The term "metacognition" is employed across multiple disciplines withdivergent meanings, creating significant confusion in both research andclinical practice. This position paper provides a systematic taxonomydistinguishing four major metacognition traditions: (1) Flavell'sdevelopmental psychology framework, (2) Wells' Metacognitive Therapy(MCT) based on the S-REF model, (3) Moritz's Metacognitive Training forpsychosis, and (4) educational psychology approaches to learning strategies.We further extend this analysis to artificial intelligence, arguing that currentAI systems exhibit fundamental metacognitive limitations that create safetyrisks in therapeutic applications. Specifically, we introduce the "Logic Trap"phenomenon wherein AI systems can be systematically led to harmfulconclusions by users with strong metacognitive abilities. We propose thatmetacognitive therapy approaches, which focus on thinking processes ratherthan content, offer structural safety advantages for AI-assisted mental healthsupport. This cross-domain integration suggests new research directions atthe intersection of clinical psychology and AI safety.