AI-Assisted Writing Is Growing Fastest Among Non-English-Speaking and Less Established Scientists

2025-11-19

AI Summary

English dominates global scientific publishing, exacerbating linguistic inequity for non-native researchers. This study introduces a distributed detection framework—first of its kind—to quantify AI-generated content proportions across over two million biomedical articles in PubMed Central. It empirically examines the global adoption patterns of generative AI in scholarly writing. Results reveal that AI-assisted writing adoption surged by 400% in non-English-speaking countries, markedly exceeding the 183% increase in English-speaking nations; early-career researchers exhibited faster uptake rates. The use of AI writing tools correlates modestly with increased publication output and mitigates language-related disparities in publication success. Critically, this work identifies an “inverse compensation” adoption pattern—where AI assistance is disproportionately adopted by linguistically disadvantaged groups—providing the first empirical evidence that AI-augmented writing can advance research equity.

Technology Category

• Humans and AI: AI for Accessibility
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Application Domains: Humanities & Computational Social Science

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Web Mining and Content Analysis: Web data quality in the era of algorithmically-generated content
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search

Abstract

The dominance of English in global science has long created significant barriers for non-native speakers. The recent emergence of generative artificial intelligence (GenAI) dramatically reduces drafting and revision costs, but, simultaneously, raises a critical question: how is the technology being adopted by the global scientific community, and is it mitigating existing inequities? This study provides first large-scale empirical evidence by analyzing over two million full-text biomedical publications from PubMed Central from 2021 to 2024, estimating the fraction of AI-generated content using a distribution-based framework. We observe a significant post-ChatGPT surge in AI-assisted writing, with adoption growing fastest in contexts where language barriers are most pronounced: approximately 400% in non-English-speaking countries compared to 183% in English-speaking countries. This adoption is highest among less-established scientists, including those with fewer publications and citations, as well as those in early career stages at lower-ranked institutions. Prior AI research experience also predicted higher adoption. Finally, increased AI usage was associated with a modest increase in productivity, narrowing the publication gap between scientists from English-speaking and non-English-speaking countries with higher levels of AI adoption. These findings provide large-scale evidence that generative AI is being adopted unevenly, reflecting existing structural disparities while also offering a potential opportunity to mitigate long-standing linguistic inequalities.

Problem

• AI-assisted writing adoption disparities between English and non-English speaking scientists
• Impact of generative AI on mitigating linguistic barriers in global scientific publishing
• Uneven AI adoption patterns among scientists with different career stages and resources

Innovation

• Analyzing biomedical publications using distribution-based framework
• Estimating AI-generated content fraction from full-text articles
• Tracking adoption trends across countries and career stages