Adaptive AI as Collaborator: Examining the Impact of an AI’s Adaptability and Social Role on Individual Professional Efficacy and Credit Attribution in Human–AI Collaboration

Human–AI collaboration has become increasingly prevalent, integrating sophisticated AI systems into various professional and personal domains. To explore how AI with trainability, dynamic participation, and real-time feedback positively, companies by different social role labels, promotes human–AI collaboration relationships, a 2 (static/adaptable AI) *2 (expert/peer) experiment was conducted in a laboratory with 96 university students. The study found that collaboration with adaptable AI teammates can greatly enhance human professional efficacy, promote individuals to attribute credit to themselves, and promote the establishment of directive and guided human–robot collaboration relationships. When collaborating with an AI expert, the individual gives more credit to the expert; individuals take credit for themselves. When collaborating with an AI peer. This study supplements the evaluation dimension of human-computer collaboration from personal long-term well-being and teamwork relationships. It provides important theoretical and practical design significance for promoting positive, healthy, and sustainable human–AI collaborative development.