Artificial Intelligence, Critical Thinking, and Self-Regulated Learning in Education: A Secondary Data Analysis of University Students’ Learning Questionnaire Data

Background: Artificial intelligence (AI) is slowly shaping the field of education to support individual learning, feedback, and control by learners. However, educational value of AI is not only based on the technological performance but also on the capability of students to self-rule the learning process, critical thinking and judgment.
Aim: This study has explored the patterns of critical-thinking in self-regulated learning and comment on their potential in AI-supported education.
Methods: The publicly available Figshare dataset by Juřík et al. (2025) comprising questionnaire data of university students who participated in self-regulated learning activities was subjected to a secondary quantitative analysis. The discussion concentrated on the questionnaire items of critical-thinking. Response distribution and pattern stability were analyzed using descriptive statistics, item-level comparison, interpretation based on correlation, and exploratory analysis through recurrence quantification.
Results: It was found that the critical-thinking scores were moderate-to-high, which implied that students tended to report affirmative involvement with reflective and evaluative learning behaviours. Patterns at items were rather balanced in the critical-thinking measures, whereas recurrence-based indicators implied the consistency of the response organization. Nevertheless, due to the fact that the data were obtained using responses of a Likert-scale questionnaire, but not direct results of AI-intervention, the results must be viewed with caution.
Conclusion: This paper holds that critical thinking and self-regulated learning are crucial capabilities of humans in AI-mediated learning. The AI will be able to improve education processes, and its potential lies in the capacity of the learners to analyze information, track their learning, and utilize technological tools in a responsible manner. Longitudinal or experimental studies should be conducted in the future to explore the direct effect of AI tools on creativity, critical thinking, and self-regulated learning outcomes.

Artificial intelligence in education, generative AI, critical thinking, creativity, self-regulated learning, recurrence quantification analysis.

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