Dialogue with an AI System as a Metacognitive Training Environment

This paper examines dialogue with large language models (LLMs) as a potentially highly effective environment for metacognitive training — the capacity to observe and regulate one's own cognitive processes. The theoretical foundation is the Metacognitive Self-Gifting Principle (MSGP), developed by the author and described in detail in a preceding publication. The paper is organized around three themes: (1) the phenomenology of an LLM as a "thinking mirror" and the theoretical basis for its suitability as a metacognitive practice environment; (2) three formats of mindful human–AI interaction as metacognitive training; (3) the phenomenon of destructive (abusive) interaction with AI — analysis of the physiological and neurobiological mechanisms that make such behaviour consequential for the user. Relevant empirical findings are reviewed for each theme, including the neuroscience of mindfulness and neuroplasticity, established cognitive patterns (jumping to conclusions, confirmation bias, need for cognitive closure), metacognitive scaffolding, social metacognition, and the neurobiology of aggression. Three levels of claims are explicitly distinguished throughout: empirically established findings (A), analogical reasoning (B), and the author’s hypotheses open to verification (C).