{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T06:35:55Z","timestamp":1776753355378,"version":"3.51.2"},"reference-count":64,"publisher":"World Scientific Pub Co Pte Ltd","issue":"01","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. AI. Consci."],"published-print":{"date-parts":[[2026,3]]},"abstract":"<jats:p>In this paper, I investigate whether metacognition \u2014 the ability to monitor, evaluate, and regulate one\u2019s own cognitive processes and performance \u2014 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.<\/jats:p>","DOI":"10.1142\/s2705078526400060","type":"journal-article","created":{"date-parts":[[2026,4,5]],"date-time":"2026-04-05T11:56:13Z","timestamp":1775390173000},"page":"127-155","source":"Crossref","is-referenced-by-count":0,"title":["Can Machines Think About Thinking? 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