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

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  • Drydakis, Nick

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, encompassing AI-related knowledge, skills, and capabilities. 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. The findings indicate that AI learning enhances students' AI Capital across all three dimensions. Moreover, AI Capital is positively associated with academic performance in AI-related coursework. However, disparities persist. Although all demographic groups experienced progress, male 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. The model integrates pedagogical, empirical and equity-centred perspectives, offering a practical framework for curriculum design and digital inclusion. The study highlights the importance of targeted interventions, inclusive pedagogy, and the integration of AI across curricula, with support tailored to students' prior academic experience.

Suggested Citation

  • Drydakis, Nick, 2025. "The Formation of AI Capital in Higher Education: Enhancing Students' Academic Performance and Employment Rates," GLO Discussion Paper Series 1668, Global Labor Organization (GLO).
    • Handle: RePEc:zbw:glodps:1668
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    References listed on IDEAS

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    1. Nick Drydakis, 2022. "Artificial Intelligence and Reduced SMEs’ Business Risks. A Dynamic Capabilities Analysis During the COVID-19 Pandemic," Information Systems Frontiers, Springer, vol. 24(4), pages 1223-1247, August.
    2. Andrew Bell & Malcolm Fairbrother & Kelvyn Jones, 2019. "Fixed and random effects models: making an informed choice," Quality & Quantity: International Journal of Methodology, Springer, vol. 53(2), pages 1051-1074, March.
    3. Gary S. Becker, 1964. "Human Capital: A Theoretical and Empirical Analysis with Special Reference to Education, First Edition," NBER Books, National Bureau of Economic Research, Inc, number beck-5, December.
    4. Nick Drydakis, 2024. "Artificial intelligence capital and employment prospects," Oxford Economic Papers, Oxford University Press, vol. 76(4), pages 901-919.
    5. Drydakis, Nick, 2016. "The effect of university attended on graduates’ labour market prospects: A field study of Great Britain," Economics of Education Review, Elsevier, vol. 52(C), pages 192-208.
    6. Nick Drydakis, 2025. "Artificial intelligence and labor market outcomes," IZA World of Labor, LISER, pages 514-514, February.
    7. Ching Sing Chai & Ding Yu & Ronnel B. King & Ying Zhou, 2024. "Development and Validation of the Artificial Intelligence Learning Intention Scale (AILIS) for University Students," SAGE Open, , vol. 14(2), pages 21582440241, April.
    8. Geoffrey M. Hodgson, 2014. "What is capital? Economists and sociologists have changed its meaning: should it be changed back?," Cambridge Journal of Economics, Cambridge Political Economy Society, vol. 38(5), pages 1063-1086.
    9. Sen, Amartya, 1997. "Editorial: Human capital and human capability," World Development, Elsevier, vol. 25(12), pages 1959-1961, December.
    10. Michael Spence, 1973. "Job Market Signaling," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 87(3), pages 355-374.
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    Cited by:

    1. Ivo Soares, 2026. "The Employability Paradox in the AI Era: How the ‘Efficacy Gap’ In Business Curricula Hinders Technological Readiness," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 10(2), pages 1407-1419, February.

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    Keywords

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    JEL classification:

    • I23 - Health, Education, and Welfare - - Education - - - Higher Education; Research Institutions
    • I21 - Health, Education, and Welfare - - Education - - - Analysis of Education
    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity
    • J21 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Labor Force and Employment, Size, and Structure
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes
    • O15 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Economic Development: Human Resources; Human Development; Income Distribution; Migration
    • I24 - Health, Education, and Welfare - - Education - - - Education and Inequality
    • J15 - Labor and Demographic Economics - - Demographic Economics - - - Economics of Minorities, Races, Indigenous Peoples, and Immigrants; Non-labor Discrimination
    • J16 - Labor and Demographic Economics - - Demographic Economics - - - Economics of Gender; Non-labor Discrimination

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