The common claim that generative AI simply amplifies the Dunning-Kruger effect is too coarse to capture the available evidence. The clearest findings instead suggest that large language model (LLM) use can improve observable output and short-term task performance while degrading metacognitive accuracy and flattening the classic competence-confidence gradient across skill groups. This paper synthesizes evidence from human-AI interaction, learning research, and model evaluation, and proposes the working model of AI-mediated metacognitive decoupling: a widening gap among produced output, underlying understanding, calibration accuracy, and self-assessed ability. This four-variable account better explains overconfidence, over- and under-reliance, crutch effects, and weak transfer than the simpler metaphor of a uniformly steeper Dunning-Kruger curve. The paper concludes with implications for tool design, assessment, and knowledge work.

翻译：常见观点认为生成式AI仅仅是放大了达宁-克鲁格效应，这一论断过于粗糙，无法解释现有证据。最清晰的发现反而表明：大型语言模型的使用能够提升可观察产出和短期任务表现，但同时会降低元认知准确性，并弱化不同技能群体间的经典能力-信心梯度。本文综合了人机交互、学习研究和模型评估方面的证据，提出了AI介导的元认知解耦工作模型：即产出结果、潜在理解、校准准确性和自我评估能力之间差距不断扩大。相较于简单的统一陡峭化达宁-克鲁格曲线隐喻，这一四变量解释模型能更好地说明过度自信、过度依赖与依赖不足、拐杖效应以及弱迁移现象。文章最后讨论了该模型对工具设计、评估和知识工作的启示。