Metacognitive disengagement and knowledge innovation quality in human–AI collaboration: the mediating role of cognitive offloading and the moderating roles of task complexity and AI literacy

Generative artificial intelligence is fundamentally reshaping the cognitive conditions of knowledge innovation. While human–AI collaboration improves the efficiency of knowledge production, it may also induce creators to outsourcing cognitive work to AI systems. This study conceptualized this tendency as task-level metacognitive disengagement, defined as the immediate reduction of planning, monitoring, and reflection within a specific AI-assisted task, and, drawing on metacognition theory, cognitive offloading theory and self-regulated learning theory, examined its impact on knowledge innovation quality, its psychological mechanism, and boundary conditions through four complementary experiments. Experiment 1 showed that higher AI assistance significantly increased metacognitive disengagement. Experiment 2 revealed that cognitive offloading partially mediated the relationship between AI assistance and metacognitive disengagement. Experiment 3 confirmed the negative effect of cognitive offloading on knowledge innovation quality, specifically on novelty, usefulness, and developability, with metacognitive disengagement partially mediating this effect. Experiment 4 tested the proposed moderated mediation model and found that task complexity strengthened, whereas individual AI literacy weakened, the negative indirect effect of cognitive offloading on knowledge innovation quality through metacognitive disengagement. These findings reveal a hidden cognitive cost behind the efficiency gains of human–AI collaboration and provide empirical evidence and theoretical implications for cultivating innovative talent and designing human–AI collaborative mechanisms in the era of artificial intelligence.