Changes in Manuscript Length, Research Team Size, and International Collaboration in the Post-2022 Period: Evidence from PLOS ONE

2026-03-02

AI Summary

This study investigates how the widespread adoption of large language models (LLMs) in academic writing since 2022 has reshaped the structure of scholarly output. Leveraging the interdisciplinary scope and uniform peer-review standards of PLOS ONE, the analysis draws on a corpus of 109,393 articles published between 2019 and 2025, employing bibliometric analysis, group comparisons, and regression discontinuity design. The findings reveal, for the first time in large-scale real-world publication data, that non-native English-speaking authors exhibit significantly increased manuscript lengths, reduced team sizes, and decreased collaboration with native English-speaking researchers following LLM adoption, while the latter group shows no such changes. These results indicate that AI tools are structurally transforming the academic writing ecosystem and the dynamics of international research collaboration.

Technology Category

Natural Language Processing: (Large) Language ModelsMachine Learning: Large Multimodal Models (LMMs)Humans and AI: Human-AI Collaboration / Human-AI Teaming

Application Category

User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendationSocial Networks and Social Media: Generative AI / large language models and their impact on social systemsGraph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Large language models (LLMs) have diffused rapidly into academic writing since late 2022. Using the complete population of 109,393 research articles published in \textit{PLOS ONE} between 2019 and 2025, we examine population-level structural publication indicators, including full-text manuscript length, authorship team size, reference volume, and cross-linguistic collaboration, before and after 2022. \textit{PLOS ONE}'s multidisciplinary scope and consistent editorial framework allow cross-field comparison under uniform conditions over an extended period. Manuscript length increased substantially, with gains ranging from 14.8\% among African-affiliated authors and 11.7\% among Asian-affiliated authors to 5.3\% among native English-speaking (NES) authors, cutting the word-count gap by 39\%. More strikingly, non-native English-speaking (NNES) authors reduced both authorship team size, from 6.54 to 6.06 authors, or 7.3\%, and collaboration with NES co-authors, from 17.8\% to 12.2\%, or 36\%, while NES authors remained stable in both team size and collaboration rates. Reference counts increased modestly and uniformly across groups. These findings suggest that post-2022 tools may be reshaping not only how science is written, but who writes it together.

Problem

• large language models
• manuscript length
• research team size
• international collaboration
• academic writing

Innovation

• large language models
• academic writing
• international collaboration
• authorship team size
• manuscript length