mlsanityData to 14 Oct 2026

Generative AI in Sociological Research: State of the Discipline

arXiv 2511.16884 ↗First posted 21 Nov 2025cs.CY cs.AIcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 14 of its 82 references that are in the map. First posted on arXiv 21 Nov 2025.

Builds on

14 of 82 references.

References · 14

  1. 2023ChatGPT Outperforms Crowd-Workers for Text-Annotation Taskscited by 870
  2. 2022Out of One, Many: Using Language Models to Simulate Human Samplescited by 752
  3. 2021Between words and characters: A Brief History of Open-Vocabulary Modeling and Tokenization in NLPcited by 132
  4. 2025LLM Social Simulations Are a Promising Research Methodcited by 99
  5. 2024Safeguarding Large Language Models: A Surveycited by 78
  6. 2023AI-Augmented Surveys: Leveraging Large Language Models and Surveys for Opinion Predictioncited by 48
  7. 2024A Shocking Amount of the Web is Machine Translated: Insights from Multi-Way Parallelismcited by 35
  8. 2025The Shrinking Landscape of Linguistic Diversity in the Age of Large Language Modelscited by 29
  9. 2023Using Large Language Models for Qualitative Analysis can Introduce Serious Biascited by 22
  10. 2023Navigating Prompt Complexity for Zero-Shot Classification: A Study of Large Language Models in Computational Social Sciencecited by 19
  11. 2025Navigating the Risks of Using Large Language Models for Text Annotation in Social Science Researchcited by 4
  12. 2024The Social Impact of Generative LLM-Based AIcited by 3
2 more
  1. 2024The Heterogeneous Productivity Effects of Generative AIcited by 2
  2. 2024Generative artificial intelligence usage by researchers at work: Effects of gender, career stage, type of workplace, and perceived barrierscited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026Selecting Language Models for Social Science: Start Small, Start Open, and Validatecited by 0
  2. 2026Validating LLMs in social science: Epistemic threats and emerging normscited by 0