Generative AI as a Linguistic Equalizer in Global Science

These authors contributed equally to this work. For decades, the dominance of English has created a substantial barrier in global science, disadvantaging non-native speakers. The recent rise of generative AI (GenAI) offers a potential technological response to this long-standing inequity. We provide the first large-scale evidence testing whether GenAI acts as a linguistic equalizer in global science. Drawing on 5.65 million scientific articles published from 2021 to 2024, we compare GenAI-assisted and non-assisted publications from authors in non-English-speaking countries. Using text embeddings derived from a pretrained large language model (SciBERT), we measure each publication's linguistic similarity to a benchmark of scientific writing from U.S.-based authors and track stylistic convergence over time. We find significant and growing convergence for GenAI-assisted publications after the release of ChatGPT in late 2022. The effect is strongest for domestic coauthor teams from countries linguistically distant from English. These findings provide large-scale evidence that GenAI is beginning to reshape global science communication by reducing language barriers in research. The rapid rise of generative AI (GenAI) has sparked an important debate regarding its role in science--raising questions of whether it homogenizes writing and erodes authorship norms (1,2) or whether it acts as a "linguistic equalizer" that lowers barriers for non-native English speakers (3,4). This debate is especially salient because English has long dominated global science, which gives native speakers a structural advantage (5-7) by creating larger writing burdens and unique peer review bias risks for researchers from non-Anglophone countries (8-12). As a result, many of these researchers have historically spent time in the U.S. or the UK to learn how to write in English or have hired (expensive) language experts (13, 14). Against this backdrop, the release of ChatGPT in late 2022, a chatbot based on a large language model (LLM), marked a turning point. This widely accessible, low-cost, and human-like tool offers a potential means of reducing longstanding linguistic imbalances (15, 16).