Adapting to LLMs: How Insiders and Outsiders Reshape Scientific Knowledge Production

Huimin Xu; Houjiang Liu; Yan Leng; Ying Ding

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arXiv · 2505.12666

Adapting to LLMs: How Insiders and Outsiders Reshape Scientific Knowledge Production

Huimin Xu·Houjiang Liu·Yan Leng·Ying Ding

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Abstract

CSCW has long examined how emerging technologies reshape the ways researchers collaborate and produce knowledge, with scientific knowledge production as a central area of focus. As AI becomes increasingly integrated into scientific research, understanding how researchers adapt to it reveals timely opportunities for CSCW research -- particularly in supporting new forms of collaboration, knowledge practices, and infrastructure in AI-driven science. This study quantifies LLM impacts on scientific knowledge production based on an evaluation workflow that combines an insider-outsider perspective with a knowledge production framework. Our findings reveal how LLMs catalyze both innovation and reorganization in scientific communities, offering insights into the broader transformation of knowledge production in the age of generative AI and sheds light on new research opportunities in CSCW.

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Huimin Xu, Houjiang Liu, Yan Leng, Ying Ding. 2025-05-19. Adapting to LLMs: How Insiders and Outsiders Reshape Scientific Knowledge Production. https://arxiv.org/abs/2505.12666

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