‘Why should students learn a skill that will become redundant?’: Teaching Quantitative Social Science in an AI era

Ross John Hume
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Ross John Hume: University of Edinburgh

No mj57s_v1, SocArXiv from Center for Open Science

Abstract: This report comprises a review of the state of the art concerning Generative AI’s impact upon quantitative social science education. Insights are combined with results of a survey of undergraduate social science students to suggest initial recommendations and avenues for further discussion. While policy recommendations are based in the context of the University of Edinburgh, the literature review and analysis presented are applicable to teaching statistics and data science in general, particularly through the lens of social science. Findings suggest that while broadly current AI models remain a procedural tool and less reliable for methodological judgements, the educational impact of AI is largely dependent upon its use cases and the educational objectives set. Students expressed different perspectives from wanting to avoid the technology to wanting to gain AI-enabled skills for their future career. Findings reflect a broader question about the purpose of university education in an AI world. However, students broadly agree on a desire for clear-cut AI policies to follow rather than simply more restrictive or permissive ones.

Date: 2026-10-19
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Persistent link: https://EconPapers.repec.org/RePEc:osf:socarx:mj57s_v1

DOI: 10.31235/osf.io/mj57s_v1

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