Using AI boosts scientific productivity and career prospects, finds study

04 Feb 2026

Lab bot: AI chatbots are increasingly being used as collaborators in day-to-day research, from experiment planning and literature synthesis to mathematical reasoning and data analysis. (Courtesy: Shutterstock/Anggalih Prasetya)

Using artificial intelligence (AI) increases scientists’ productivity and impact but collectively leads to a shrinking of research focus. That is according to an analysis of more than 41 million research papers by scientists in China and the US, which finds that scientists who produce AI-augmented research also progress faster in their careers than their colleagues who do not (Nature 649 1237).

The study was conducted by James Evans, a sociologist at the University of Chicago, and his colleagues, who analysed 41.3 million papers listed in the OpenAlex dataset, published between 1980 and 2025. They examined papers in physics and five other disciplines: biology, chemistry, geology, materials science, and medicine.

Using an AI language model to identify AI-assisted work, the team picked out almost 310 000 AI-augmented papers from the dataset. They found that AI-supported publications receive more citations than non-AI-assisted papers, while also being more impactful across multiple indicators and having a higher prevalence in high-impact journals.

Individual researchers who adopt AI publish, on average, three times as many papers and get almost five times as many citations as those who do not use AI. In physics, researchers who use AI tools garner 183 citations every year, on average, while those who do not use AI get only 51 annually.

AI also boosts career trajectories. Based on an analysis of more than two million scientists in the dataset, the study finds that junior researchers who adopt AI are more likely to become established scientists. They also gain project leadership roles almost one-and-a-half years earlier, on average, than those who do not use AI.

Fundamental questions

However, when the researchers examined the spread of knowledge in a random sample of 10 000 papers, half of which used AI, they found that AI-produced work reduced the range of topics covered by almost 5%. The finding is consistent across all six disciplines. Furthermore, AI papers are more clustered than non-AI papers, suggesting a tendency to concentrate on specific problems.

Knowledge grows step-by-step despite the exponential growth of papers, finds study

AI tools, in other words, appear to funnel research towards areas rich in data and help to automate established fields rather than exploring new topics. Evans and colleagues think this AI-induced convergence could drive science away from foundational questions and towards data-rich operational topics.

AI could, however, help combat this trend. “We need to reimagine AI systems that expand not only cognitive capacity but also sensory and experimental capacity,” they say. “[This could] enable and incentivize scientists to search, select, and gather new types of data from previously inaccessible domains rather than merely optimizing analysis of standing data.”

Meanwhile, a new report by the AI company OpenAI finds that messages on advanced topics in science and mathematics on ChatGPT over the last year have increased by nearly 50%, to almost 8.4 million per week. The firm reports that its generative AI chatbot is used to advance research across scientific fields, from experimental planning and literature synthesis to mathematical reasoning and data analysis.

Michael Allen is a science writer based in the UK.

FROM PHYSICSWORLD.COM    5/2/2026