Drivers and Barriers of AI Adoption and Use in Scientific Research

TL;DR

This study identifies key drivers and barriers influencing AI adoption in scientific research, highlighting the roles of human capital, collaboration networks, and institutional resources across diverse scientific disciplines from 1980 to 2020.

Contribution

It empirically analyzes the factors affecting AI adoption in science using a large publication dataset, integrating theories of scientific and technical human capital.

Findings

AI adoption is driven by exploratory scientists embedded in collaborative networks.

Institutional reputation and publication history influence AI integration.

Access to computing resources impacts specific disciplines like chemistry and medicine.

Abstract

New technologies have the power to revolutionize science. It has happened in the past and is happening again with the emergence of new computational tools, such as artificial intelligence and machine learning. Despite the documented impact of these technologies, there remains a significant gap in understanding the process of their adoption within the scientific community. In this paper, we draw on theories of scientific and technical human capital to study the integration of AI in scientific research, focusing on the human capital of scientists and the external resources available within their network of collaborators and institutions. We validate our hypotheses on a large sample of publications from OpenAlex, covering all sciences from 1980 to 2020, and identify a set key drivers and inhibitors of AI adoption and use in science. Our results suggest that AI is pioneered by domain scientists with a `taste for exploration' and who are embedded in a network rich of computer scientists, experienced AI scientists and early-career researchers; they come from institutions with high citation impact and a relatively strong publication history on AI. The access to computing resources only matters for a few scientific disciplines, such as chemistry and medical sciences. Once AI is integrated into research, most adoption factors continue to influence its subsequent reuse. Implications for the organization and management of science in the evolving era of AI-driven discovery are discussed.