Generative AI and academic scientists in US universities: Perception, experience, and adoption intentions - Wenceslao Arroyo-Machado; Jinghuan Ma; Tipeng Chen; Timothy P. Johnson; Shaika Islam; Lesley Michalegko; Eric Welch (PLoS ONE, 2025)
The integration of generative Artificial Intelligence (AI) into academia has sparked interest and debate among academic scientists. This paper explores the early adoption and perceptions of US academic scientists regarding the use of generative AI in teaching and research activities. To do so, this analysis focuses exclusively on STEM fields due to their high exposure to rapid technological advancements. Drawing from a nationally representative survey of 232 respondents, we examine academic scientists’ attitudes, experiences, and intentions regarding AI adoption. Results indicate that 65% of respondents have utilized generative AI in teaching or research activities, with 20% applying it in both areas. Among those currently using AI, 84% intend to continue its application, indicating a high level of confidence in its perceived benefits. AI is most frequently used in teaching to develop pedagogical materials (51%) and in research for writing, reviewing, and editing tasks (40%). Despite concerns about misinformation, with 78% of respondents indicating it as their top concern regarding AI, there is broad recognition of AI’s potential impact on society. Most academic scientists have already integrated AI into their academic activities, demonstrating cautious yet optimistic adoption due to perceived risks. Furthermore, there is strong support for academic-led regulation of AI, highlighting the need for responsible governance to maximize benefits while minimizing risks in educational and research settings.