Vol. 2026 No. 1 (2026): 2026 Continuous Issue
Articles

On the Measurement of AI Literacy Among Students in Higher Education: A Scoping Review

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  • Jeffrey Jones
Jeffrey Jones
Oregon Health & Science University

Published 2026-02-17

Keywords

  • AI literacy,
  • higher education

How to Cite

Jones, J. (2026). On the Measurement of AI Literacy Among Students in Higher Education: A Scoping Review. International Journal of AI in Pedagogy, Innovation, and Learning Futures, 2026(1). Retrieved from https://journals.calstate.edu/ijaipil/article/view/6920

Copyright (c) 2026 Jeffrey Jones

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Artificial intelligence (AI) literacy is essential in higher education. This scoping review maps how AI literacy is measured among higher education students. Using Arksey and O’Malley’s (2005) five-stage framework, we searched five databases, screened 190 records, and included 39 studies. We synthesized definitions, instruments, construct dimensions, theoretical frameworks, related variables, and geographic contexts. Findings indicate emerging consensus that AI literacy is multidimensional, commonly covering knowledge, application, evaluation, and ethics. Research was largely quantitative and self-report; the Artificial Intelligence Literacy Scale (AILS) (Wang et al., 2022) was the most frequently used measure. Few studies used objective tests or mixed methods, limiting evidence of demonstrated competence. AI literacy was often examined alongside attitudes, self-efficacy, and adoption intentions. Studies were concentrated in East Asia, with fewer from Europe, North America, and the Middle East. This review highlights needs for performance-based assessment, clearer boundaries between AI and generative AI literacy, and broader geographic coverage.

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