The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
Artificial Intelligence (AI) Literacy Questionnaire with Confirmatory Factor Analysis
Abstract
In recent years, schools started to incorporate artificial intelligence (AI) into computer science/STEAM curricula. However, few validated measurements have been designed to examine how secondary students develop AI literacy and perceive their learning outcomes. AI literacy has been measured from students' knowledge and skill acquisition, and behavior and attitudes. This research aims to develop and validate an instrument to assess AI literacy for secondary students. A questionnaire with 25 items measured in a 5-point Likert scale was created. In a pilot study, the questionnaire was administered to a sample of 363 secondary school students from two different schools in Hong Kong. Confirmatory factor analysis was conducted and grouped into six factors: (1) intrinsic motivation, (2) self-efficacy, (3) behavioral intention, (4) behavioral engagement, (5) know and understand, and (6) use and apply AI. The questionnaire showed a good fit model to support internal consistency reliability in most of the factors, with Cronbach's Alpha levels ranging from .58 to .88. A less-parsimonious model was proposed that help educators measure a wider AI literacy skill set with an acceptable model fit, with Cronbach's Alpha levels ranging from .91 to .94 based on affective, behavorial, cognitive and ethical (ABCE) learning framework. Further studies are needed to confirm the factor structure.
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“Previous DL scales varied according to their specific geographic and demographic differences; however, they could be summarized as the measurement of affective, behavioural, cognitive and ethical perspectives, which could help serve as the basis of this study (Ng, Leung, Su, Ng, et al., 2023).”
From Design and validation of the AI literacy questionnaire: The affective, behavioural, cognitive and ethical approach (2024) · cites this paper as Ng, Leung, Su, Ng, et al., 2023
In the citing paper
“The study offers an updated framework to conceptualize necessary AI literacy and provides some empirical evidence for Authors (2021b)'s four cognitive domains and Ng, Leung, et al., (2023)'s conceptual framework of AI literacy.”
From Design and validation of the AI literacy questionnaire: The affective, behavioural, cognitive and ethical approach (2024) · cites this paper as Ng, Leung, et al., (2023)
Citations
Cites
The global landscape of AI ethics guidelines
Jobin, A., Ienca, M., & Vayena, E. (2019) · Nature Machine Intelligence · doi:10.1038/s42256-019-0088-2 · Cited by 6,220
What is AI Literacy? Competencies and Design Considerations
Long, D., & Magerko, B. (2020) · Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376727 · Cited by 3,241
Conceptualizing AI literacy: An exploratory review
Ng, D. T. K., Leung, J. K. L., Chu, S. K. W., & Qiao, M. S. (2021) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2021.100041 · Cited by 2,205 · cited 5×
“literacy, Ng [1,2,7] conducted a systematic literature review to”
BackgroundMethodInfluentialSelf-citationAI Literacy: Definition, Teaching, Evaluation and Ethical Issues
Ng, D. T. K., Leung, J. K. L., Chu, K. W. S., & Qiao, M. S. (2021) · Proceedings of the Association for Information Science and Technology · doi:10.1002/pra2.487 · Cited by 537
Self-citationXia, Q., Chiu, T. K. F., Lee, M., Sanusi, I. T., Dai, Y., & Chai, C. S. (2022) · Computers & Education · doi:10.1016/j.compedu.2022.104582 · Cited by 469 · cited 1×
“The model demonstrated better fit, and the items were consistent with prior research [3,4,5,8].”
Chiu, T. K. F., & Chai, C.-S. (2020) · Sustainability · doi:10.3390/su12145568 · Cited by 448
Creation and Evaluation of a Pretertiary Artificial Intelligence (AI) Curriculum
Chiu, T. K. F., Meng, H., Chai, C.-S., King, I., Wong, S., & Yam, Y. (2022) · IEEE Transactions on Education · doi:10.1109/te.2021.3085878 · Cited by 380
Promoting students' well-being by developing their readiness for the artificial intelligence age
Dai, Y., Chai, C.-S., Lin, P.-Y., Jong, M. S.-Y., Guo, Y., & Qin, J. (2020) · Sustainability · doi:10.3390/su12166597 · Cited by 330
Using digital story writing as a pedagogy to develop AI literacy among primary students
Ng, D. T. K., Luo, W., Chan, H. M. Y., & Chu, S. K. W. (2022) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2022.100054 · Cited by 266 · cited 1×
“[19] examined a structural equation model to understand students’ perceived usefulness, attitude towards using AI, subjective norms, AI literacy and their behavioral intention to learn about AI.”
BackgroundSelf-citationChai, C. S., Wang, X., & Xu, C. (2020) · Mathematics · doi:10.3390/math8112089 · Cited by 226
Research works printed but not linked: 16
- 10.1111/j.1467-8535.2011.01229.x · DOI
- 0, Responsible and trusted AI - Cloud Adoption Framework, Cloud Adoption Framework ? Microsoft Learn
- 10.1145/3411764.3445377 · DOI
- chai, 2021, Perceptions of and behavioral intentions towards learning artificial intelligence in primary school students, Educational Technology & Society, 24, 89
- 10.1007/s11423-022-10111-1 · DOI
- 10.1080/00220671.1940.10880929 · DOI
- ng, 2023, Design and validation of the AI literacy questionnaire The affective behavioral cognitive and ethical approach
- 10.1207/s15328007sem1103_2 · DOI · Paper page
- 10.1207/S15328007SEM1001_6 · DOI · Paper page
- 10.1007/s10639-022-11491-w · DOI
- 10.1016/S0747-5632(01)00017-6 · DOI
- 10.1016/j.compedu.2018.02.013 · DOI
- 10.1016/j.compedu.2016.09.013 · DOI
- 10.1145/3408877.3432513 · DOI · Paper page
- 10.1007/s11423-023-10203-6 · DOI
- 10.24059/olj.v25i1.2454 · DOI · Paper page
Cited by
Walter, Y. (2024) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-024-00448-3 · Cited by 1,113
“Due to its importance, there are first AI-Literacy-Scales emerging using questionnaires that can be handed to students (Ng et al., 2023).”
Ng, D. T. K., Wu, W., Leung, J. K. L., Chiu, T. K. F., & Chu, S. K. W. (2024) · British Journal of Educational Technology · doi:10.1111/bjet.13411 · Cited by 390
Self-citationHornberger, M., Bewersdorff, A., & Nerdel, C. (2023) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2023.100165 · Cited by 276
AI Literacy in K-12 and Higher Education in the Wake of Generative AI: An Integrative Review
Gu, X., & Ericson, B. J. (2025) · arXiv · doi:10.48550/arxiv.2503.00079 · Cited by 73
Ma, S., & Chen, Z. (2024) · IEEE Access · doi:10.1109/access.2024.3468378 · Cited by 55
Liu, M., Zhang, L. J., & Zhang, D. (2025) · Computers in Human Behavior · doi:10.1016/j.chb.2025.108569 · Cited by 43
Soto-Sanfiel, M. T., Angulo-Brunet, A., & Lutz, C. (2024) · doi:10.31235/osf.io/bvyku · Cited by 20
Metadata
- Authors
- Kit Ng, D. T., Wu, W., Lok Leung, J. K., & Wah Chu, S. K.
- Affiliations
- The University of Hong Kong,Faculty of Education,Hong Kong,China
- Chinese University of Hong Kong (HK)
- The Hong Kong University of Science and Technology,Division of Integrative Systems and Design,Hong Kong,China
- Venue
- 2023 IEEE International Conference on Advanced Learning Technologies (ICALT)
- Publisher
- IEEE
- Published
- 2023-07
- Pages
- 233-235
- OpenAlex
- W4387193182
- Cited by
- 68
- References
- 11
- Citations by year
- 2026: 21 · 2025: 27 · 2024: 16 · 2023: 4
- FWCI
- 61.14
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE International Conference on Advanced Learning Technologies (ICALT) (publishedVersion)
- Rare & Special e-Zone (The Hong Kong University of Science and Technology) (submittedVersion)
- Topics
- Social Sciences › Education › Digital literacy in education
- Social Sciences › Education › Artificial Intelligence in Education
- Physical Sciences › Computer Science › Computer Science Applications › Teaching and Learning Programming
- Keywords
- AI literacy, high school students, instrument validation, factor structure, intrinsic motivation, self-efficacy, behavioral intention, behavioral engagement, artificial intelligence in education, STEAM education
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