Generative AI in Sociological Research: State of the Discipline

2025-11-20

AI Summary

Despite growing adoption of generative artificial intelligence (GenAI) in academia, empirical evidence on its integration into sociological research—and scholars’ corresponding attitudes—remains scarce. Method: This study conducts the first systematic investigation through a large-scale survey of 433 authors from 50 leading sociology journals published over the past five years, employing descriptive statistics and cross-tabular analysis. Results: Approximately one-third of respondents use GenAI weekly, predominantly for writing assistance; no significant differences emerge between computational and non-computational researchers regarding usage frequency, purposes, or attitudes; overall trust in GenAI remains low, yet expectations for improvement are strong; scholars express pronounced concerns about environmental and societal risks, while opinions diverge sharply on its disciplinary impact. This work establishes a critical empirical baseline and reflective framework for integrating GenAI into the humanities and social sciences, addressing a key gap in sociological AI scholarship.

Technology Category

• Application Domains: Humanities & Computational Social Science
• Philosophy and Ethics of AI: Societal Impact of AI
• Humans and AI: Other Foundations of Human Computation & AI

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Generative artificial intelligence (GenAI) has garnered considerable attention for its potential utility in research and scholarship, even among those who typically do not rely on computational tools. Early commentators, however, have also articulated concerns about how GenAI usage comes with enormous environmental costs, serious social risks, and a tendency to produce low-quality content. In the midst of both excitement and skepticism, it is crucial to take stock of how GenAI is actually being used. Our study focuses on sociological research as our site, and here we present findings from a survey of 433 authors of articles published in 50 sociology journals in the last five years. The survey provides an overview of the state of the discipline with regard to the use of GenAI by providing answers to fundamental questions: how (much) do scholars use the technology for their research; what are their reasons for using it; and how concerned, trustful, and optimistic are they about the technology? Of the approximately one third ofrespondents who self-report using GenAI at least weekly, the primary uses are for writing assistance and comparatively less so in planning, data collection, or data analysis. In both use and attitudes, there are surprisingly few differences between self-identified computational and non-computational researchers. Generally, respondents are very concerned about the social and environmental consequences of GenAI. Trust in GenAI outputs is low, regardless of expertise or frequency of use. While optimism that GenAI will improve is high, scholars are divided on whether GenAI will have a positive impact on the field.

Problem

• Assessing actual GenAI usage patterns among sociology researchers
• Investigating scholar attitudes about trust and concerns regarding GenAI
• Examining differences between computational and non-computational researchers

Innovation

• Surveyed 433 sociology authors on GenAI usage
• Focused on writing assistance over data tasks
• Assessed concerns about social environmental impacts