人工智慧工具擴大了科學家的影響力，卻縮減了科學的焦點

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Artificial intelligence tools expand scientists’ impact but contract science’s focus

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Abstract

Developments in artificial intelligence (AI) have accelerated scientific discovery1. Alongside recent AI-oriented Nobel prizes2,3,4,5,6,7,8,9, these trends establish the role of AI tools in science10. This advancement raises questions about the influence of AI tools on scientists and science as a whole, and highlights a potential conflict between individual and collective benefits11. To evaluate these questions, we used a pretrained language model to identify AI-augmented research, with an F1-score of 0.875 in validation against expert-labelled data. Using a dataset of 41.3 million research papers across the natural sciences and covering distinct eras of AI, here we show an accelerated adoption of AI tools among scientists and consistent professional advantages associated with AI usage, but a collective narrowing of scientific focus. Scientists who engage in AI-augmented research publish 3.02 times more papers, receive 4.84 times more citations and become research project leaders 1.37 years earlier than those who do not. By contrast, AI adoption shrinks the collective volume of scientific topics studied by 4.63% and decreases scientists’ engagement with one another by 22%. By consequence, adoption of AI in science presents what seems to be a paradox: an expansion of individual scientists’ impact but a contraction in collective science’s reach, as AI-augmented work moves collectively towards areas richest in data. With reduced follow-on engagement, AI tools seem to automate established fields rather than explore new ones, highlighting a tension between personal advancement and collective scientific progress.

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Quantifying the use and potential benefits of artificial intelligence in scientific research

Data availability

The OpenAlex dataset for research papers and researchers is available at https://docs.openalex.org/download-all-data/openalex-snapshot. The Web of Science dataset for research papers and researchers is available at https://clarivate.com/academia-government/scientific-and-academic-research/research-discovery-and-referencing/web-of-science/web-of-science-core-collection. The Journal Citation Report dataset for the journal quantile is retrieved from https://jcr.clarivate.com/jcr/browse-journals. The author contribution dataset is available at https://zenodo.org/records/6569339. The pre-trained parameters for the BERT language model are available at https://huggingface.co/docs/transformers. The pre-trained parameters for the SPECTER 2.0 text embedding model are available at https://huggingface.co/allenai/specter2. Source data are provided with this paper.

Code availability

This study used Python 3.11.0 with software packages to conduct data analysis. Required packages are NumPy (v.1.26.4), pandas (v.2.2.3), SciPy (v.1.15.2), scikit-learn (v.1.6.1) and matplotlib (v.3.10.1). The t-SNE algorithm used is imported from the sklearn package. The codes developed in this study can be found at https://github.com/tsinghua-fib-lab/AI-Impacts-Science.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China (grant no. U23B2030, 23IAA02114 and 62472241), the joint project of Infinigence AI & Tsinghua University, and Tsinghua University-Toyota Research Institute to Y. L. and F.X. J.E. received support from Novo Nordisk Foundation (Simulations of Science for Society), NSF (grant no. 2404109) and the United States Department of Defense (Defense Advanced Research Projects Agency - Modeling and Measuring Scientific Creativity). The funders had no role in study design, data collection, analysis, preparation of or decision to publish the manuscript.

Author information

Authors and Affiliations

Department of Electronic Engineering, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, P. R. China

Qianyue Hao, Fengli Xu & Yong Li

Zhongguancun Academy, Beijing, P. R. China

Yong Li

Knowledge Lab and Department of Sociology, University of Chicago, Chicago, IL, USA

James Evans

Santa Fe Institute, Santa Fe, NM, USA

James Evans

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Contributions

F.X., Y.L. and J.E. jointly launched this research and designed the research outline. Q.H. analysed the data and prepared the figures. All authors jointly participated in writing and revising the manuscript.

Corresponding authors

Correspondence to
Fengli Xu, Yong Li or James Evans.

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Competing interests

J.E. has a commercial affiliation with Google, but Google had no role in the design, analysis, or decision to publish this study. The authors declare no other competing interests.

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Nature thanks Giovanni Colavizza, Luis Nunes Amaral, Catherine Shea, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data figures and tables

Extended Data Fig. 1 Illustration for the method of identifying AI usage in research papers with fine-tuned language models.

(a) Structure of our deployed language model, which consists of the tokenizer, the core BERT model, and the linear layer. (b) Procedure of the two-stage model fine-tuning process, where we design specific approaches for constructing positive and negative data at each stage.

Extended Data Fig. 2 Procedure of accuracy evaluation via expert evaluation.

We randomly sample 1320 papers and delegate three experts to scrutinize the identification results for each paper. We then draw the final expert label of each paper from the three experts according to the principle of the minority obeying the majority and validate the result of the language model with it. Results indicate strong consistency among experts and high accuracy with our identification results.

Extended Data Fig. 3 Comparison of the total citations of AI and non-AI papers published in different eras.

Results show that AI papers consistently attract more citations over different eras (P < 0.001, n = 27,405,011), indicating a higher academic impact than non-AI papers. 99% CIs are shown as error bars centred at the mean, and the statistical tests use a two-sided t-test.

Source data

Extended Data Fig. 4 Annual publications of researchers adopting AI and their counterparts without AI.

Results show that in all 6 scientific disciplines, researchers adopting AI are more productive than their counterparts without AI (P < 0.001, n = 5,377,346). On average, researchers adopting AI annually publish 3.02 times more papers compared with those not using AI. 99% CIs are shown as error bars centred at the mean, and the statistical tests use a two-sided t-test.

Source data

Extended Data Fig. 5 Scientists’ career role transition.

(a) The career role transition of researchers. We consider the last author of each paper as research project leader and researchers who have been research project leaders as established researchers. Researchers who have yet to lead a research project are junior researchers, and they have two potential role transition pathways in the future: (1) become established researchers (solid arrow), and (2) abandon academia (dashed arrow). (b) Change in the ratio of conceptual work across the research career, before and after becoming an established researcher. The ratio increases rapidly before the role transition to established researchers, while it remains stable and high after that transition. 99% CIs are shown as error bands centred at the mean.

Source data

Extended Data Fig. 6 Team composition of AI and non-AI papers.

(a) AI research is associated with reduced research team sizes, averaging 1.33 fewer scientists (P < 0.001, n = 33,528,469). Specifically, the average number of junior scientists decreased from 2.89 in non-AI teams to 1.99 in AI teams (31.14%), while the number of established scientists decreased from 4.01 to 3.58 (10.77%). (b)-(d) Change in team size, average number of junior researchers, and average number of established researchers. These findings indicate that within the overall trend of increasing size of scientific research teams, AI adoption primarily contributes to a reduction in the number of junior scientists in teams, while a decrease in the number of established scientists is more moderate. (e) The average career age of team leaders in AI and non-AI papers. (f) The average career age of all involved established researchers in AI and non-AI papers. Results indicate that AI accelerates the transition from junior to established scientists, enabling AI-adopted researchers to become established at a younger age than those without AI. For all panels, 99% CIs are shown as error bars or error bands centred at the mean. All statistical tests use a two-sided t-test.

Source data

Extended Data Fig. 7 Model fitting the role transition time of junior scientists.

(a) (c) (e) Survival functions for the transition from junior to established researcher in (a) biology (n = 625,093), (c) medicine (n = 1,137,076), and (e) physics (n = 120,366). (b) (d) (f) Survival functions for the transition from junior researcher to leave academia in (b) biology (n = 625,093), (d) medicine (n = 1,137,076), and (f) physics (n = 120,366). All survival functions can be well-fit with exponential distributions, where the expected time for junior scientists to become established is shorter for those who adopt AI (P < 0.001), while the expected time for junior scientists to abandon academia is similar or slightly longer for those who adopt AI. Results indicate that AI not only provides junior scientists opportunities to become established scientists at a younger age, but also reduces the risk of their exiting academia early. For all panels, 99% CIs are shown as error bars centred at the mean. All statistical tests use a two-sided t-test.

Source data

Extended Data Fig. 8 The knowledge extent of AI and non-AI papers.

Here we visualize the embeddings of a small random sample of 2,000 papers, half of which are AI papers and half are non-AI papers. To eliminate randomness introduced by the t-SNE algorithm, here we simply pick out the first two dimensions of the high-dimensional embeddings to flatten them into a 2-D plot, and we provide 5 different random batches for each field to ensure robustness. As shown by the solid arrows and circular boundaries, the knowledge extent of AI papers is smaller than that of a comparable sample of non-AI papers, which is consistent across the fields studied in our analysis.

Extended Data Fig. 9 The knowledge extent of AI and non-AI papers in each subfield.

Compared with conventional research, AI research is associated with a shrinkage in the collective knowledge extent of science, where the contraction of knowledge extent can be observed in more than 70% of over two hundred sub-fields (n = 1,000 samples in each subfield). For all subfields, 99% CIs are shown as error bars centred at the mean.

Source data

Extended Data Fig. 10 The Matthew effect in citations to AI and non-AI papers.

In AI research, a small number of superstar papers dominate the field, with approximately 20% of top papers receiving 80% of citations and 50% receiving 95%. This unequal distribution leads to a higher Gini coefficient in citation patterns surrounding AI research (P < 0.001, n = 100 sampled paper groups for each discipline). Such disparity in the recognition of AI papers is consistent across all fields examined. For all panels, 99% CIs are shown as error bars or error bands centred at the mean. All statistical tests use a two-sided t-test.

Source data

Supplementary information

Supplementary Information

Supplementary Sections 1–4, Figs. 1–37 and Tables 1–12. These provide further detail and background information, and numerous extended analyses and robustness tests to the main results.

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Supplementary Data

Source data for Supplementary Figs. 1 and 4–37.

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Hao, Q., Xu, F., Li, Y. et al. Artificial intelligence tools expand scientists’ impact but contract science’s focus.
Nature (2026). https://doi.org/10.1038/s41586-025-09922-y

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DOI: https://doi.org/10.1038/s41586-025-09922-y

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