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

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  • Thomas Davidson
  • Daniel Karell

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.

Suggested Citation

  • Thomas Davidson & Daniel Karell, 2025. "Integrating Generative Artificial Intelligence into Social Science Research: Measurement, Prompting, and Simulation," Sociological Methods & Research, , vol. 54(3), pages 775-793, August.
    • Handle: RePEc:sae:somere:v:54:y:2025:i:3:p:775-793
    DOI: 10.1177/00491241251339184
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    References listed on IDEAS

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    1. Kreps, Sarah & McCain, R. Miles & Brundage, Miles, 2022. "All the News That’s Fit to Fabricate: AI-Generated Text as a Tool of Media Misinformation," Journal of Experimental Political Science, Cambridge University Press, vol. 9(1), pages 104-117, March.
    2. Arthur Spirling, 2023. "Why open-source generative AI models are an ethical way forward for science," Nature, Nature, vol. 616(7957), pages 413-413, April.
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    Cited by:

    1. Bohan Zhang & Jiaxuan Li & Ali Hortac{c}su & Xiaoyang Ye & Victor Chernozhukov & Angelo Ni & Edward W Huang, 2025. "Agentic Economic Modeling," Papers 2510.25743, arXiv.org, revised Mar 2026.

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