Individual Gain, Collective Loss: Metacognitive Adaptation in AI-Assisted Creativity

Computer Science > Artificial Intelligence [Submitted on 4 Jun 2026] Individual Gain, Collective Loss: Metacognitive Adaptation in AI-Assisted Creativity Anna Mikeda 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. Comments: 6 pages. AAAI 2026 paper Subjects: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC) Cite as: arXiv:2606.05532 [cs.AI]   (or arXiv:2606.05532v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2606.05532 Focus to learn more Submission history From: Anna Mikeda Ms [view email] [v1] Thu, 4 Jun 2026 00:21:35 UTC (21 KB) Access Paper: HTML (experimental) view license Current browse context: cs.AI < prev   |   next > new | recent | 2026-06 Change to browse by: cs cs.HC References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)