Field diffusion · General science · Social science · Computer science
Study quantifies AI use and citation-associated benefits across science
A peer-reviewed measurement framework for direct AI use and citation-associated benefits across 19 scientific disciplines and 292 fields.
Summary
Gao and Wang developed a text-based framework to measure direct AI use in scientific publications and its association with citation impact. Across a large historical corpus, they find rapidly increasing use since 2015 and widespread but uneven citation-associated benefits, alongside gaps between AI training and research use and demographic disparities across disciplines.
AI role
AI methods are identified from publication text and their use is compared with subsequent citation impact within and outside disciplines.
Narrative role
This fills a historical field-impact gap with an early peer-reviewed, science-wide measurement of both AI diffusion and heterogeneous research benefits, providing a baseline for the tracker's later OpenAlex, AlphaFold, and post-LLM studies.
Caveat
AI use is inferred from publication text and benefits are measured through citations, so the observational associations do not establish causal productivity or discovery effects; the corpus ends before widespread generative-AI use.