The Formation of AI Capital in Higher Education: Enhancing Students’ Academic Performance and Employment Rates

Nick Drydakis ()
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Nick Drydakis: Anglia Ruskin University

No 18138, IZA Discussion Papers from Institute of Labor Economics (IZA)

Abstract: The study evaluates the effectiveness of a 12-week AI module delivered to non-STEM university students in England, aimed at building students’ AI Capital. An integral part of the process involved the development and validation of the AI Capital of Students scale, used to measure AI Capital before and after the educational intervention. The module was delivered on four occasions to final-year students between 2023 and 2024, with follow-up data collected on students’ employment status. Moreover, AI Capital is positively associated with academic performance in AI-related coursework. However, disparities persist.students, White students, and those with stronger backgrounds in mathematics and empirical methods achieved higher levels of AI Capital and academic success. Furthermore, enhanced AI Capital is associated with higher employment rates six months after graduation. To provide a theoretical foundation for this pedagogical intervention, the study introduces and validates the AI Learning–Capital–Employment Transition model, which conceptualises the pathway from structured AI education to the development of AI Capital and, in turn, to improved employment outcomes.

Keywords: university students; AI Capital; AI literacy; Artificial Intelligence; grades; academic performance; employment rates (search for similar items in EconPapers)
JEL-codes: I21 I23 I24 J15 J16 J21 J24 O15 O33 (search for similar items in EconPapers)
Date: 2025-09
New Economics Papers: this item is included in nep-edu, nep-eur and nep-lma
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