AI-assisted writing and the reorganization of scientific knowledge
Erjia Yan, Chaoqun Ni

TL;DR
This study investigates how AI-assisted writing influences scientific disruption and knowledge recombination, revealing a shift post-2023 towards more disruptive citation patterns without broader cross-field integration.
Contribution
It provides empirical evidence on the changing relationship between AI-assisted writing and scientific knowledge organization over time.
Findings
Post-2023, AI-assisted writing correlates with increased scientific disruption.
Before 2023, higher AI-assisted writing is weakly or negatively linked to disruption.
AI-assisted writing is associated with more disruptive citation structures without expanding cross-field knowledge.
Abstract
Generative AI systems such as ChatGPT are increasingly used in scientific writing, yet their broader implications for the organization of scientific knowledge remain unclear. We examine whether AI-assisted writing intensity, measured as the share of text in a paper that is predicted to exhibit features consistent with LLM-generated text, is associated with scientific disruption and knowledge recombination. Using approximately two million full-text research articles published between 2021 and 2024 and linked to citation networks, we document a sharp temporal pattern beginning in 2023. Before 2023, higher AI-assisted writing intensity is weakly or negatively associated with disruption; after 2023, the association becomes positive in within-author, within-field analyses. Over the same period, the positive association between AI-assisted writing intensity and cross-field citation breadth…
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