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

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.

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Publication Details

Journal
Sociological Methods & Research
Published
2025-05-07
DOI
https://doi.org/10.1177/00491241251339184
Citations
10
Primary Topic
Computational and Text Analysis Methods
Type
article
Field-Weighted Citation Impact
53.42
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article

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

Thomas Davidson, Daniel Karell
10 citations
Sociological Methods & Research
Computational and Text Analysis Methods
53.42
article

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

Thomas Davidson, Daniel Karell
article en
10 citations

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.

Sociological Methods & ResearchVol. 54(3)
Rutgers, The State University of New Jersey (US), Yale University (US)
Openalex Percentile: Top 0%
Computational and Text Analysis Methods
53.42
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