The DOI matches its Crossref record; 69 of the 69 references it cites were found in a registry or an index.
Measuring user competence in using artificial intelligence: validity and reliability of artificial intelligence literacy scale
Abstract
As artificial intelligence (AI) became a part of daily life, it has become important to determine user competence in using AI technology. Here, we propose the concept of AI literacy and develop a quantitative scale for obtaining accurate data regarding the AI literacy of ordinary users. We first identified the primary core constructs of AI literacy, including awareness, use, evaluation, and ethics. Next, we generated 65 items to capture these four constructs; only 31 items were retained after a three-step content validation process. Then, we conducted a survey, and collected two samples of data. By reducing the number of items using the first sample and performing reliability and validity tests on the second sample, we obtained a 12-item instrument for the quantitative measurement of AI literacy. The results confirmed that the proposed four-construct model is an adequate representation of AI literacy. Further, AI literacy is significantly related to digital literacy, attitude towards robots, and users’ daily usage of AI. This study will not only aid researchers in understanding how user competence in using AI technology affects human–AI interactions but will also help designers develop AI applications that are aligned with the AI literacy levels of the target users.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“This attitude scale was used because it is one of the shortest and, thus, one of the most resource-efficient instruments designed to capture attitudes; it had already been validated in the native language of the course participants.”
From Evaluating AI Courses: A Valid and Reliable Instrument for Assessing Artificial-Intelligence Learning through Comparative Self-Assessment (2023) · cites this paper as [22]
In the citing paper
“This could be an indication of discriminant validity because, in theory, AI literacy and attitudes toward AI are assumed to be two different constructs, representing cognitive/skill and affective aspects, respectively [9,16,20-22].”
From Evaluating AI Courses: A Valid and Reliable Instrument for Assessing Artificial-Intelligence Learning through Comparative Self-Assessment (2023) · cites this paper as [22]
In the citing paper
“The reliability coefficients of the sub-dimensions range between 0.72 and 0.76, and the overall reliability (Cronbach’s alpha) of the scale is 0.84, indicating good internal consistency.”
From Integrating artificial intelligence into preservice teacher education: enhancing AI literacy and digital material design competency (2026) · cites this paper as Wang et al. (2022)
In the citing paper
“The Artificial Intelligence Literacy Scale (AILS; Wang et al., 2023) measures AI understanding and competence across four subscales: awareness, usage, evaluation and ethics, using 12 items on a 7- point Likert scale.”
From The role of critical thinking on undergraduates' reliance behaviours on generative AI in problem‐solving (2025) · cites this paper as Wang et al., 2023
In the citing paper
“The validity of the self-report measurement instruments for AI literacy [11,12,14,15] has only been tested regarding their factorial structure.”
From Meta AI literacy scale: Further validation and development of a short version (2024) · cites this paper as [15]
In the citing paper
“It appears that individuals with higher positive attitudes, lower negative attitudes, and more willingness to use technology are also more likely to consider themselves competent [1, 46, 48].”
In the citing paper
“According to Wang et al. [47], the degree of AI literacy can determine individuals' readiness to embrace AI technologies.”
From Modeling teachers’ acceptance of generative artificial intelligence use in higher education: the role of AI literacy, intelligent TPACK, and perceived trust (2024) · cites this paper as [47]
In the citing paper
“AI literacy improves understanding of GenAI's advantages and functions, which encourages a favorable view of its acceptance and use in educational contexts [3,47,48,64].”
From Modeling teachers’ acceptance of generative artificial intelligence use in higher education: the role of AI literacy, intelligent TPACK, and perceived trust (2024) · cites this paper as [47]
In the citing paper
“Wang et al. [58] affirmed a notable correlation between AI literacy and attitudes toward robots’ and users’ regular use of AI.”
From ChatGPT in Learning: Assessing Students’ Use Intentions through the Lens of Perceived Value and the Influence of AI Literacy (2024) · cites this paper as [58]
In the citing paper
“Higher AI literacy enables users to better understand the capabilities and limitations of AI technologies, leading to more effective adoption and usage [22,58].”
From ChatGPT in Learning: Assessing Students’ Use Intentions through the Lens of Perceived Value and the Influence of AI Literacy (2024) · cites this paper as [58]
In the citing paper
“Wang et al. [58] indicated that the level of AI literacy determines whether students embrace AI technologies.”
From ChatGPT in Learning: Assessing Students’ Use Intentions through the Lens of Perceived Value and the Influence of AI Literacy (2024) · cites this paper as [58]
In the citing paper
“Their ability to leverage the tool efficiently contributes to their perception of the AI tool as highly useful [22,58,62].”
From ChatGPT in Learning: Assessing Students’ Use Intentions through the Lens of Perceived Value and the Influence of AI Literacy (2024) · cites this paper as [58]
In the citing paper
“The support for H10, which states that AI literacy positively influences the perceived value of ChatGPT, underscores the significance of students’ understanding of and familiarity with AI in appreciating the tool’s potential and contributions to their academic and personal endeavours.”
From ChatGPT in Learning: Assessing Students’ Use Intentions through the Lens of Perceived Value and the Influence of AI Literacy (2024) · cites this paper as [58]
Citations
Cites
Evaluating Structural Equation Models with Unobservable Variables and Measurement Error
Fornell, C., & Larcker, D. F. (1981) · Journal of Marketing Research · doi:10.2307/3151312 · Cited by 109,314
Evaluating structural equation models with unobservable variables and measurement error
Fornell, C., & Larcker, D. F. (1981) · Journal of Marketing Research · doi:10.1177/002224378101800104 · Cited by 109,190
(1994) · Journal of the American Dietetic Association · doi:10.1016/0002-8223(94)92216-0 · Cited by 46,726
Principles and Practice of Structural Equation Modeling
Tabri, N., & Elliott, C. M. (2012) · Canadian Graduate Journal of Sociology and Criminology · doi:10.15353/cgjsc.v1i1.3787 · Cited by 39,094
A new criterion for assessing discriminant validity in variance-based structural equation modeling
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-014-0403-8 · Cited by 38,459
null (2016) · Philosophy Study · doi:10.17265/2159-5313/2016.09.003 · Cited by 28,317
Einax, J. W., Zwanziger, H. W., & Geiß, S. (1997) · Wiley eBooks · doi:10.1002/352760216x.ch5 · Cited by 18,779
Scale Development: Theory and Applications.
Xie, Y.-P., & Devellis, R. (1992) · Contemporary Sociology A Journal of Reviews · doi:10.2307/2075704 · Cited by 14,143
Costello, A. B., & Osborne, J. (2005) · University of Massachusetts Amherst · doi:10.7275/jyj1-4868 · Cited by 10,297
Constructing validity: Basic issues in objective scale development.
Clark, L. A., & Watson, D. (1995) · Psychological Assessment · doi:10.1037/1040-3590.7.3.309 · Cited by 6,567
Research works printed but not linked: 15
- Buckingham D., 1989, Radical Philosophy, 51, 12
- Buckingham D., 2007, Journal of Educational Multimedia and Hypermedia, 16, 323
- Calvani A., 2008, Journal of E-Learning and Knowledge Society, 4, 183
- 10.1177/002224378001700401 · DOI
- Eshet Y., 2004, Journal of Educational Multimedia and Hypermedia, 13, 93
- Finstad K., 2010, Journal of Usability Studies, 5, 104
- Gapski, H. 2007. “Some Reflections on Digital Literacy.” Paper Presented at the Proceedings of the 3rd International Workshop on Digital Literacy.
- Gilster P., 1997, Digital Literacy
- 10.7551/mitpress/8975.001.0001 · DOI · Paper page
- 10.17265/2159-5313/2016.09.003 · DOI · Paper page
- Kim Y., 2013, Journal of Digital Convergence, 11, 9
- Likert R., 1932, Archives of Psychology
- Mayerl J., 2016, Green European, 210
- Tarafdar M., 2019, MIT Sloan Management Review, 60, 37
- Wilson, H. J., and P. R. Daugherty. 2018. “Collaborative Intelligence: Humans and AI Are Joining Forces.” Harvard Business Review .
Cited by
Sorted by citations
Items 401–500 of 1,003
Yılmaz, F. G. K., Olpak, Y. Z., & Yılmaz, R. (2026) · Journal of Research on Technology in Education · doi:10.1080/15391523.2026.2683793 · Cited by 4
Zhou, J., Lu, Y., & Li, W. (2026) · Telecommunications Policy · doi:10.1016/j.telpol.2026.103273 · Cited by 6
Information Literacy in the Artificial Intelligence Sphere
Molopa, S. T., Mothopeng, O., Zulu, M., Kgasi, E. M., Mila-Ndenge, N., Ndlovu, N. S., Barends, C. J. M., Xesi, X., & Levi, Y. (2024) · Advances in library and information science (ALIS) book series · doi:10.4018/979-8-3693-1143-1.ch006 · Cited by 4
Haris, H., Darwis, M. R., Arsyanda, JY, M. R. W., & Ilham, M. A. (2024) · Jurnal Pendidikan Terapan · doi:10.61255/jupiter.v2i1.200 · Cited by 4
Yapay Zekâya Yönelik Tutum Ölçeğinin Türkçeye Uyarlanması: Geçerlik ve Güvenirlik Çalışması
İlhan, A. G., & Önal, N. (2025) · Temel Eğitim · doi:10.52105/temelegitim.1606097 · Cited by 6
Digital Literacy and Critical Thinking Enhancing Student Perspectives on Artificial Intelligence
Bhimavarapu, U. (2025) · IGI Global eBooks · doi:10.4018/979-8-3693-9370-3.ch005 · Cited by 4
Mao, P., Cai, Z., Sun, X., Niu, G., Zhang, Y., & Liu, J. (2025) · Learning and Individual Differences · doi:10.1016/j.lindif.2025.102756 · Cited by 4
Lenke, M., Klowait, N. O., Biere, L., & Schulte, C. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-01222-7_8 · Cited by 4
Toward a new framework for teaching algorithmic literacy
Archambault, S. G. (2023) · Information and Learning Sciences · doi:10.1108/ils-07-2023-0090 · Cited by 7
Leadership in action: how senior management behaviours propel AI adoption and innovation success
Zhang, X., Song, M., & Zhang, H. (2025) · Journal of Experimental & Theoretical Artificial Intelligence · doi:10.1080/0952813x.2025.2530416 · Cited by 4
Sarwari, A. Q. (2024) · Interdisciplinary Description of Complex Systems · doi:10.7906/indecs.22.1.1 · Cited by 4
Wang, R., Li, C., Tan, Y., Liu, S., & Cao, L. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5735717 · Cited by 4
Developing Teachers' AI Literacy Through Professional Development
Valenzuela, J. M. (2025) · Advances in higher education and professional development book series · doi:10.4018/979-8-3693-9102-0.ch009 · Cited by 4
Low, M. P., Wut, T. M., & Pok, W. F. (2025) · Educational Gerontology · doi:10.1080/03601277.2025.2528673 · Cited by 4
KİREMİT, B. Y. (2025) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2025.2556230 · Cited by 3
Kılıç, E., Almasri, F., & Çelik, H. E. (2025) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2025.100486 · Cited by 5
Kong, E. W. S., Dilinika, J. M. S., Nie, X., Gautam, A., & Huang, K. (2025) · Proceedings of the Association for Information Science and Technology · doi:10.1002/pra2.1325 · Cited by 5
Aleksić, V., Mandić, M., & Ivanović, M. D. (2025) · Education Sciences · doi:10.3390/educsci15121582 · Cited by 5
Zhang, Y. L., Liu, Z., Liu, T., Xie, J., & Chao, M. (2025) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2025.2580549 · Cited by 6
Gökçe, H., & Nacaroğlu, O. (2025) · Education and Information Technologies · doi:10.1007/s10639-025-13853-6 · Cited by 3
Zhang, T., Zhang, P., & Ma, H. (2025) · KNOWLEDGE ORGANIZATION · doi:10.31083/ko44114 · Cited by 4
How AI social responsibility shapes consumers’ brand advocacy
Shen, P., Peng, D., Zhu, A., & Xu, J. (2025) · Humanities and Social Sciences Communications · doi:10.1057/s41599-025-06344-z · Cited by 3
Wu, Q., Li, B. J., & Zhang, H. (2025) · Proceedings of the Association for Information Science and Technology · doi:10.1002/pra2.1350 · Cited by 3
Colón‐Aguirre, M., & Bright, K. M. (2025) · Proceedings of the Association for Information Science and Technology · doi:10.1002/pra2.1244 · Cited by 4
Lutz, C., Newlands, G., & Jarrahi, M. H. (2025) · Springer eBooks · doi:10.1007/978-981-97-8440-0_87-1 · Cited by 4
Durmuşçelebi, M., KONGÜL, R., & Çoşkun, G. (2025) · Türk Eğitim Bilimleri Dergisi · doi:10.37217/tebd.1715015 · Cited by 3
Qi, J., Zhao, H., Xiang, R., Zhang, Y., Zhang, X., Wang, C., Jian, L., & Ji, F. (2026) · Journal of Research on Technology in Education · doi:10.1080/15391523.2025.2601067 · Cited by 7
Kumar, J. A. (2025) · IEEE Frontiers in Education Conference (FIE) · doi:10.1109/fie63693.2025.11328420 · Cited by 4
An assessment of artificial intelligence literacy in Ecuadorian university students
Verduga-Alcívar, D. A., Román-Cao, E., & Cossío, N. S. (2026) · Social Sciences & Humanities Open · doi:10.1016/j.ssaho.2026.102465 · Cited by 5
Unleashing human potential: An artificial intelligence competency framework for K–12 education
Kong, S. C., & Hu, W. (2026) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2026.100556 · Cited by 7
Wang, Z., Wang, K., Park, S., & Yan, J. (2026) · Current Psychology · doi:10.1007/s12144-025-08966-7 · Cited by 5
Xu, L., Li, Y.-Y., Chan, T.-H., & Saleem, F. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.1699919 · Cited by 3
Luo, J., Qu, L., Wang, M., & Li, H. (2026) · Interactive Learning Environments · doi:10.1080/10494820.2026.2642871 · Cited by 3
Duong, C. D. (2026) · International Journal of Innovation Science · doi:10.1108/ijis-03-2025-0143 · Cited by 5
Language teachers’ AI literacy: A psychometric study based on the ED-AI framework
Nabhan, S., & Habók, A. (2026) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2026.100583 · Cited by 6
Lin, Y., Wang, W., & Chen, C. (2026) · The International Journal of Management Education · doi:10.1016/j.ijme.2026.101411 · Cited by 3
Zheng, W. (2026) · European Journal of Education · doi:10.1111/ejed.70628 · Cited by 3
Klein-Avraham, I., & Baram‐Tsabari, A. (2026) · Public Understanding of Science · doi:10.1177/09636625261442388 · Cited by 4
Liu, Y., 赵秀芳, & Zeng, Q. (2026) · Frontiers in Public Health · doi:10.3389/fpubh.2026.1812848 · Cited by 4
Xu, D., Zheng, Y., Yin, Y., Lin, C., Yang, L. Y., Liang, B., & Du, J. (2026) · Journal of Nursing Management · doi:10.1155/jonm/5593996 · Cited by 6
AI-based cognitive offloading by students in higher education in China
Jiang, Y., & Chen, H. (2026) · Computers in Human Behavior Artificial Humans · doi:10.1016/j.chbah.2026.100327 · Cited by 3
Zheng, F., Wang, H., Gu, X., & Wang, G. (2026) · Acta Psychologica · doi:10.1016/j.actpsy.2026.107190 · Cited by 3
Li, J., Liu, Y., 向丽婷, & Guanmian, L. (2026) · BMC Nursing · doi:10.1186/s12912-026-04872-w · Cited by 3
Essien, A. E., Zhou, X., Kremantzis, M. D., & Teng, D. (2026) · Studies in Higher Education · doi:10.1080/03075079.2026.2686986 · Cited by 6
Zi, Y., 梁舒乔, Zhang, M., Liu, X., & Liu, B. (2026) · Sustainability · doi:10.3390/su18136701 · Cited by 4
Pennanen, N., Linkola, S., Kantosalo, A., Hiillos, N., Männistö, T., & Guckelsberger, C. (2025) · ACM Transactions on Human-Robot Interaction · doi:10.1145/3711939 · Cited by 4
Teacher Leadership and AI Literacy
Çoban, Ö., & Durnalı, M. (2025) · IGI Global eBooks · doi:10.4018/979-8-3373-0472-4.ch001 · Cited by 3
Πέλλας, Ν. (2025) · Multimodal Technologies and Interaction · doi:10.3390/mti9070065 · Cited by 8
Torreciba, N. D., & Alieto, E. O. (2025) · EthAIca · doi:10.56294/ai2025416 · Cited by 4
Ateş, V. (2025) · Türk Eğitim Bilimleri Dergisi · doi:10.37217/tebd.1688486 · Cited by 6
Miao, C., & Zhang, S. (2025) · doi:10.1109/icaie64856.2025.11158179 · Cited by 4
Anik, A. I., & Bunt, A. (2024) · IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC) · doi:10.1109/vl/hcc60511.2024.00024 · Cited by 4
Adaptation of Artificial Intelligence Literacy Scale: Latent Profile Analysis
Kırksekiz, A., Yıldız, M., Kıyıcı, M., & Yıldız, M. (2024) · Sakarya University Journal of Education · doi:10.19126/suje.1479294 · Cited by 5
AI or human? A study of university students’ awareness of library reference service agents
Wang, D., Guo, J., Zheng, K., & Deng, X. (2026) · The Electronic Library · doi:10.1108/el-06-2025-0230 · Cited by 2
Gulen, H. N., & Kucuk, S. (2026) · Education and Information Technologies · doi:10.1007/s10639-026-14020-1 · Cited by 3
AI in Scholarly Communications and Academic Research
Tzanova, S. (2024) · Advances in library and information science (ALIS) book series · doi:10.4018/979-8-3693-3053-1.ch002 · Cited by 3
Integrating generative AI tools in higher education
Emre, İ. E. (2025) · American Journal of STEM Education · doi:10.32674/2gxk8g16 · Cited by 2
Baptista, R., de Almeida, C. V., & Belim, C. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-0725-1.ch004 · Cited by 2
Silvennoinen, M., Aksovaara, S., & Alanko-Turunen, M. (2025) · Bled eConference · doi:10.18690/um.fov.4.2025.29 · Cited by 2
Pamungkas, R., Suwono, H., & Ibrohim, I. (2025) · Salud Ciencia y Tecnología · doi:10.56294/saludcyt20251976 · Cited by 2
Consumers’ perceived benefits, barriers and opportunities on technology driven fashion marketing
Çalışkan, A., & Ergun, A. G. (2025) · Qualitative Market Research An International Journal · doi:10.1108/qmr-07-2024-0146 · Cited by 3
Üretken Yapay Zekânın Akademik Yazma Becerisi Üzerindeki Etkisi: Bir SWOT Analizi
Kayhan, S., & Eyüp, B. (2025) · Abant İzzet Baysal Üniversitesi Eğitim Fakültesi Dergisi · doi:10.17240/aibuefd.2025..-1563495 · Cited by 2
Yu, H. Y., Son, G.-Y., & Kim, J. G. (2025) · Journal of Biological Education · doi:10.1080/00219266.2025.2549086 · Cited by 2
AKYÜREK, M. İ. (2025) · Gazi Üniversitesi Gazi Eğitim Fakültesi Dergisi · doi:10.17152/gefad.1469370 · Cited by 2
Reichardt, L., Greiner, C., & Peisl, T. C. (2025) · Lecture notes in networks and systems · doi:10.1007/978-3-032-07986-2_16 · Cited by 3
Huang, G., & Hu, B. (2025) · Science Communication · doi:10.1177/10755470251382889 · Cited by 5
Comparing AI literacy policies in the European Union, the United States, India, and China
Yang, H., Xu, J., Zeng, X., & Gu, X. (2025) · Telecommunications Policy · doi:10.1016/j.telpol.2025.103142 · Cited by 5
AI literacy in shadow education: exploring Chinese EFL practitioners’ perceptions and experiences
Liu, Q., Jiang, M., Wang, Y. Y., & He, L. (2025) · Journal of China Computer-Assisted Language Learning · doi:10.1515/jccall-2025-0011 · Cited by 4
Lartey, S. O., Afful-Dadzie, E., & Afful‐Dadzie, A. (2025) · Data & Policy · doi:10.1017/dap.2025.10049 · Cited by 3
Lap, N. D., & Çakır, R. (2025) · Journal of Teacher Education and Lifelong Learning · doi:10.51535/tell.1808375 · Cited by 2
Can We Be AI-Literate? The State of GenAI Literacy in African Countries: Ghana as a Case Study
Setsoafia, P., & Ng, D. T. K. (2025) · Bridging human and machine: future education with intelligence · doi:10.1007/978-981-95-0950-8_12 · Cited by 2
Şahin, N. T., & Pehlivan, S. (2026) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2026.113082 · Cited by 2
Çapuk, H., Yiğit, M. F., & Uçar, M. (2026) · Informatics for Health and Social Care · doi:10.1080/17538157.2025.2602515 · Cited by 5
Wang, W., Huang, T., & Hu, Y. (2026) · BMC Psychology · doi:10.1186/s40359-025-03947-8 · Cited by 3
The impact of AI usage, literacy and proactive innovation behavior on scholars' mental health
Gao, X., Ding, Y., ZHANG, H., Liang, W., & Zuo, R. (2026) · Industrial Management & Data Systems · doi:10.1108/imds-05-2025-0593 · Cited by 3
Aksoy, M., Aksoy, D. A., Polat, E., Hopcan, S., & Bush, A. (2026) · European Journal of Education · doi:10.1111/ejed.70454 · Cited by 6
Arianpoor, A., Borhani, S. A., & Safa, M. (2026) · The TQM Journal · doi:10.1108/tqm-03-2025-0148 · Cited by 3
Zhao, J. (2026) · Current Psychology · doi:10.1007/s12144-025-08631-z · Cited by 2
Huang, Y., Zhang, K., & Pan, R. (2026) · Higher Education · doi:10.1007/s10734-026-01630-4 · Cited by 2
Açıkgöz, S., Arslan, M., & Göl, İ. (2026) · Collegian Journal of the Royal College of Nursing Australia · doi:10.1016/j.colegn.2026.01.004 · Cited by 2
On the Measurement of AI Literacy Among Students in Higher Education: A Scoping Review
Jones, J. (2026) · International Journal of AI in Pedagogy Innovation and Learning Futures · doi:10.46787/ijaipil.v2026i1.6920 · Cited by 7
Balancing utopian and dystopian paradigms: trust, values, and AI literacy in generative AI adoption
Kim, D., Bak, S., Kim, S., Lee, B. H., Lee, Y., & Park, S. (2026) · Asian Journal of Communication · doi:10.1080/01292986.2026.2639672 · Cited by 2
Teachers’ adoption of generative artificial intelligence (GenAI) in educational settings
Deshen, M., Harari, R., & Aharony, N. (2026) · Cogent Education · doi:10.1080/2331186x.2026.2624877 · Cited by 4
Duong, C. D. (2026) · Asian Education and Development Studies · doi:10.1108/aeds-10-2025-0538 · Cited by 4
Kwon, K., Yang, S., Kale, U., Kim, K., & Park, J. (2026) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2026.100588 · Cited by 4
Emotional Responses to AI Use: Development of the SAG-AI Scale for Shame and Guilt
Cipriani, E., Menicucci, D., Greco, A., & Grassini, S. (2026) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2026.2648803 · Cited by 4
A meta-synthesis study on the use of artificial intelligence in primary education
Polat, S., & SARIDAŞ, G. (2026) · AI & Society · doi:10.1007/s00146-026-03007-8 · Cited by 2
Xu, T., Wu, K., Wang, D., & Wang, W. (2026) · Learning and Motivation · doi:10.1016/j.lmot.2026.102276 · Cited by 2
Kucharska, W., & Kucharski, M. (2026) · International Journal of Information Management Data Insights · doi:10.1016/j.jjimei.2026.100406 · Cited by 4
Chen, C., Troy, C. L. C., & Zhang, M. M. (2026) · Science Communication · doi:10.1177/10755470261434438 · Cited by 2
Socioeconomic disparities in AI awareness: examining the mediating roles of AI usage and familiarity
Wang, S., 周天伦, & Liu, X. (2026) · Information Communication & Society · doi:10.1080/1369118x.2026.2652505 · Cited by 3
Naveed, M. A., Asghar, M. Z., Talha, Riaz, M., & Zeeshan, A. (2026) · Discover Computing · doi:10.1007/s10791-026-10145-6 · Cited by 3
Students’ Artificial Intelligence (AI) literacy: An exploratory study
Deshen, M., & Aharony, N. (2026) · Journal of Librarianship and Information Science · doi:10.1177/09610006261442178 · Cited by 2
Li, Q., Huang, T., Huang, L., & Wu, X. (2026) · PLoS ONE · doi:10.1371/journal.pone.0350124 · Cited by 4
Sultana, N., Haque, A., & Rupa, R. A. (2026) · Discover Artificial Intelligence · doi:10.1007/s44163-026-01469-9 · Cited by 2
Student AI resistance: status anxiety and trust in Kazakhstani higher education
Cespedes, A. A., & Courtney, M. G. R. (2026) · Asian Education and Development Studies · doi:10.1108/aeds-11-2025-0604 · Cited by 3
Unpacking the heterogeneity of AI literacy among nursing undergraduates: A latent profile analysis
Yang, B., Li, C., Wang, M., Wu, Y., Yuan, R., Zhao, W., Guo, Y., Li, P., & Zhang, P. (2026) · Nurse Education Today · doi:10.1016/j.nedt.2026.107238 · Cited by 3
Liu, D., Li, W., Lu, G., Ma, Z., Jing, J., Qiu, H., Li, K., Xu, X., & Chen, C. (2026) · Nurse Education Today · doi:10.1016/j.nedt.2026.107237 · Cited by 2
Change Fatigue and Attitudes Toward AI in County‐Level Nurses: The Mediating Role of AI Literacy
Yu, M., Zhou, M., Yu, R., Fan, X., Geng, R., Ji, J., Cai, S., JIang, L., & Jiang, L. (2026) · Journal of Nursing Management · doi:10.1155/jonm/4386800 · Cited by 2
AI Literacy Scale for Undergraduate Design Students: Development and Validation
Lin, S., & Tan, Z. (2026) · SAGE Open · doi:10.1177/21582440261463131 · Cited by 2
Metadata
- Authors
- Wang, B., Rau, P.-L. P., & Yuan, T.
- Affiliations
- Tsinghua University (CN)
- Corresponding author
- Rau, Pei-Luen Patrick
- Venue
- Behaviour & Information Technology
- Publisher
- Informa UK Limited
- Published
- 2022-05-10
- Volume
- 42
- Issue
- 9
- Pages
- 1324-1337
- ISSN
- 0144-929X (ISSN-L)
- 1362-3001
- OpenAlex
- W4280549083
- Cited by
- 1,036
- References
- 69
- Citations by year
- 2026: 549 · 2025: 323 · 2024: 137 · 2023: 27
- FWCI
- 120.30
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Behaviour and Information Technology (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- Ministry of Science and Technology of China (2018AAA0101700)
- Topics
- Social Sciences › Education › Digital literacy in education
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Social Sciences › Gender Studies › Gender and Technology in Education
- Keywords
- AI literacy, AI literacy scale, user competence, AI awareness, AI ethics, AI evaluation, digital literacy, artificial intelligence use, human-AI interaction, scale development
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
