AI让自信与真实能力脱钩，反而降低自我评估准确性。
Beyond the Steeper Curve: AI-Mediated Metacognitive Decoupling and the Limits of the Dunning-Kruger Metaphor
- 用大模型时输出变好但自我判断更差，四者逐渐脱节。
- 高阶用户仍过度自信，低阶用户信心下降，曲线变平。
- 适合关注AI辅助学习、测评设计的教育与人机交互研究者。
普遍认为生成式AI会放大邓宁-克鲁格效应，但现有证据表明这一说法过于粗糙。更清晰的发现显示，使用大型语言模型（LLM）可提升可见输出与短期任务表现，却损害元认知准确性，并使不同技能群体间的经典能力-信心梯度趋于平坦。本文综合了人机交互、学习研究与模型评估的证据，提出‘AI介导的元认知解耦’工作模型：产出、理解、校准准确性和自评能力之间出现裂隙。该四变量框架比简单‘更陡峭的邓宁-克鲁格曲线’更能解释过度自信、过度依赖、依赖惯性及弱迁移现象。论文最后探讨了对工具设计、评估体系和知识工作的启示。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。