Quantifying the use and potential benefits of artificial intelligence in scientific research
Open accessAbstract
The rapid advancement of artificial intelligence (AI) is poised to reshape almost every line of work. Despite enormous efforts devoted to understanding AI's economic impacts, we lack a systematic understanding of the benefits to scientific research associated with the use of AI. Here we develop a measurement framework to estimate the direct use of AI and associated benefits in science. We find that the use and benefits of AI appear widespread throughout the sciences, growing especially rapidly since 2015. However, there is a substantial gap between AI education and its application in research, highlighting a misalignment between AI expertise supply and demand. Our analysis also reveals demographic disparities, with disciplines with higher proportions of women or Black scientists reaping fewer benefits from AI, potentially exacerbating existing inequalities in science. These findings have implications for the equity and sustainability of the research enterprise, especially as the integration of AI with science continues to deepen.
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When used in proposal preparation, LLMs may enhance fluency and alignment with prevailing norms while reducing rhetorical and topical variance, making proposed projects more closely resemble those that have recently succeeded.
“When used in proposal preparation, such tools may enhance fluency and alignment with prevailing norms while reducing rhetorical and topical variance, making proposed projects more closely resemble those that have recently succeeded (23, 26, 36).”
From The rise of large language models and the direction and impact of US federal research funding (2026) · cites this paper as [23]
LLMs are trained on large corpora of existing scientific text, including published papers and public records of funded projects, and may tend to produce high-probability, discipline-typical language.
“Because LLMs are trained on large corpora of existing scientific text, including published papers and public records of funded projects, they may tend to produce high-probability, discipline-typical language.”
From The rise of large language models and the direction and impact of US federal research funding (2026) · cites this paper as [23]
Little is known about how the rise of LLMs is reshaping the public funding landscape.
“Despite growing attention to the use of AI in science (15–27), little is known about how the rise of large language models (LLMs) is reshaping the public funding landscape.”
From The rise of large language models and the direction and impact of US federal research funding (2026) · cites this paper as [23]
Removing all AI-related terms identified in prior work at every stage of the estimation pipeline yields measures highly correlated with original estimates, and all main regression results are robust to this exclusion.
“we remove all AI-related terms identified in prior work (23) at every stage of the estimation pipeline and re-estimate α. The resulting measures remain highly correlated with the original estimates, and all main regression results are robust to this exclusion”
From The rise of large language models and the direction and impact of US federal research funding (2026) · cites this paper as [23]
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Self-citation
- Authors
- Gao, J., & Wang, D.
- Affiliations
- Northwestern University (US)
- Corresponding author
- Wang, Dashun
- Venue
- Nature Human Behaviour
- Published
- 2024-10-11
- Volume
- 8
- Issue
- 12
- Pages
- 2281-2292
- ISSN
- 2397-3374 (ISSN-L)
- OpenAlex
- W4403338646
- Cited by
- 81
- References
- 27
- Citations by year
- 2026: 49 · 2025: 28 · 2024: 3 · 2023: 1
- FWCI
- 22.37
- Type
- journal article
- Language
- en
- Open access
- green
- hub.hku.hk
- Licence
- Locations
- Nature Human Behaviour (publishedVersion)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- The HKU Scholars Hub (University of Hong Kong) (submittedVersion, cc-by-nc-nd)
- Funders
- National Science Foundation (SBE 1829344, TIP 1123649-464363//2241237, TIP 2404035)
- Alfred P. Sloan Foundation (G-2019-12485)
- Peter G. Peterson Foundation
- Topics
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › scientometrics and bibliometrics research
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Keywords
- artificial intelligence in science, measurement framework, artificial intelligence in education, gender inequality in science, research equity
- MeSH terms
- Artificial Intelligence, Humans, Research, Science
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