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Can Machines Think About Thinking? Reframing Metacognition in the Age of Generative AI

Resource type
Journal Article
Author/contributor
Title
Can Machines Think About Thinking? Reframing Metacognition in the Age of Generative AI
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.
Publication
Journal of Artificial Intelligence and Consciousness
Publisher
World Scientific Publishing Co.
Date
2026-03
Volume
13
Issue
01
Pages
127-155
Journal Abbr
J. AI. Consci.
Accessed
4/22/26, 6:57 AM
ISSN
2705-0785
Short Title
Can Machines Think About Thinking?
Library Catalog
Citation
Conversano, S. (2026). Can Machines Think About Thinking? Reframing Metacognition in the Age of Generative AI. Journal of Artificial Intelligence and Consciousness, 13(01), 127–155. https://doi.org/10.1142/S2705078526400060