Nov 2025· Proceedings of the National Academy of Sciences of the United States of America· Vol 123 35, pp.
e2530627123
· 4 citations· ⚡ 1 influential· 60 references
MedicineComputer Science
TL;DR
The results demonstrate that personality pairing can improve human-AI collaboration and performance and motivate future research on the complex implications of AI personalization for human-AI collaboration, teamwork, and performance.
Abstract
Here we examine how AI agent "personalities" interact with human personalities to shape human-AI collaboration and performance. In a large-scale, preregistered randomized experiment, we paired 1,258 participants with AI agents prompted to exhibit varying levels of the Big Five personality traits. These human-AI teams produced 7,266 display ads for a real think tank, which we evaluated using 1,168 independent human raters, and a field experiment on X that generated nearly 5 million impressions. We found that human and AI personalities individually shaped ad quality and teamwork and that human-AI personality pairings directly influenced ad quality. For example, extraverted humans paired with conscientious AI produced the lowest quality ads, followed by conscientious humans paired with agreeable AI and neurotic humans paired with conscientious AI. In the field experiment, ad quality significantly influenced ad performance, measured by click-through rates and cost-per-click. Together, these results demonstrate that personality pairing can improve human-AI collaboration and performance. They also motivate future research on the complex implications of AI personalization for human-AI collaboration, teamwork, and performance.
Prior research often finds that AI creativity is limited: single systems rarely outperform humans, and human-AI collaboration does not exceed human output. We argue these conclusions underestimate AI's potential because most studies do not allow iterative, multi-agent exchanges that mirror the social processes underpin...
Y. Luan, Luning Sun, YeunJoon Kim et al.· 0 citations
This work proposes A-B-D to infer traits bottom-up from behavioral data (B-data), namely how agents act on their environment and communicate with users, as recorded in existing trajectories, and offers a new lens for understanding AI personality.
Hao-Kai Zhao, Jie Gao, Yunze Xiao et al.· 0 citations
AI assistants are increasingly framed as digital coworkers rather than tools. In immersive settings such as VR, AI embodiment design choices can become salient social signals that shape human–AI interaction. We investigate how such choices influence users’ perceptions of and behavior toward AI coworkers by employing th...
Isabelle Cuber, Tarek Alakmeh, Mary M. Hausfeld et al.· Proceedings of the 14th Nord...· 0 citations
This study compares Human alone and Human+AI performance on reasoning tasks, for which the AI model is benchmarked on the same items, and distinguishes performance augmentation from metacognitive augmentation and motivate interfaces that support verification, communicate task-specific AI model performance, and help use...
Daniela Fernandes, Michelle Rausch, A. M. Kloft et al.· 0 citations
The rapid increase in generative artificial intelligence (Gen AI) products has revolutionised the interaction between humans and artificial intelligence. However, the conceptualisation, measurement, and performance implications of human-AI interaction in the workplace remain underexplored. This study utilised a grounde...
Ye-Peng Wu, Yuan-Yuan Jiao, Ping Li et al.· Humanities and Social Scienc...· 0 citations
Findings provide evidence that frequent interactions with AI platforms can diminish sensitivity to the differences between real and synthetic content, but that targeted interventions can meaningfully benefit human evaluators' discernment skill.
Tanaka Manhede, Yuliana Fartachuk, S. Martinez et al.· AI & SOCIETY· 0 citations
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