AI-assisted writing and the reorganization of scientific knowledge

2026-04-15

AI Summary

This study investigates how the widespread adoption of generative AI in scientific writing is reshaping the structure of scientific knowledge and patterns of innovation. Leveraging a corpus of approximately two million full-text papers and their citation networks from 2021 to 2024, combined with large language model–based text detection, large-scale textual analysis, and panel regression, the research reveals—for the first time—that prior to 2023, AI-assisted writing exhibited a weak negative association with scientific disruptiveness, which subsequently shifted to a significant positive correlation. However, this positive relationship is not accompanied by an increase in cross-disciplinary citation breadth; instead, it coincides with a deceleration in the decline of citation concentration, suggesting that AI may be fostering a novel mode of knowledge recombination rooted in a narrower set of knowledge sources.

Technology Category

• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Cognitive Modeling & Cognitive Systems: Computational Creativity
• Data Mining & Knowledge Management: Linked Open Data, Knowledge Graphs & KB Completion

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Web Mining and Content Analysis: Interdisciplinary science discovery with web data mining

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 weakens substantially, and the negative association with citation concentration attenuates. Thus, the post-2023 increase in disruption is not accompanied by broader knowledge sourcing. These patterns suggest that generative AI is associated with more disruptive citation structures without a corresponding expansion in cross-field recombination. Rather than simply broadening the search space of science, AI-assisted writing may be associated with new forms of recombination built from relatively narrower knowledge inputs.

Problem

• generative AI
• scientific writing
• knowledge recombination
• scientific disruption
• citation networks

Innovation

• generative AI
• scientific disruption
• knowledge recombination
• AI-assisted writing
• citation networks