LLMs in Wikipedia: Investigating How LLMs Impact Participation in Knowledge Communities

Abstract

Large language models (LLMs) are reshaping knowledge production as community members increasingly incorporate them into their contribution workflows. At the same time, participating in knowledge communities involves more than just contributing content - it is also a deeply social process shaped by members’ level of expertise. Although communities must carefully consider appropriate and responsible LLM integration, the absence of concrete norms has left individual editors to experiment and navigate LLM use on their own. Understanding how LLMs influence community participation across expertise levels is therefore critical in shaping future norms and supporting effective adoption. To address this gap, we investigated Wikipedia, one of the largest knowledge production communities, to understand participation in three dimensions: 1) how LLMs influence the ways editors gather knowledge, 2) how editors leverage strategies to align LLM outputs with community norms, and 3) how other editors in the community respond to LLM-assisted contributions. Through interviews with 16 Wikipedia editors with different levels of expertise who had used LLMs for their edits, we revealed a participation gap mediated by expertise in adopting LLMs across knowledge gathering, community norm alignment, and peer responses. Based on these findings, we challenge existing models of novice editors’ involvement and propose design implications for LLMs to support community engagement, highlighting opportunities for LLMs to sustain mentorship, knowledge transmission, and legitimacy building by scaffolding and feedback, process documentation, and LLM disclosure by good-faith novice editors.