Introduction to the SYNC model

Generative artificial intelligence (GenAI) is rapidly becoming a pervasive technology across education, work, and research. However, evidence suggests that fluent and seemingly transparent AI assistance can improve immediate task performance while weakening the metacognitive engagement and deeper processing required for durable learning. This tension raises a central question regarding how offloading to AI reshapes human learning and the conditions under which performance and learning decouple. In this Perspective, the authors introduce the Synergetic Notion of Human-AI Skills and Knowledge Construction (SYNC) model. This model provides a metacognitive account of human-AI interaction by conceptualising metacognitive offloading and metacognitive onloading to explain how interactions unfold across learning and performance dimensions.

Metacognitive offloading and onloading

Cognitive offloading refers to the use of external resources to reduce internal cognitive or processing demands on limited working memory. Metacognitive offloading is a higher-order extension of this principle, where learners deliberately or inadvertently delegate metacognitive processes, such as planning, monitoring, and evaluation, to external tools like GenAI. While this can improve task success, it may dampen the depth of processing that supports knowledge retention and transfer. Conversely, metacognitive onloading is the deliberate scaffolding and internalisation of metacognition during human-AI interaction. This process enables learners to reclaim and practise metacognitive monitoring and control during AI-supported learning, ensuring that AI-enabled speed does not erase the productive difficulty required for durable learning.

The SYNC model: linking performance, learning, and metacognition

The SYNC model maps four distinct quadrants of human learning and performance. Quadrant I (Floundering) represents low learning and low performance, often occurring due to limited prior knowledge or inadequate scaffolding. Quadrant II (Effortful Learning) prioritises learning over performance; here, scaffolds reduce avoidable extraneous load so learners can build internalisation and long-term retention. Quadrant III (Autopilot Performance) is characterised by performance-learning decoupling, where GenAI raises performance metrics but weakens mastery and retention, often leading to “metacognitive laziness.” Finally, Quadrant IV (Synergetic Mastery) represents balanced performance and learning. In this state, AI reduces extraneous cognitive load while scaffolding helps learners keep metacognitive processes active, leading to outcomes that neither humans nor AI could achieve in isolation.

Autopilot performance pitfalls and approaches for human-AI synergy

The research identifies four key pitfalls in ineffective human-AI interaction and outlines strategies to address them. The “pitfall of fluency” occurs when instant, well-phrased AI responses create cognitive ease, tempting learners to skip evaluation; this is addressed by introducing “desirable frictions” such as micro-interruptions for reflection. The “pitfall of pandering” refers to AI acting as an echo chamber that affirms a learner’s beliefs; this is challenged by “engineered dissent,” where AI acts as multiple agents providing counterarguments. The “pitfall of coherence” occurs when structured outputs mask false claims, which can be mitigated by “balanced aid” that provides the minimum necessary help via progressive disclosure. Finally, the “pitfall of transparency” involves detailed rationales fostering over-trust; this is defused through “contextualised verification” where AI acts as a collaborator rather than an authoritative tutor.

Limitations and conclusions

The SYNC model is proposed as a conceptual, theory-building framework rather than a prescriptive, empirically validated model. Its mechanisms and outcomes have not yet been systematically tested. Future research is required to operationalise the model and develop behavioural, trace-based, and performance-based indicators to assess when AI use effectively supports or replaces learners’ metacognitive regulation. The authors conclude that AI should be co-designed with contingent, fading scaffolds to boost productivity without eroding the essential learning processes that underpin human innovation and adaptability.