IDEAS home Printed from https://ideas.repec.org/a/prg/jnlaip/vpreprintid278.html
   My bibliography  Save this article

Student Perceptions and Preferences in Personalized AI-driven Learning

Author

Listed:
  • Marta Slepankova
  • Kristyna Kilianova
  • Petra Kockova
  • Katerina Kostolanyova
  • Martin Kotyrba
  • Hashim Habiballa

Abstract

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.

Suggested Citation

  • Marta Slepankova & Kristyna Kilianova & Petra Kockova & Katerina Kostolanyova & Martin Kotyrba & Hashim Habiballa, . "Student Perceptions and Preferences in Personalized AI-driven Learning," Acta Informatica Pragensia, Prague University of Economics and Business, vol. 0.
    • Handle: RePEc:prg:jnlaip:v:preprint:id:278
    DOI: 10.18267/j.aip.278
    as

    Download full text from publisher

    File URL: http://aip.vse.cz/doi/10.18267/j.aip.278.html
    Download Restriction: free of charge
    File URL: https://libkey.io/10.18267/j.aip.278?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:prg:jnlaip:v:preprint:id:278. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Stanislav Vojir (email available below). General contact details of provider: https://edirc.repec.org/data/uevsecz.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.