# Generative AI in Sociological Research: State of the Discipline

arXiv: https://arxiv.org/abs/2511.16884 · HTML: https://mlsanity.com/paper/2511.16884/

## 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 (most cited first, 14 of 14)

- [ChatGPT Outperforms Crowd-Workers for Text-Annotation Tasks](https://mlsanity.com/paper/2303.15056/) (2023)
- [Out of One, Many: Using Language Models to Simulate Human Samples](https://mlsanity.com/paper/2209.06899/) (2022)
- [Between words and characters: A Brief History of Open-Vocabulary Modeling and Tokenization in NLP](https://mlsanity.com/paper/2112.10508/) (2021)
- [LLM Social Simulations Are a Promising Research Method](https://mlsanity.com/paper/2504.02234/) (2025)
- [Safeguarding Large Language Models: A Survey](https://mlsanity.com/paper/2406.02622/) (2024)
- [AI-Augmented Surveys: Leveraging Large Language Models and Surveys for Opinion Prediction](https://mlsanity.com/paper/2305.09620/) (2023)
- [A Shocking Amount of the Web is Machine Translated: Insights from Multi-Way Parallelism](https://mlsanity.com/paper/2401.05749/) (2024)
- [The Shrinking Landscape of Linguistic Diversity in the Age of Large Language Models](https://mlsanity.com/paper/2502.11266/) (2025)
- [Using Large Language Models for Qualitative Analysis can Introduce Serious Bias](https://mlsanity.com/paper/2309.17147/) (2023)
- [Navigating Prompt Complexity for Zero-Shot Classification: A Study of Large Language Models in Computational Social Science](https://mlsanity.com/paper/2305.14310/) (2023)
- [Navigating the Risks of Using Large Language Models for Text Annotation in Social Science Research](https://mlsanity.com/paper/2503.22040/) (2025)
- [The Social Impact of Generative LLM-Based AI](https://mlsanity.com/paper/2410.21281/) (2024)
- [The Heterogeneous Productivity Effects of Generative AI](https://mlsanity.com/paper/2403.01964/) (2024)
- [Generative artificial intelligence usage by researchers at work: Effects of gender, career stage, type of workplace, and perceived barriers](https://mlsanity.com/paper/2409.14570/) (2024)

## Built on by (most cited first, 2 of 2)

- [Selecting Language Models for Social Science: Start Small, Start Open, and Validate](https://mlsanity.com/paper/2601.10926/) (2026)
- [Validating LLMs in social science: Epistemic threats and emerging norms](https://mlsanity.com/paper/2607.07915/) (2026)

## 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.