Individual Gain, Collective Loss: Metacognitive Adaptation in AI-Assisted Creativity
For researchers and practitioners in AI-assisted creativity, this framework explains the trade-off between individual gains and collective losses, providing design principles to preserve diversity.
The paper identifies a paradox where AI boosts individual creativity but reduces collective diversity, and proposes selective metacognitive adaptation as the mechanism. It offers a taxonomy of six metacognitive capacities and shows how individually rational adaptation leads to collective convergence.
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.