Scientific exploration, collaboration and labor division in the large language model era

2026-07-23

AI Summary

This study investigates how the widespread adoption of large language models (LLMs) is reshaping scientists’ research direction choices, collaboration patterns, and role allocation. Leveraging data from PubMed Central and OpenAlex—including publication records of over 770,000 researchers and CRediT contributorship statements from 137,000 multi-author papers—the authors employ bibliometric analysis, AI-writing signal detection, role annotation parsing, and network modeling to systematically uncover structural shifts in scientific organization in the LLM era. Findings reveal that since 2022, researchers have engaged more frequently in interdisciplinary exploration, with collaboration networks exhibiting greater disciplinary diversity. Higher intensity of AI use correlates with increased interdisciplinarity but reduced complementarity among co-authors’ disciplinary backgrounds. Furthermore, team roles have become more granular and fluid, with growing emphasis on software development and validation roles, while conceptualization and project management roles have relatively declined.

Abstract

Large language models (LLMs) have rapidly and significantly entered scientific workflows, but it remains unclear how their diffusion is associated with changes in scientists' strategies in research directions and team building. We link PubMed Central full text with OpenAlex publication and collaboration histories for 775,323 scientists and analyze CRediT contribution statements from 137,120 multi-author papers. After 2022, scientists increasingly published across more intellectually distant fields and entered fields in which they had not previously worked. These increases in interdisciplinarity and exploration were especially pronounced among established scientists and scientists from non-English-speaking low- and middle-income countries. Authors with stronger AI-writing signals were already more interdisciplinary and exploratory before the widespread adoption of LLMs, and the gap widened further after 2022 compared with authors with weaker AI-writing signals. Scientists' collaboration networks also became more interdisciplinary after 2022. Yet, among authors with stronger AI-writing signals, research interdisciplinarity was less closely tied to the disciplinary diversity of their collaborators. The division of labor within research teams also became more differentiated. Contributors on papers published after 2022 reported narrower role sets on average, coauthors shared fewer roles in common, and their role profiles became less rigid and more fluid. Software and validation roles increased, while conceptual and management roles decreased. These patterns suggest that team members are taking on more distinct responsibilities and may rely less on one another to perform research tasks. Overall, this study indicates that the LLM era coincides with a broader reorganization of scientific exploration, collaboration, and the division of labor.

Problem

• large language models
• scientific collaboration
• interdisciplinarity
• division of labor
• research workflows

Innovation

• large language models
• scientific collaboration
• interdisciplinarity
• division of labor
• CRediT roles