The Mediating Role of Generative AI Self-Regulation on Students’ Critical Thinking and Problem-Solving

Within the rapid integration of AI into educational settings, understanding its impact on essential cognitive skills is crucial for developing effective teaching strategies and improving student outcomes. This study examines the influence of generative artificial intelligence (GenAI) on students’ critical thinking and problem-solving skills in higher education. Our research specifically investigates how the perceived ease of use, usefulness, and learning value of GenAI tools might influence students’ critical thinking and problem-solving skills, and whether self-regulation serves as a mediator in this relationship. Utilising a quantitative approach, we surveyed 223 students and analysed their responses using a structural equation modelling method. The results reveal that the ease of use of GenAI significantly enhances self-regulation, which in turn positively impacts both the critical thinking and problem-solving abilities of students. However, the perceived usefulness and learning value of GenAI were not found to significantly influence these skills through self-regulation. These findings suggest that, while AI tools can offer an environment conducive to developing higher-order cognitive skills, this might not necessarily translate to the enhancement of students’ skills. This research contributes to the ongoing literature on the role of technology in education by highlighting the importance of designing GenAI tools that support self-regulated learning. Furthermore, it calls for educators and developers to focus not just on the functionality of AI, but also on how these tools can be integrated into curricula to effectively support critical thinking and problem-solving. The practical implications of our research highlight the need for AI tools that are user-friendly and aligned with educational goals, enhancing their adoption and effectiveness in improving student outcomes. It is crucial for educators to integrate strategies that promote self-regulation within AI-enhanced learning environments to maximise their impact on student learning.