Integrating Generative Artificial Intelligence into Social Science Research: Measurement, Prompting, and Simulation

Thomas Davidson and Daniel Karell

Sociological Methods & Research, 2025, vol. 54, issue 3, 775-793

Abstract: Generative artificial intelligence (AI) offers new capabilities for analyzing data, creating synthetic media, and simulating realistic social interactions. This essay introduces a special issue that examines how these and other affordances of generative AI can advance social science research. We discuss three core themes that appear across the contributed articles: rigorous measurement and validation of AI-generated outputs, optimizing model performance and reproducibility via prompting, and novel uses of AI for the simulation of attitudes and behaviors. We highlight how generative AI enable new methodological innovations that complement and augment existing approaches. This essay and the special issue’s ten articles collectively provide a detailed roadmap for integrating generative AI into social science research in theoretically informed and methodologically rigorous ways. We conclude by reflecting on the implications of the ongoing advances in AI.

Keywords: computational sociology; generative artificial intelligence; large language models; simulation; prompting; measurement‌ (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:sae:somere:v:54:y:2025:i:3:p:775-793

DOI: 10.1177/00491241251339184

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