Generative AI adoption and scientific output: Quasi-experimental evidence on heterogeneous changes across 26 disciplines
Year: 2026
Type: article
Abstract: Using monthly OpenAlex data on 92 million publications across 26 academic fields (2015–2025), we examine publication growth associated with large language model (LLM) adoption through a quasi-experimental difference-in-differences design around ChatGPT’s release. The pooled estimate is statistically indistinguishable from zero. However, fields with one standard deviation higher pre-existing AI intensity show 4.52 percentage points greater output growth per percentage point of measured adoption ( 𝑝 = 0 . 0 1 0 ). Expanded keyword definitions reduce this estimate to 1.21–1.75 percentage points. Computer Science shows an estimated cumulative output increase of 8.7% by late 2025. These findings indicate heterogeneous field-level publication responses, with magnitudes sensitive to how adoption is measured.
Source: Journal of Informetrics
Author: Andres Arcila
Institution: University of Waterloo
Language: English
Subfield: Library and Information Sciences
Field: Social Sciences
Domain: Social Sciences
Open Access status: hybrid
Estimated APC paid: $3,220
OpenAlex ID: W7134958845