Key points are not available for this paper at this time.
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 students' perceptions of this approach and its effectiveness compared to traditional teaching methods.Objective: This study aimed to analyse university students' 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.
Slepankova et al. (Mon,) studied this question.