The Impact of Generative AI Tutors on Metacognitive Monitoring and Self-Regulation in Higher Education: An Experimental Study

The rapid integration of generative artificial intelligence (AI) tools in higher education has prompted significant scholarly debate regarding their pedagogical implications. While these tools offer unprecedented opportunities for personalized learning support, concerns have been raised about their potential to undermine students' metacognitive development by enabling cognitive offloading and fostering intellectual dependency. This experimental study investigated the causal impact of generative AI tutors on undergraduate students' metacognitive monitoring accuracy (calibration) and self-regulated learning processes. One hundred and thirty-eight (138) undergraduate students were randomly assigned to either an experimental condition, in which they completed complex problem-solving tasks in introductory programming with access to a generative AI tutor, or a control condition, in which they used traditional instructional resources. Metacognitive monitoring accuracy was measured through judgments of learning and compared with actual performance. Interaction logs and think-aloud protocols were analyzed to examine self-regulatory processes. A transfer task completed without AI support assessed the durability of observed effects. Students who used the AI tutor demonstrated significantly poorer calibration accuracy compared to the control group (p < .001, d = 0.75). Interaction log analysis revealed that AI users predominantly engaged in direct answer requests (44%) and verification requests (32%), indicating substantial cognitive offloading. Prior knowledge moderated the effect, with lower-knowledge students being most adversely affected. The metacognitive deficit persisted on a transfer task completed without AI support (p < .001, d = 0.83). This study provides the first rigorous experimental evidence that generative AI tutors can impair metacognitive monitoring, particularly among novice learners. The findings extend theories of self-regulated learning to account for distributed cognition with AI tools and have significant implications for instructional design, AI literacy curricula, and institutional policies governing AI use in higher education. As generative AI becomes increasingly prevalent in educational settings, these findings underscore the urgent need for pedagogical frameworks that harness the benefits of AI while safeguarding the development of students' metacognitive autonomy and capacity for lifelong learning.