Drivers and barriers of AI adoption and use in scientific research

Stefano Bianchini (), Moritz Müller and Pierre Pelletier
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Stefano Bianchini: BETA - Bureau d'Économie Théorique et Appliquée - AgroParisTech - UNISTRA - Université de Strasbourg - Université de Haute-Alsace (UHA) - Université de Haute-Alsace (UHA) Mulhouse - Colmar - UL - Université de Lorraine - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement
Moritz Müller: BETA - Bureau d'Économie Théorique et Appliquée - AgroParisTech - UNISTRA - Université de Strasbourg - Université de Haute-Alsace (UHA) - Université de Haute-Alsace (UHA) Mulhouse - Colmar - UL - Université de Lorraine - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement
Pierre Pelletier: BETA - Bureau d'Économie Théorique et Appliquée - AgroParisTech - UNISTRA - Université de Strasbourg - Université de Haute-Alsace (UHA) - Université de Haute-Alsace (UHA) Mulhouse - Colmar - UL - Université de Lorraine - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement

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Abstract: We study the early adoption and use of artificial intelligence (AI) in scientific research. Using a large dataset of publications from OpenAlex (all fields, up to 2024) and building on theories of scientific and technical human capital, we identify key factors that influence AI adoption. We find that early adopters were domain scientists embedded in AI-rich collaboration networks and affiliated with institutions with strong AI credentials. Access to high-performance computing (HPC) mattered only in a few scientific disciplines, such as biology and medical sciences. More recently, as tools like Large Language Models (LLMs) have diffused, AI has become more accessible, and institutional advantages appear to matter less. However, social capital—especially ties to AI-experienced collaborators and early-career researchers—remains a persistent driver of adoption. We discuss the implications for science policy and the organization of research in the age of AI.

Keywords: Economics of science; Scientific discovery; Technology adoption; Artificial intelligence (search for similar items in EconPapers)
Date: 2025-11
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Published in Technological Forecasting and Social Change, 2025, 220, pp.124303. ⟨10.1016/j.techfore.2025.124303⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-05795699

DOI: 10.1016/j.techfore.2025.124303

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