MEASURING AI LITERACY: A REVIEW OF GENERAL-PURPOSE SCALES AND A SIX-DIMENSIONAL FRAMEWORK

Authors

  • ChunYi Lin (Corresponding Author) School of Humanities, Fujian University of Technology, Fuzhou 350118, Fujian, China.

Keywords:

AI literacy, AI attitude scale, Generative AI literacy scale, Cross-cultural assessment, Measurement invariance, Literature review

Abstract

The rapid development of generative artificial intelligence (AI) has made AI literacy one of the core competencies of citizens in the digital age. Yet current instruments for assessing AI literacy tend to be regionally bounded, lack cross-cultural equivalence, and measure only a narrow range of dimensions, which limits their usefulness for general assessment across cultures and contexts. This paper reviews Chinese- and English-language studies published between 2021 and 2025, combining CiteSpace bibliometric mapping with thematic analysis of 147 screened records, in order to compare the theoretical foundations, dimensional structures, psychometric properties, and cross-cultural adaptability of AI attitude scales (ALS) and generative AI literacy scales (GLAT). The review finds that most existing scales follow a four-dimensional model of cognition–skill–affect–ethics, but rarely address how assessment procedures might be standardized or how cross-cultural adaptation might work in practice. On this basis, the paper proposes a six-dimensional model—cognition, skill, affect, ethics, assessment, and cross-cultural—to provide theoretical support for building a general-purpose AI literacy scale, and it outlines directions and trends for future scale development.

References

[1] Long D, Magerko B. What is AI literacy? Competencies and design considerations. Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. New York: ACM, 2020: 1-16.

[2] Ng D T K, Leung J K L, Chu S K W, et al. Conceptualizing AI literacy: An exploratory review. Computers and Education: Artificial Intelligence, 2021, 2: 100041.

[3] Lin J, Gong X, Wu Z. A review of research on the Generative AI Literacy Scale (GLAT). Chinese Journal of Science and Technology, 2025, 1(4): 273-277.

[4] Jin Y, Martinez-Maldonado R, Gasevic D, et al. GLAT: The generative AI literacy assessment test. Computers and Education: Artificial Intelligence, 2025, 9: 100436.

[5] Lin J, Hu X, Wu Z. A review of research on the Artificial Intelligence Attitude Scale (ALS). Chinese Journal of Science and Technology, 2025, 1(4): 267-272.

[6] OECD. OECD Digital Education Outlook 2021: Pushing the Frontiers with Artificial Intelligence, Blockchain and Robots. Paris: OECD Publishing, 2021.

[7] European Commission. DigComp 2.2: The Digital Competence Framework for Citizens. European Commission, 2025. https://digital-strategy.ec.europa.eu/en/library/digcomp-22.

[8] Cai Y, Zhang J, Yu C, et al. Artificial intelligence literacy in the era of digital intelligence: Connotation, framework and implementation paths. Journal of Library Science in China, 2024(4): 71-84.

[9] Zhong B, Liu X, Yang M. What is artificial intelligence literacy? Essence, composition and evaluation system. Journal of East China Normal University (Educational Sciences), 2024, 42(1): 71-84.

[10] Davis F D. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 1989, 13(3): 319-340.

[11] Vygotsky L S. Mind in society: The development of higher psychological processes. Cambridge, MA: Harvard University Press, 1978.

[12] Frankena W K. Ethics. 2nd ed. Englewood Cliffs, NJ: Prentice-Hall, 1973.

[13] Carolus A, Koch M J, Straka S, et al. 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, 2023, 1(2): 100014.

[14] Ning Y, Zhang W, Yao D, et al. Development and validation of the Artificial Intelligence Literacy Scale for Teachers (AILST). Education and Information Technologies, 2025, 30(12): 17769-17803.

[15] Ng D T K, Wu W, Leung J K L, et al. Design and validation of the AI literacy questionnaire: The affective, behavioural, cognitive and ethical approach. British Journal of Educational Technology, 2024, 55(3): 1082-1104.

[16] Lee S C, Baby T, Vongvit R, et al. Development and validation of Generative AI Competence Scale (GenAIComp) among university students. Technology in Society, 2026, 84: 103059.

[17] Li N, Liu S, Ji J. Construction of a smart literacy evaluation system for university students from the perspective of the continuum. Information Studies: Theory & Application, 2025, 48(1): 150-159.

[18] Zhu S, Li H, Wu D, et al. Constructing artificial intelligence core literacy for minors: Research on technical specifications of artificial intelligence for minors (Part II). e-Education Research, 2023, 44(6): 15-20.

[19] Liu M, Ma S, Wang J. University students' learning with generative artificial intelligence: Model construction and evaluation index system. Modern Educational Technology, 2025, 35(11): 14-22.

[20] Zhang J. AI-empowered foreign language learning literacy of university students: Framework construction and scale development. Technology Enhanced Foreign Language Education, 2025(1): 69-78.

[21] Lee S, Park G. Development and validation of ChatGPT literacy scale. Current Psychology, 2024, 43(21): 18992-19004.

[22] Zhang H, Perry A, Lee I. Developing and validating the artificial intelligence literacy concept inventory: An instrument to assess artificial intelligence literacy among middle school students. International Journal of Artificial Intelligence in Education, 2025, 35(1): 398-438.

[23] Chiu T K F, Chen Y, Yau K W, et al. Developing and validating measures for AI literacy tests: From self-reported to objective measures. Computers and Education: Artificial Intelligence, 2024, 7: 100282.

[24] Yabana Kiremit B. Validity and reliability of the Turkish version of the artificial intelligence literacy scale among healthcare professionals. International Journal of Human-Computer Interaction, 2025, 42(10): 7087-7098.

[25] Lintner T. A systematic review of AI literacy scales. npj Science of Learning, 2024, 9(1): 50.

[26] Wang Y, Wu H, Derakhshan A, et al. Development and validation of an attitude scale toward generative AI-assisted English learning for Chinese university students. Journal of Beijing International Studies University, 2024, 46(6): 30-45.

[27] Wang Y, Derakhshan A, Ghiasvand F. EFL teachers' generative artificial intelligence (GenAI) literacy: A scale development and validation study. System, 2025, 133: 103791.

[28] Wang L, Fan H, Cao F, et al. Generative artificial intelligence empowering the digital literacy of international Chinese language teachers: Assessment framework, current status and suggestions. Journal of Tianjin Normal University (Social Science), 2024(3): 72-83.

[29] Walter Y. Embracing the future of Artificial Intelligence in the classroom: The relevance of AI literacy, prompt engineering, and critical thinking in modern education. International Journal of Educational Technology in Higher Education, 2024, 21(1): 15.

[30] Li Y, Zhu Y, Sun D, et al. Differences in the use of generative artificial intelligence in typical research scenarios: The influence of disciplinary background and artificial intelligence literacy. Modern Distance Education Research, 2025, 37(2): 92-101+112.

[31] Celik I. Towards intelligent-TPACK: An empirical study on teachers' professional knowledge to ethically integrate artificial intelligence (AI)-based tools into education. Computers in Human Behavior, 2023, 138: 107468.

[32] UNESCO. AI and education: Guidance for policy-makers. Paris: UNESCO Publishing, 2021.

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Published

2026-10-16

How to Cite

ChunYi Lin. Measuring Ai Literacy: A Review Of General-Purpose Scales And A Six-Dimensional Framework. World Journal of Information and Knowledge Management. 2026, 4(2): 8-15. DOI: https://doi.org/10.61784/wjikm3030.