Can Machines Think About Thinking? Reframing Metacognition in the Age of Generative AI
Simone Conversano
Journal of Artificial Intelligence and Consciousness April 3, 2026 DOI: 10.1142/s2705078526400060 (opens in new tab)
Study at a glance
AI-extracted from the abstract| Characteristics | Theoretical or philosophical paper Peer reviewed |
|---|---|
| Key points | Argues that metacognition should be understood as a functionally realizable property across different substrates, defined by internal representation, monitoring, and self-regulation, and evaluated by what systems do rather than what they are. Contends that the absence of evidence for artificial metacognition is not evidence of impossibility, and that recent LLM findings show early functional signatures of self-monitoring suggesting limited operational introspection, though artificial metacognition has not been demonstrated. |
Abstract
In this paper, I investigate whether metacognition — the ability to monitor, evaluate, and regulate one’s own cognitive processes and performance — can arise in non-biological systems, especially Large Language Models (LLMs). Drawing on cognitive science and philosophy of mind, I contrast embodied and enactivist accounts, which tie metacognition to biological consciousness and embodied entities, with functionalist perspectives that define it as a substrate-independent process. I argue that the absence of evidence is not evidence of impossibility and propose a functional definition of metacognition based on internal representation, monitoring, and self-regulation. Recent studies on LLMs show early functional signatures of self-monitoring, suggesting the emergence of limited operational introspection. While I do not claim that artificial metacognition has been demonstrated, I advocate an epistemically open, non-anthropocentric approach. Metacognition, I conclude, should be conceived as a functionally realizable property across different substrates, evaluated by what systems do, not what they are.