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

Artificial intelligence (AI) literacy has become an essential competence in higher education as students prepare for AI-driven academic and professional environments. This scoping review systematically maps the landscape of AI literacy measurement among higher education students. Following Arksey and O’Malley’s (2005) five-stage framework, a systematic search of five databases yielded 190 records, with 39 studies meeting inclusion criteria. The analysis examined definitions of AI literacy, measurement methods, tools, constructs, theoretical frameworks, related variables, and geographical contexts.Findings reveal a growing consensus around AI literacy as a multidimensional construct encompassing knowledge, application, evaluation, and ethics. Research is dominated by quantitative methods. The Artificial Intelligence Literacy Scale (AILS) (Wang et al., 2022) emerged as the most frequently used instrument, alongside newer validated tools and several study-specific measures. Only a small number of studies employed objective knowledge tests or mixed-methods approaches, highlighting a reliance on perceptual rather than demonstrated competencies. AI literacy was commonly studied in relation to attitudes, self-efficacy, and technology adoption intentions. Geographically, research is concentrated in East Asia, with smaller representation from Europe, North America, and the Middle East.This review underscores the field’s progress toward standardized measurement while identifying critical gaps, including overreliance on self-report, limited use of qualitative and performance-based assessments, conceptual ambiguities between AI and generative AI literacy, and geographical imbalance. Addressing these gaps will strengthen the validity and global applicability of AI literacy measurement, enabling more effective educational practices in higher education.