Chatbots are changing who wins research grants, study finds

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Chatbots are changing who wins research grants, study finds

While AI is increasing the quantity of studies scientists can produce, it may also be narrowing the breadth of discovery

EVANSTON, Ill. - A new study from Northwestern's Kellogg School of Management found that research proposals showing stronger signs of AI-assisted writing were four percentage points more likely to receive funding from the National Institutes of Health (NIH).

The study, led by researchers Dashun Wang and Yifan Qian, uncovered a concerning tradeoff. Those same AI-assisted proposals tended to resemble ideas the agency had already funded, raising questions about whether AI could gradually steer scientific funding toward safer, more conventional research.

"Science advances by exploring ideas that don't yet look obvious," Wang said. "If AI increasingly learns from yesterday's successful proposals, one of the questions we should ask is whether tomorrow's scientific portfolio becomes less adventurous."

Continuous policy changes at NIH and the U.S. National Science Foundation (NSF) have created a more challenging landscape for scientists to get their research funded.

Qian added: "These patterns we documented in this study indicate that large language model (LLM) use is already reshaping how scientific ideas are articulated and evaluated in the federal funding system, with implications for research diversity, transparency and public trust in the stewardship of taxpayer-supported science."

Wang is chair of technology and a professor of management and organizations at Kellogg; professor of industrial engineering and management sciences at McCormick School of Engineering; and director of Kellogg's Center for Science of Science and Innovation (CSSI) and the Northwestern Innovation Institute, as well as co-director of Kellogg's Ryan Institute on Complexity. Qian is a research assistant professor at CSSI.

The study, "The Rise of Large Language Models and the Direction and Impact of US Federal Research Funding," will be published on August 11 in the journal Proceedings of the National Academy of Sciences (PNAS). It's among the first to examine how LLMs are influencing federal research funding - not after discoveries are made, but at the point where scientists compete for the resources to pursue them.

Results

The researchers found that LLM use rose sharply after the public release of ChatGPT in late 2022. Rather than gradual adoption, grant writing quickly split into two groups: one showing little evidence of AI assistance and another relying on it much more extensively.

Moreover, proposals with greater LLM involvement consistently appeared less semantically distinctive - how distinct a grant is relative to recently funded work. Compared with proposals written with less AI assistance, they were more closely aligned with ideas agencies had already previous funded. The pattern appeared not only in confidential proposals but also in funded awards.

At NIH, proposals with stronger signs of AI involvement were more likely to receive funding. Funded projects also produced more follow-on publications, papers that acknowledge the award, but not more highly cited "hit" papers, suggesting that AI may improve research productivity without necessarily increasing breakthrough discoveries.

The pattern looked very different at NSF. There, the researchers found no significant relationship between AI use and either funding success or follow-on publication output,

The findings shift the conversation about AI in science beyond concerns over whether researchers should use generative AI to write grant proposals, the researchers said. Instead, it points to a broader question about what future scientific discovery looks like.

"A central concern in science policy is maintaining a diverse and exploratory research portfolio, one that supports both cumulative progress and the pursuit of unconventional ideas," Qian said. "Across both agencies and both stages - proposal submission and award funding - higher LLM involvement is consistently associated with lower semantic distinctiveness. A portfolio that is closer to recent funding patterns may reflect improved clarity and tighter alignment with reviewer expectations, but it also implies reduced exploration in the idea landscape, which matters for public funders explicitly tasked with sustaining high-variance discovery, with implications for long-run impact and sustainability of science."

As for why proposals with higher LLM involvement are more likely to be funded by NIH, Qian said that he and his team can only speculate.

"To the best of our knowledge, both NIH and NSF rely on human peer review," Qian said. "One potential explanation for why the human reviewers are siding with the AI-assisted abstracts could simply be that review norms may more strongly reward incremental, executable projects that yield multiple publications, and LLM-assisted drafting may help proposals conform to those established templates."

Researchers also pointed out that federal funding shapes the future of science long before papers are published or discoveries are made.

"Federal research funding is the primary mechanism through which the United States converts public resources into scientific knowledge," Qian said. "Understanding forces that influence this federal funding process is therefore essential not only for science policy and the rate and direction of scientific progress, but also for the stewardship and accountability of public investment in research."

In addition to Wang and Qian, co-authors include Zhe Wen, Alexander Furnas, Yue Bai and Erzhuo Shao of Northwestern University.