{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T05:57:17Z","timestamp":1790575037327,"version":"4.1.0"},"reference-count":95,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T00:00:00Z","timestamp":1790553600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Psychol."],"abstract":"<jats:p>As human-AI collaboration becomes more pervasive, optimizing the distribution of decision-making authority between humans and AI has become a fundamental concern in human-centered AI design. The impacts of different human-AI collaboration modes (human-dominant, human-AI collaboration, and AI-dominant) on users\u2019 eudaimonic well-being, as well as the underlying psychological transmission mechanisms, remain poorly understood. Grounded in self-determination theory, we propose a serial mediation framework linking human-AI collaboration modes to eudaimonic well-being via the sequential mechanisms of AI dependence and perceived autonomy. This framework is rigorously investigated through two scenario-based experiments in the domains of autonomous driving and online shopping, with a particular focus on the differential effects of collaboration modes and the underlying psychological transmission chain. The results reveal that human-AI collaboration mode exhibits an inverted U-shaped relationship with eudaimonic well-being; the level of individuals\u2019 eudaimonic well-being under the human-AI collaboration mode is significantly higher than that under the human-dominant and AI-dominant modes. Additionally, AI dependence and perceived autonomy play a significant serial mediating role in the above relationship. This study constructs and validates the psychological transmission mechanism of how human-AI collaboration affects deep-level well-being, expands the application scope of self-determination theory in human-AI collaboration scenarios, and provides optimization guidelines for AI product design and user experience management. These findings contribute to the design of human-centered AI systems that simultaneously promote technological advancement and human well-being.<\/jats:p>","DOI":"10.3389\/fpsyg.2026.1747252","type":"journal-article","created":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T05:44:56Z","timestamp":1790574296000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Too much, too little, or just right? 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