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.
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| Title: | 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. |
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| Authors: | Du, Tianshu (AUTHOR), Li, Xiaoqian (AUTHOR), Jiang, Naifei (AUTHOR), Xu, Yichen (AUTHOR), Zhou, Yushu (AUTHOR) |
| Source: | International Journal of Human-Computer Interaction. Oct2025, Vol. 41 Issue 19, p12422-12433. 12p. |
| Subjects: | Artificial intelligence, Social role, Adaptive control systems, Attribution (Social psychology), Interpersonal relations, Occupational achievement, Participation |
| Abstract: | 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. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Human-Computer Interaction is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Psychology and Behavioral Sciences Collection |
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| Abstract: | 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. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 10447318 |
| DOI: | 10.1080/10447318.2025.2462120 |
