Generative AI in Sociological Research: State of the Discipline

TL;DR

This study surveys sociologists to understand how they currently use GenAI, their attitudes towards it, and potential future impacts, revealing cautious adoption mainly for writing assistance and concerns about trust and social implications.

Contribution

It provides the first comprehensive survey-based analysis of sociologists' use, perceptions, and attitudes towards GenAI in research practices.

Findings

Sociologists mainly use GenAI for writing tasks like revising and summarizing.

Respondents are curious about GenAI but do not feel strong institutional pressure to adopt it.

Participants express concerns about social and environmental impacts and trust in GenAI outputs.

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.