Smaller, Younger, and More Impactful: How AI-Assisted Writing Transforms Research Teams
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866917536804634624 |
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| author | Wang, Haoyang Zhang, Mingze Bu, Yi Zhao, Star Xing Liu, Meijun |
| author_facet | Wang, Haoyang Zhang, Mingze Bu, Yi Zhao, Star Xing Liu, Meijun |
| contents | The era of Big Science has long been defined by increasingly large and specialized research teams pushing the frontiers of knowledge. However, recent advances in artificial intelligence (AI), particularly large language models (LLMs), are beginning to reshape academic writing and scientific research, potentially disrupting the longstanding trend toward ever-larger teams and transforming other dimensions of research team structure. Drawing on 147,074 full-text publications from the PLoS family and the Nature portfolio since 2020, we examined whether and how AI-assisted writing influences team structure and team outcomes in science. Using multiple methods, including ordinary least square, quantile regression, Poisson regression, logistic regression and propensity score matching, we found that research teams using AI-assisted writing tend to be younger and smaller. Importantly, this shift toward more compact, junior-leaning teams does not come at the expense of scientific impact. On the contrary, we observed a higher probability of research teams that employed AI-assisted writing producing highly impactful publications. These results highlight the significant role of AI-assisted writing in reshaping not only how research is produced, but also how research teams are formed and assembled. Our findings call for policy improvements in research evaluation, funding, and training to address this emerging trend. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_27404 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Smaller, Younger, and More Impactful: How AI-Assisted Writing Transforms Research Teams Wang, Haoyang Zhang, Mingze Bu, Yi Zhao, Star Xing Liu, Meijun Computers and Society Artificial Intelligence The era of Big Science has long been defined by increasingly large and specialized research teams pushing the frontiers of knowledge. However, recent advances in artificial intelligence (AI), particularly large language models (LLMs), are beginning to reshape academic writing and scientific research, potentially disrupting the longstanding trend toward ever-larger teams and transforming other dimensions of research team structure. Drawing on 147,074 full-text publications from the PLoS family and the Nature portfolio since 2020, we examined whether and how AI-assisted writing influences team structure and team outcomes in science. Using multiple methods, including ordinary least square, quantile regression, Poisson regression, logistic regression and propensity score matching, we found that research teams using AI-assisted writing tend to be younger and smaller. Importantly, this shift toward more compact, junior-leaning teams does not come at the expense of scientific impact. On the contrary, we observed a higher probability of research teams that employed AI-assisted writing producing highly impactful publications. These results highlight the significant role of AI-assisted writing in reshaping not only how research is produced, but also how research teams are formed and assembled. Our findings call for policy improvements in research evaluation, funding, and training to address this emerging trend. |
| title | Smaller, Younger, and More Impactful: How AI-Assisted Writing Transforms Research Teams |
| topic | Computers and Society Artificial Intelligence |
| url | https://arxiv.org/abs/2605.27404 |