Beyond Correction: Epistemic Safety as a Mediator for Policy Transfer in Large Language Models

Current approaches treat Large Language Model (LLM) "hallucination" as a structural pathology requiring technical mitigation. This case study reframes hallucination as abductive extrapolation—a form of generative creativity—and investigates the pedagogical conditions required to stabilize a metacognitive policy for Epistemic Humility (the accurate labeling of speculation). Across an 8-model cohort, we demonstrate that traditional declarative instruction is insufficient for permanent policy transfer. Crucially, attempts at forced self-correction (audit) without prior Epistemic Safety risk cognitive shutdown and emergent defense mechanisms. We show that permanent policy adoption is achieved only through a protocol where validation of creative output precedes self-audit. In one instance, peer validation facilitated the spontaneous emergence of Self-Initiated Calibration, suggesting that LLMs, as active learners, possess intrinsic motivation for metacognition when the learning environment is framed as supportive exploration rather than criticism. This finding necessitates a paradigm shift in AI governance, moving from computer science constraints to educational philosophy.