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

TL;DR

This paper investigates how large language models (LLMs) influence scientific knowledge production, highlighting their role in fostering innovation and reorganizing research practices within scientific communities.

Contribution

It introduces an evaluation workflow combining insider-outsider perspectives with a knowledge production framework to quantify LLM impacts on science.

Findings

LLMs catalyze innovation in scientific communities.

LLMs lead to reorganization of research practices.

The study offers insights into AI-driven transformation of knowledge production.

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.