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AI-Generated Data as Epistemic Artifacts: Insights from Quantitative Methods Education

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  • Laura Arosio

    (Department of Sociology and Social Research, University of Milano-Bicocca, 20126 Milan, Italy)

Abstract

This article critically examines the use of generative AI (specifically ChatGPT-4) as a tool for designing teaching materials in university courses on quantitative social research methods. It is conceived as a concept paper grounded in an illustrative, AI-assisted co-design session. The purpose is not to evaluate learning outcomes or produce generalizable empirical findings, but to develop a theory-informed analytical framework for examining AI-generated materials as epistemic artifacts. The analysis illustrates how seemingly neutral AI outputs embed specific assumptions and can actively shape the way social research is approached, intensifying constitutive methodological conventions. By critically unpacking the simulated outputs, the article proposes a framework for integrating AI-generated content into quantitative methods education as an object of critical inquiry.

Suggested Citation

  • Laura Arosio, 2026. "AI-Generated Data as Epistemic Artifacts: Insights from Quantitative Methods Education," Societies, MDPI, vol. 16(6), pages 1-21, June.
    • Handle: RePEc:gam:jsoctx:v:16:y:2026:i:6:p:186-:d:1965469
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