AI Literacy and Workforce Readiness in The Digital Era: The Mediating Role of Human Capital Development

Faisal Hamid Maka Raja; Daeng Ayub; M. Jais; Tri Handoko

doi:10.59024/ijellacush.v4i3.1918

Authors

Faisal Hamid Maka Raja Universitas Riau
Daeng Ayub Universitas Riau
M. Jais Universitas Riau
Tri Handoko Universitas Riau

DOI:

https://doi.org/10.59024/ijellacush.v4i3.1918

Keywords:

AI Literacy, Community Education, Digital Skills, Human Capital, Workforce Readines

Abstract

Artificial intelligence (AI) has become an integral component of higher education and is increasingly recognized as a strategic competency required for future employment. However, although university students frequently utilize AI technologies, limited empirical evidence explains how AI literacy contributes to workforce readiness through the development of human capital. This study aims to examine the direct effects of AI Literacy on Human Capital Development and Workforce Readiness, as well as the mediating role of Human Capital Development in the relationship between AI Literacy and Workforce Readiness among university students. A quantitative explanatory research design was employed using a census sampling technique involving 200 active students from the Community Education Study Program, Faculty of Teacher Training and Education, Universitas Riau, Indonesia. Data were collected through a structured questionnaire using a five-point Likert scale and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS 4. The findings indicate that AI Literacy positively influences Human Capital Development and Workforce Readiness, while Human Capital Development also exerts a significant positive effect on Workforce Readiness and partially mediates the relationship between AI Literacy and Workforce Readiness. These findings demonstrate that AI Literacy extends beyond technological competence by strengthening students' human capital and enhancing their readiness for the digital workforce. Therefore, higher education institutions should integrate AI literacy into teaching and learning practices to foster graduate competitiveness and sustainable workforce preparedness in the era of digital transformation.

References

Alexander Benlian, M. P. (2025). The AI literacy development canvas: Assessing and building AI literacy in organizations. Business Horizons, 1(2), 1–25. https://doi.org/10.1016/j.bushor.2025.10.001

Biagini, G. (2025). Towards an AI-literate future: A systematic literature review exploring education, ethics, and applications. International Journal of Artificial Intelligence in Education, 35(4), 2616–2666. https://doi.org/10.1007/s40593-025-00466-w

Carolus, A., Koch, M. J., Straka, S., Erich, M., & Wienrich, C. (2023). MAILS—Meta AI literacy scale: Development and testing of an AI literacy questionnaire based on well-founded competency models and psychological change- and meta-competencies. Computers in Human Behavior: Artificial Humans, 1(2), 100014. https://doi.org/10.1016/j.chbah.2023.100014

Chiu, T. K. F. (2025). AI literacy and competency: Definitions, frameworks, development and future research directions. Interactive Learning Environments, 33(5), 3225–3229. https://doi.org/10.1080/10494820.2025.2514372

Ng, D. T. K. (2021). Conceptualizing AI literacy: An exploratory review. Computers and Education: Artificial Intelligence, 2, 100041. https://doi.org/10.1016/j.caeai.2021.100041

Hackl, V. (2026). The AI literacy heptagon: A structured approach to AI literacy in higher education. Computers and Education: Artificial Intelligence, 10, 100540. https://doi.org/10.1016/j.caeai.2026.100540

Hair, J. (2022). Partial least squares structural equation modelling (PLS-SEM) in second language and education research: Guidelines using an applied example. Research Methods in Applied Linguistics, 1(2), 100027. https://doi.org/10.1016/j.rmal.2022.100027

Joseph, F. (2026). Partial least squares structural equation modeling (PLS-SEM) using R. Springer. https://doi.org/10.1007/978-3-030-80519-7

Kholifah, N. (2025). Unlocking workforce readiness through digital employability skills in vocational education graduates: A PLS-SEM analysis based on human capital theory. Social Sciences & Humanities Open, 11(1), 101625. https://doi.org/10.1016/j.ssaho.2025.101625

Koch, M. J. (2024). Meta AI literacy scale: Further validation and development of a short version. Heliyon, 10(21), e39686. https://doi.org/10.1016/j.heliyon.2024.e39686

Lee, O. A., Ahn, J. H., & others. (2024). A systematic review of AI literacy conceptualization, constructs, and implementation and assessment efforts (2019–2023). Computers and Education Open, 6, 100173. https://doi.org/10.1016/j.caeo.2024.100173

Lintner, T. (2024). A systematic review of AI literacy scales. Science of Learning, 1(2), 1–11. https://doi.org/10.1038/s41539-024-00264-4

Oldemeyer, L., Jede, A., & Teuteberg, F. (2026). Facilitating AI acceptance in SMEs: Exploring the end-user perspective. Journal of Small Business Strategy, 36(3), 41–56. https://doi.org/10.53703/001c.163481

Ramdana, A. D. (2026). Advancing digital literacy in higher education through pedagogical innovations and institutional strategies between 2014 and 2025. Discover Education, 1(2), 1–40. https://doi.org/10.1007/s44217-026-01256-9

Rasoolimanesh, S. M. (2022). Discriminant validity assessment in PLS-SEM: A comprehensive composite-based approach. Data Analysis Perspectives Journal, 3, 1–8.

Tian, J., & Zhang, Y. (2025). Does artificial intelligence help in improving human capital based educational development? Evidence from 29 countries. Technology in Society, 83, 103004. https://doi.org/10.1016/j.techsoc.2025.103004

Zahidi, S. (2025). Future of Jobs Report. World Economic Forum.