A Metacognitive Approach to Learning and Performance in Human-AI Interaction

Generative artificial intelligence (GenAI) is rapidly becoming a pervasive technology across education, work, research, and everyday life. Yet growing evidence suggests that fluent, coherent, and seemingly transparent AI assistance can improve task performance while weakening the metacognitive engagement and deeper processing required for durable learning. This tension raises a central question: how does offloading to AI reshape human learning, and under what conditions do performance and learning decouple? In this Perspective, we advance a metacognitive account of human-AI interaction by conceptualising metacognitive offloading and metacognitive onloading and by introducing the Synergetic Notion of Human-AI Skills and Knowledge Construction (SYNC) model. The SYNC model explains how human-AI interaction unfolds across learning and performance dimensions, clarifying how learners may drift into overreliant AI-supported performance or move toward human-AI synergy. We further identify key pitfalls in ineffective human-AI interaction and outline strategies for scaffolding metacognitive engagement. This Perspective provides a conceptual foundation for understanding when AI-supported performance contributes to durable human capability and when it instead risks undermining durable learning.