Generative AI in Sociological Research: State of the Discipline
AJ Alvero, Dustin S. Stoltz, Oscar Stuhler, Marshall Taylor
TL;DR
The paper provides the first systematic empirical baseline on how sociologists and their collaborators currently use GenAI and how they view its future role. Using a large, weighted survey of 433 respondents from 50 sociology journals, the authors document that GenAI is predominantly used for writing assistance, with modest adoption among computational and non-computational scholars alike. They report high concerns about social, environmental, and data-related risks and very low trust in GenAI outputs, alongside cautious optimism about eventual improvements. The study underscores the need to establish normative guidelines and monitoring as GenAI tools diffuse through sociological research practice.
Abstract
Generative artificial intelligence (GenAI) has garnered considerable attention for its potential utility in research and scholarship. A growing body of work in sociology and related fields demonstrates both the potential advantages and risks of GenAI, but these studies are largely proof-of-concept or specific audits of models and products. We know comparatively little about how sociologists actually use GenAI in their research practices and how they view its present and future role in the discipline. In this paper, we describe the current landscape of GenAI use in sociological research based on a survey of authors in 50 sociology journals. Our sample includes both computational sociologists and non-computational sociologists and their collaborators. We find that sociologists primarily use GenAI to assist with writing tasks: revising, summarizing, editing, and translating their own work. Respondents report that GenAI saves time and that they are curious about its capabilities, but they do not currently feel strong institutional or field-level pressure to adopt it. Overall, respondents are wary of GenAI's social and environmental impacts and express low levels of trust in its outputs, but many believe that GenAI tools will improve over the next several years. We do not find large differences between computational and non-computational scholars in terms of GenAI use, attitudes, and concern; nor do we find strong patterns by familiarity or frequency of use. We discuss what these findings suggest about the future of GenAI in sociology and highlight challenges for developing shared norms around its use in research practice.