AI Insight
This study investigated how 600 undergraduate students develop overreliance on generative AI feedback during learning. Results showed that perceived cognitive load directly increases overreliance on AI feedback and also works indirectly through cognitive offloading, where students externalize mental effort to the AI system. Students with lower metacognitive monitoring (self-awareness of their thinking) showed more direct dependence on AI under cognitive pressure, while those with higher metacognitive monitoring exhibited a more regulated process involving cognitive offloading as an intermediary step.
Why it matters
The findings reveal that AI learning tools may inadvertently encourage students to substitute rather than supplement their cognitive efforts, particularly when facing challenging material. Understanding these patterns can help educators design interventions that promote healthier AI use in education, especially for students who lack strong self-monitoring skills.
Understand the Science
Generative artificial intelligence (GAI) has introduced an important question in education, when AI provides immediate and reliable feedback, do students use it as a learning support tool or gradually rely on it as a substitute for their own cognitive efforts? Drawing on Cognitive Load Theory, Cognitive Offloading Theory, and Metacognitive Monitoring Theory, this study develops a theoretical model to explain the formation process of undergraduate students’ overreliance on GAI feedback. Specifically, this study examines the relationships among perceived cognitive load, cognitive offloading, and overreliance on GAI feedback, while further exploring how metacognitive monitoring levels shape this process. A questionnaire survey was conducted among 600 undergraduate students, and structural equation modeling was performed using AMOS 31.0. The results showed that perceived cognitive load significantly and positively predicted cognitive offloading, which further contributed to students’ overreliance on GAI feedback. Meanwhile, perceived cognitive load also directly predicted overreliance on GAI feedback. Bootstrap analysis further confirmed that cognitive offloading mediated the relationship between perceived cognitive load and overreliance on GAI feedback. In addition, this study established separate structural equation models for students with different levels of metacognitive monitoring and found distinct patterns in the formation of GAI feedback overreliance. Specifically, students with lower metacognitive monitoring were more likely to be influenced by cognitive pressure during learning, showing a more prominent direct relationship between perceived cognitive load and GAI feedback overreliance. This finding suggests that these students tend to rely more directly on GAI feedback when facing higher cognitive demands. In contrast, students with higher metacognitive monitoring demonstrated a more evident cognitive regulation process, in which cognitive offloading played a more important role in linking perceived cognitive load with GAI feedback overreliance. This indicates that these students may actively adjust their cognitive resource allocation when using GAI, although continuous cognitive offloading may still increase their dependence on AI-generated feedback.