. 1-6). ACM. https://doi.org/10.1145/3378184.3378199 Go to original source...
    8. Jaakkola, H., Henno, J., Lahti, A., Järvinen, J.-P., & Mäkelä, J. (2020). Artificial Intelligence and Education. In K. Skala (Ed.), MIPRO 2020 - 43rd International Convention, (pp. 633-640). IEEE. https://doi.org/10.23919/MIPRO48935.2020.9245329 Go to original source...
    9. Jian, M. J. (2023). Personalized learning through AI. Advances in Engineering Innovation, 5, 16-19. https://doi.org/10.54254/2977-3903/5/2023039 Go to original source...
    10. Katiyar, N., Awasthi, V. K., Pratap, R., Mishra, K., Shukla, N., Singh, R., & Tiwari, M. (2024). Ai-Driven Personalized Learning Systems: Enhancing Educational Effectiveness. Educational Administration: Theory and Practice, 30(5), 11514-11524. https://doi.org/10.53555/kuey.v30i5.4961 Go to original source...
    11. Lee, L., Fung, Y., Pun, Y., Wong, K., Yu, M.T., & Wu, N. (2020). Using a Multiplatform Chatbot as an Online Tutor in a University Course. In 2020 International Symposium on Educational Technology (ISET), (pp. 53-56). IEEE. https://doi.org/10.1109/ISET49818.2020.00021 Go to original source...
    12. Ltifi, M. (2024). Advances in Digital Marketing in the Era of Artificial Intelligence: Case Studies and Data Analysis for Business Problem Solving. CRC Press. https://doi.org/10.1201/9781003450443 Go to original source...
    13. Merino-Campos, C. (2025). The impact of artificial intelligence on personalized learning in higher education: A systematic review. Trends in Higher Education, 4(2), 17. https://doi.org/10.3390/higheredu4020017 Go to original source...
    14. Number Analytics. (2025). 10 revolutionary applications of AI in modern education trends. https://www.numberanalytics.com/blog/10-revolutionary-applications-of-ai-in-education
    15. Paugh, R. (2025). AI and education: A new era of personalized, adaptive learning. Senior Executive. https://seniorexecutive.com/ai-in-education-personalized-learning-risks-and-future/
    16. Raja, S., Jebadurai, D.J., Ivan, L., Mykola, R.V., Ruslan, K., & Nadiia, P.R. (2024). Impact of Artificial Intelligence in Students' Learning Life. In Khamis, R., Buallay, A. (eds) AI in Business: Opportunities and Limitations. Studies in Systems, Decision and Control, (pp. 317). Springer. https://doi.org/10.1007/978-3-031-49544-1_1 Go to original source...
    17. Russo-Spena, T., Mele, C., & Marzullo, M. (2018). Practising Value Innovation through Artificial Intelligence: The IBM Watson Case. Journal of Creating Value, 5(1), 11-24. https://doi.org/10.1177/2394964318805839 Go to original source...
    18. Sandu, N., & Gide, E. (2019). Adoption of AI-Chatbots to enhance student learning experience in higher education in India. In 18th International Conference on Information Technology Based Higher Education and Training (ITHET). IEEE. https://doi.org/10.1109/ithet46829.2019.8937382 Go to original source...
    19. Slepankova, M. (2025). Zvýšení motivace studentů prostřednictvím adaptivního učení generovaného umělou inteligencí [Enhancing Student Motivation Through AI-Generated Adaptive Learning]. Dissertation thesis. University of Ostrava, Faculty of Education.
    20. Vorst, T. V., & Jelicic, N. (2019). Artificial Intelligence in Education: Can AI bring the full potential of personalized learning to education?. In 30th European Conference of the International Telecommunications Society (ITS), (pp. 1-21). International Telecommunications Society.
    21. Wang, S., Christensen, C., Cui, W., Tong, R., Yarnall, L., Shear, L., & Feng, M. (2023). When adaptive learning is effective learning: comparison of an adaptive learning system to teacher-led instruction. Interactive Learning Environments, 31(2), 793-803. https://doi.org/10.1080/10494820.2020.1808794 Go to original source...
    22. Xie, H., Chu, H.-C., Hwang, G.-J., & Wang, C. C. (2019). Trends and development in technology-enhanced adaptive/personalized learning: A systematic review of journal publications from 2007 to 2017. Computers & Education, 140, 103599. https://doi.org/10.1016/j.compedu.2019.103599 Go to original source...

    This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits use, distribution, and reproduction in any medium, provided the original publication is properly cited. No use, distribution or reproduction is permitted which does not comply with these terms.


    Return to the content