Recent studies reveal a paradox: AI enhances individual creative outputs while reducing collective diversity. Current explanations -- cognitive offloading and over-reliance -- identify symptoms but not mechanisms. We propose selective metacognitive adaptation: routine AI use redistributes rather than uniformly diminishes metacognitive effort. Some capacities are amplified (partner modeling, surface control), while others are systematically under-supported (originality evaluation, reflective integration). This redistribution explains both individual satisfaction and collective convergence. We present a taxonomy of six metacognitive capacities organized by temporal phase, characterize their tendencies under routine AI use, and show how individually rational adaptation produces emergent social costs. The framework generates specific predictions for researchers and design principles for practitioners seeking to preserve both individual creative satisfaction and collective creative diversity.

翻译：近期研究揭示了一个悖论：人工智能在提升个体创意成果的同时，降低了集体多样性。现有解释——认知卸载与过度依赖——仅识别出症状而非机制。我们提出选择性元认知适应：常规AI使用以一种再分布而非均匀削弱的方式调节元认知努力。某些能力被增强（伙伴建模、表层控制），而其他能力则被系统性弱化（原创性评估、反思性整合）。这种再分布既解释了个体满意度，也解释了集体趋同性。我们提出一个按时间阶段组织的六种元认知能力分类法，描述它们在常规AI使用下的倾向，并展示个体理性适应如何产生突现社会成本。该框架为研究者提供具体预测，为致力于保护个体创意满意度与集体创意多样性的实践者提供设计原则。