Design and validation of the AI literacy questionnaire: The affective, behavioural, cognitive and ethical approach

Ng, Davy Tsz Kit; Wu, Wenjie; Leung, Jac Ka Lok; Chiu, Thomas Kin Fung; Chu, Samuel Kai Wah

doi:10.1111/bjet.13411

Cited by 391Digital literacy in educationArtificial Intelligence in EducationLibrary Science and Information Literacy

Design and validation of the AI literacy questionnaire: The affective, behavioural, cognitive and ethical approach

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Ng, Davy Tsz Kit, Wu, Wenjie, Leung, Jac Ka Lok, Chiu, Thomas Kin Fung, Chu, Samuel Kai Wah·2024·British Journal of Educational Technology

DOI 10.1111/bjet.13411

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Abstract

Artificial intelligence (AI) literacy is at the top of the agenda for education today in developing learners' AI knowledge, skills, attitudes and values in the 21st century. However, there are few validated research instruments for educators to examine how secondary students develop and perceive their learning outcomes. After reviewing the literature on AI literacy questionnaires, we categorized the identified competencies in four dimensions: (1) affective learning (intrinsic motivation and self‐efficacy/confidence), (2) behavioural learning (behavioural commitment and collaboration), (3) cognitive learning (know and understand; apply, evaluate and create) and (4) ethical learning. Then, a 32‐item self‐reported questionnaire on AI literacy (AILQ) was developed and validated to measure students' literacy development in the four dimensions. The design and validation of AILQ were examined through theoretical review, expert judgement, interview, pilot study and first‐ and second‐order confirmatory factor analysis. This article reports the findings of a pilot study using a preliminary version of the AILQ among 363 secondary school students in Hong Kong to analyse the psychometric properties of the instrument. Results indicated a four‐factor structure of the AILQ and revealed good reliability and validity. The AILQ is recommended as a reliable measurement scale for assessing how secondary students foster their AI literacy and inform better instructional design based on the proposed affective, behavioural, cognitive and ethical (ABCE) learning framework. Practitioner notes: What is already known about this topic AI literacy has drawn increasing attention in recent years and has been identified as an important digital literacy. Schools and universities around the world started to incorporate AI into their curriculum to foster young learners' AI literacy. Some studies have worked to design suitable measurement tools, especially questionnaires, to examine students' learning outcomes in AI learning programmes. What this paper adds Develops an AI literacy questionnaire (AILQ) to evaluate students' literacy development in terms of affective, behavioural, cognitive and ethical (ABCE) dimensions. Proposes a parsimonious model based on the ABCE framework and addresses a skill set of AI literacy. Implications for practice and/or policy Researchers are able to use the AILQ as a guide to measure students' AI literacy. Practitioners are able to use the AILQ to assess students' AI literacy development.

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The study extends conceptual frameworks of AI literacy by specifying the structural components through which AI literacy can be cultivated beyond classroom instruction.
“it extends conceptual frameworks of AI literacy by specifying the structural components through which AI literacy can be cultivated beyond classroom instruction.”
From Toward Sustainable AI Literacy in Higher Education: A Delphi–AHP Approach to Identifying Key Components of AI/Data Extracurricular Programs (2026) · cites this paper as [25]
The 'Definition and significance of AI literacy' section says the study establishes affective, behavioural, cognitive and ethical dimensions of AI literacy development, supporting the claim that it extends a conceptual framework. The passages do not establish that these components cultivate AI literacy beyond classroom instruction; the study’s instructional-design passage describes a series of lessons and workshops. (LLM, conf=0.83)
Experimental — accuracy not yet measured
Most existing research on AI education in higher education has focused on conceptual frameworks for AI literacy, measurement instrument development, or pedagogical strategies for AI in classroom contexts.
“Most existing research on AI education in higher education has focused on conceptual frameworks for AI literacy [1,9,10], measurement instrument development [25], or pedagogical strategies for AI in classroom contexts [30].”
From Toward Sustainable AI Literacy in Higher Education: A Delphi–AHP Approach to Identifying Key Components of AI/Data Extracurricular Programs (2026) · cites this paper as [25]

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Using Bloom’s Digital Taxonomy as a framework to evaluate webcast learning experience in the context of Covid-19 pandemic
Ögeyik, M. C. (2022) · Education and Information Technologies · doi:10.1007/s10639-022-11064-x · Cited by 31 · see citing works
From machine ethics to computational ethics
Segun, S. T. (2020) · AI & Society · doi:10.1007/s00146-020-01010-1 · Cited by 30 · see citing works
School leaders’ self-efficacy and job satisfaction over nine annual waves: A substantive-methodological synergy juxtaposing competing models of directional ordering
Marsh, H. W., Lüdtke, O., Pekrun, R. H., Parker, P. D., Murayama, K., Guo, J., Basarkod, G., Dicke, T., Donald, J. N., & Morin, A. J. S. (2023) · Contemporary Educational Psychology · doi:10.1016/j.cedpsych.2023.102170 · Cited by 23 · see citing works
The Influence of Self-Regulation on Learner’s Behavioral Intention to Reuse E-Learning Systems: A Moderated Mediation Model
Xu, J., & Qiu, X. (2021) · Frontiers in Psychology · doi:10.3389/fpsyg.2021.763889 · Cited by 16 · see citing works
Is Science Me? Exploring Middle School Students’ STE-M Career Aspirations
Aschbacher, P. R., Ing, M., & Tsai, S. M. (2014) · Journal of Science Education and Technology · doi:10.1007/s10956-014-9504-x · Cited by 11 · see citing works
The ARCS Model for the Design of Motivationally Adaptive Computer- Mediated Instruction
Song, S. H. (1998) · Journal of Educational Technology · doi:10.17232/kset.14.1.119 · Cited by 10 · see citing works
Open artificial intelligence - one course for all
Merzbacher, M. (2001) · ACM Technical Symposium on Computer Science Education (SIGCSE) · doi:10.1145/364447.364554 · Cited by 4 · see citing works

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Sorted by citations

Empowering student self‐regulated learning and science education through ChatGPT : A pioneering pilot study
Ng, D. T. K., Tan, C. W., & Leung, J. K. L. (2024) · British Journal of Educational Technology · doi:10.1111/bjet.13454 · Cited by 531 · see citing works
Self-citation
A systematic review of AI literacy scales
Lintner, T. (2024) · npj Science of Learning · doi:10.1038/s41539-024-00264-4 · Cited by 292 · see citing works
Artificial intelligence in higher education: exploring faculty use, self-efficacy, distinct profiles, and professional development needs
Mah, D.-K., & Groß, N. (2024) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-024-00490-1 · Cited by 218 · see citing works
“One prominent aspect affecting the acceptance of AI and digital transformation among lecturers is lecturers’ perceived self-efficacy (Ajzen, 2002; Bandura, 1977) and literacy in utilizing these technologies (Ng et al., 2023; Wang et al., 2023).”
Navigating the Ethical Terrain of AI in Education: A Systematic Review on Framing Responsible Human-Centered AI Practices
Fu, Y., & Weng, Z. (2024) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2024.100306 · Cited by 195 · see citing works
Investigating L2 writers' critical AI literacy in AI-assisted writing: An APSE model
Wang, C., & Wang, Z. (2025) · Journal of Second Language Writing · doi:10.1016/j.jslw.2025.101187 · Cited by 140 · see citing works
A critical review of teaching and learning artificial intelligence (AI) literacy: Developing an intelligence-based AI literacy framework for primary school education
Yim, I. H. Y. (2024) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2024.100319 · Cited by 197 · see citing works
Fostering students' AI literacy development through educational games: AI knowledge, affective and cognitive engagement
Ng, D. T. K., Xinyu, C., Leung, J. K. L., & Chu, S. K. W. (2024) · Journal of Computer Assisted Learning · doi:10.1111/jcal.13009 · Cited by 107 · see citing works
Self-citation
Artificial Intelligence in Higher Education: The Impact of Need Satisfaction on Artificial Intelligence Literacy Mediated by Self-Regulated Learning Strategies
Wang, K., Cui, W., & Xue, Y. (2025) · Behavioral Sciences · doi:10.3390/bs15020165 · Cited by 120 · see citing works
“Other AI literacy studies, such as those by H. Zhang et al. (2024) and Ng et al. (2024), focus primarily on middle school or secondary education students.”
GLAT: The generative AI literacy assessment test
Jin, Y., Martínez‐Maldonado, R., Gašević, D., & Yan, L. (2025) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2025.100436 · Cited by 119 · see citing works
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
AI Literacy in K-12 and Higher Education in the Wake of Generative AI: An Integrative Review
Gu, X., & Ericson, B. J. (2025) · ACM Conference on International Computing Education Research (ICER) · doi:10.1145/3702652.3744217 · Cited by 113 · see citing works
“…surface machine learning principles and ethics, having learners exchange narrative stories about their experiences with AI online [83], dancing activities to teach posture recognition [17], and traditional lectures and assessments that cover both technical AI knowledge and AI ethics [53, 59, 88].”
Artificial Intelligence in the Education of Teachers: A Qualitative Synthesis of the Cutting-Edge Research Literature
Meylani, R. (2024) · Journal of Computer and Education Research · doi:10.18009/jcer.1477709 · Cited by 97 · see citing works
Evaluating AI literacy of secondary students: Framework and scale development
Zhong, B., & Liu, X. (2024) · Computers & Education · doi:10.1016/j.compedu.2024.105230 · Cited by 85 · see citing works
Generative AI in Human-AI Collaboration: Validation of the Collaborative AI Literacy and Collaborative AI Metacognition Scales for Effective Use
Sidra, S., & Mason, C. (2025) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2025.2543997 · Cited by 70 · see citing works
“Among the self-report tools, scales such as the AI Literacy Scale (AILS) (Wang et al., 2023), AI Literacy Questionnaire (AILQ) (Ng et al., 2024), and the Meta AI Literacy Scale (MAILS) (Carolus et al., 2023a) offer multidimensional assessments of AI literacy in student and general populations.”
A systematic literature review of generative artificial intelligence (GenAI) literacy in schools
Park, J. (2025) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2025.100487 · Cited by 89 · see citing works
Fostering responsible AI literacy: A systematic review of K-12 AI ethics education
Ma, M., Ng, D. T. K., Liu, Z., & Wong, G. K. W. (2025) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2025.100422 · Cited by 78 · see citing works
Self-citation
Investigating the level of artificial intelligence literacy of university students using decision trees
Gökçe, A. T., Topal, A. D., Geçer, A. K., & Eren, C. D. (2024) · Education and Information Technologies · doi:10.1007/s10639-024-13081-4 · Cited by 61 · see citing works
“Educational programs that incorporate AI training equip students with the necessary skills and instill a sense of readiness and confidence to effectively use AI tools in their academic and future professional endeavors (Ng et al., 2024).”
A multinational assessment of AI literacy among university students in Germany, the UK, and the US
Hornberger, M., Bewersdorff, A., Schiff, D. S., & Nerdel, C. (2025) · Computers in Human Behavior Artificial Humans · doi:10.1016/j.chbah.2025.100132 · Cited by 57 · see citing works
Generative Artificial Intelligence Literacy: Scale Development and Its Effect on Job Performance
Xin, L., Zhang, L., & Wei, X. (2025) · Behavioral Sciences · doi:10.3390/bs15060811 · Cited by 71 · see citing works
Meta AI literacy scale: Further validation and development of a short version
Koch, M. J., Carolus, A., Wienrich, C., & Latoschik, M. E. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e39686 · Cited by 44 · see citing works
Enhancing student GAI literacy in digital multimodal composing through development and validation of a scale
Liu, M., Zhang, L. J., & Zhang, D. (2025) · Computers in Human Behavior · doi:10.1016/j.chb.2025.108569 · Cited by 43 · see citing works
The AI Motivation Scale (AIMS): a self-determination theory perspective
Li, J., King, R. B., Chai, C. S., Zhai, X., & Lee, V. W. Y. (2025) · Journal of Research on Technology in Education · doi:10.1080/15391523.2025.2478424 · Cited by 59 · see citing works
“Research on the use of AI in learning is also growing exponentially (Chiu et al., 2023c; Ng et al., 2023a; Xia et al., 2024).”
Influential
The AI literacy heptagon: A structured approach to AI literacy in higher education
Hackl, V., Müller, A. E., & Sailer, M. (2026) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2026.100540 · Cited by 41 · see citing works
“Existing instruments such as SNAIL (Laupichler et al., 2023) emphasize technical, critical appraisal and application dimensions; more recent questionnaires such as AILQ (Ng et al., 2024) or AICOS (Markus et al., 2025) provide broader perspectives.”
Unveiling AI literacy in K-12 education: a systematic literature review of empirical research
Tan, Q., & Tang, X. (2025) · Interactive Learning Environments · doi:10.1080/10494820.2025.2482586 · Cited by 50 · see citing works
“The most common pedagogical approaches include collaborative learning, project-based/problem-based learning, learning with game elements, scaffolding support, and online learning strategies (Ng et al., 2023).”
Influential
The artificial intelligence literacy (AIL) scale for teachers: A tool for enhancing AI education
Younis, B. (2025) · Journal of Digital Learning in Teacher Education · doi:10.1080/21532974.2024.2441682 · Cited by 72 · see citing works
“Ng et al. (2023) further suggested a set of AI competencies for teachers, such as using basic applications, managing information, creating learning content, and connecting with students via technology.”
AI’s role in transforming learning environments: a review of collaborative approaches and innovations
Mariyono, D., & Hd, A. N. A. (2025) · Quality Education for All · doi:10.1108/qea-08-2024-0071 · Cited by 66 · see citing works
Influence of AI-driven educational tools on critical thinking dispositions among university students in Malaysia: a study of key factors and correlations
Hao, L., Yusoff, S. M., & Marzaini, A. F. M. (2024) · Education and Information Technologies · doi:10.1007/s10639-024-13150-8 · Cited by 44 · see citing works
The development and validation of a scale on student AI literacy in L2 writing: A domain-specific perspective
Xu, L., Zhang, L., Ling, O., & Wang, D. (2025) · Journal of Second Language Writing · doi:10.1016/j.jslw.2025.101227 · Cited by 32 · see citing works
Understanding the potential role of GenAI-mediated informal digital learning of English (GenAI-IDLE) in the Global South: AI literacy, emotions, and willingness to communicate as outcomes
Zou, M., Reinders, H., & Amjad, F. (2025) · ReCALL · doi:10.1017/s0958344025100360 · Cited by 32 · see citing works
“Considering new technologies (e.g. generative AI) continue to grow and reframe the digital realm (Liu et al ., 2024a), digital literacy should also advance to include AI literacy to prepare students to become responsible digital citizens in the digital era (Ng et al ., 2024).”
AcademAI: Investigating AI Usage, Attitudes, and Literacy in Higher Education and Research
Brown, R., Sillence, E., & Branley-Bell, D. (2025) · Journal of Educational Technology Systems · doi:10.1177/00472395251347304 · Cited by 43 · see citing works
“It is important to measure people ’ s attitudes towards AI in order to determine individual differences in acceptance and use (Ng, Wu, et al., 2024).”
Developing and validating an artificial intelligence ethical awareness scale for secondary and university students: Cultivating ethical awareness through problem-solving with artificial intelligence tools
Kong, S. C., & Zhu, J. (2025) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2025.100447 · Cited by 47 · see citing works
Development and validation of an artificial intelligence literacy assessment for kindergarten children
Su, J. (2024) · Education and Information Technologies · doi:10.1007/s10639-024-12611-4 · Cited by 30 · see citing works
“Ng et al. ( 2023 ) developed an AI literacy question - naire to assess secondary schools students’ literacy development.”
Understanding the role of AI in Malaysian higher education curricula: an analysis of student perceptions
Yusoff, S. M., Marzaini, A. F. M., Hao, L., Zainuddin, Z., & Basal, M. H. (2025) · Discover Computing · doi:10.1007/s10791-025-09567-5 · Cited by 29 · see citing works
Enhancing EFL Learners' Engagement and Motivation Through Immersive Technologies: The Role of Artificial Intelligence, Augmented Reality, Virtual Reality, and Mobile Applications
Zhang, Y., & Miao, Z. (2025) · European Journal of Education · doi:10.1111/ejed.70128 · Cited by 28 · see citing works
AI literacy as a core component of AI education
Tadimalla, S. Y., & Maher, M. L. (2025) · AI Magazine · doi:10.1002/aaai.70007 · Cited by 48 · see citing works
Objective measurement of AI literacy: Development and validation of the AI competency objective scale (AICOS)
Markus, A., Carolus, A., & Wienrich, C. (2025) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2025.100485 · Cited by 45 · see citing works
Investigating the mediating role of TPACK on teachers’ AI competency and their teaching performance in higher education
Tan, X., Cheng, G., & Ling, M. H. (2025) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2025.100461 · Cited by 27 · see citing works
Overview and confirmatory and exploratory factor analysis of AI literacy scale
Koch, M. J., Wienrich, C., Straka, S., Latoschik, M. E., & Carolus, A. (2024) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2024.100310 · Cited by 28 · see citing works
Developing and validating a scale of empowerment in using artificial intelligence for problem-solving for senior secondary and university students
Kong, S. C., Zhu, J., & Yang, Y. N. (2024) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2024.100359 · Cited by 28 · see citing works
Perspectives of academic staff on artificial intelligence in higher education: exploring areas of relevance
Mah, D.-K., Knoth, N., & Egloffstein, M. (2025) · Frontiers in Education · doi:10.3389/feduc.2025.1484904 · Cited by 35 · see citing works
Factors influencing international students’ adoption of generative artificial intelligence
Ittefaq, M., Zain, A., Arif, R., Taufiq, A., Khan, M. L., & Seo, H. (2025) · Journal of International Students · doi:10.32674/fnwdpn48 · Cited by 29 · see citing works
Beyond tool use: Tracking the evolution of generative AI literacy among university students through a process-oriented investigation
Yan, W., Nakajima, T., & Sawada, R. (2025) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2025.100465 · Cited by 39 · see citing works
Exploring researchers’ artificial intelligence (AI) literacy: The mediating role of digital literacy and data literacy between 21st century skills and AI literacy
Seker, O., Kwon, K., & Kocak, O. (2025) · Information Development · doi:10.1177/02666669251336368 · Cited by 21 · see citing works
Ethical Use of Generative Artificial Intelligence Among Ecuadorian University Students
Buele, J., García, Á. R. S., Sabando-García, B. J., & Yánez-Rueda, H. (2025) · Sustainability · doi:10.3390/su17104435 · Cited by 21 · see citing works
Analyzing the network structure of students’ motivation to learn AI: a self-determination theory perspective
Li, J., Zhang, J., Chai, C. S., Lee, V. W. Y., Zhai, X., Wang, X., & King, R. B. (2025) · npj Science of Learning · doi:10.1038/s41539-025-00339-w · Cited by 29 · see citing works
Assessing AI literacy in college students: the mediating role of self-efficacy in motivational commitment pathways
Kong, J., Liu, J., Chen, G., & Shang, W. (2025) · Education and Information Technologies · doi:10.1007/s10639-025-13753-9 · Cited by 24 · see citing works
“Recently, several researchers have introduced questionnaires to investigate AI literacy, particularly in K-12 settings (Chai et al., 2020; Ng et al., 2022, 2023a, b; Stolpe & Hallström, 2024; Xia et al., 2022).”
Influential
AI literacy and gender equity in elementary education: A quasi-experimental study of a STEAM–PBL–AIoT course with questionnaire validation
Cheng, C.-C., Wang, J.-S., Zhaı, X., & Yang, Y. (2025) · International Journal of STEM Education · doi:10.1186/s40594-025-00574-y · Cited by 22 · see citing works
“Students with well-rounded AI literacy are more likely to engage with AI technologies ethically and proactively, contributing to societal well-being (Ng et al., 2024c).”
Influential
Multinational validation of the Arabic version of the Artificial Intelligence Literacy Scale (AILS) in university students
Hobeika, E., Hallit, R. R., Malaeb, D. N., Sakr, F., Dabbous, M., Merdad, N., Rashid, T., Amin, R., Jebreen, K., Zarrouq, B., et al. (2024) · Cogent Psychology · doi:10.1080/23311908.2024.2395637 · Cited by 23 · see citing works
“Mainly, validity and reliability must be carefully assessed (ng et al., 2023). hence the efforts to publish data related to the psychometric properties of the ails in arabic. the current study aimed to evaluate the psychometric properties of a first arabic translation of the ails among university…”
AI Literacy and Digital Wellbeing: The Multiple Mediating Roles of Positive Attitudes Towards AI and Satisfying Basic Psychological Needs
Kaya, B., Çınar, S., & Önder, F. C. (2025) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2025.2499162 · Cited by 21 · see citing works
Development and evaluation of artificial intelligence literacy training for teacher education students
Le, T. H., Huynh, L., Dang, B., Pham, H., Nguyen, N. T. V., & Nguyen, A. (2026) · British Journal of Educational Technology · doi:10.1111/bjet.70047 · Cited by 29 · see citing works
From revolution to evolution: What generative AI really means for language learning
Pegrum, M. A. (2025) · Language Teaching · doi:10.1017/s0261444825000151 · Cited by 17 · see citing works
A systematic review on the future of educational assessment: AI-driven grading and personalised feedback in higher education
Deepshikha, D. (2025) · International Conference on Artificial Intelligence in Education · doi:10.1108/aiie-03-2025-0036 · Cited by 16 · see citing works
Enhancing Competence for a Sustainable Future: Integrating Artificial Intelligence–Supported Educational Technologies in Pre‐Service Teacher Training for Sustainable Development
Kayaalp, F., Durnalı, M., & GÖKBULUT, B. (2024) · European Journal of Education · doi:10.1111/ejed.12865 · Cited by 24 · see citing works
Evidence from West Africa on the interplay of affective behavioral cognitive and ethical dimensions of AI literacy in Ghanaian and Nigerian Universities
Asghar, M. Z., Duah, K. A., Iqbal, J., & Järvenoja, H. (2025) · Discover Computing · doi:10.1007/s10791-025-09691-2 · Cited by 20 · see citing works
“The study measured four constructs—affective , behavioral , cognitive, and ethical learn-ing—using validated scales adapted from Ng et al., [74].”
Influential
Developing a college student feedback literacy scale for the GenAI context
Cui, Y. H., Meng, Y., Qian, X., & Tang, L. (2025) · Assessment & Evaluation in Higher Education · doi:10.1080/02602938.2025.2495048 · Cited by 17 · see citing works
“These principles are crucial for developing a set of assessment items that evaluate how students cultivate proper and ethical practices when engaging with GenAI-generated feedback (Ng et al. 2024).”
Influential
AI sensation and engagement: Unpacking the sensory experience in human-AI interaction
Foroudi, P., Marvi, R., & Zha, D.-M. (2025) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2025.102918 · Cited by 16 · see citing works
The nexus of artificial intelligence literacy collaborative knowledge practices and inclusive leadership development among higher education students in Bangladesh China Finland and Turkey
Asghar, M., Iqbal, J., Özbilen, F. M., Abedin, J., Järvenoja, H., & Widanapathirana, U. (2025) · Discover Computing · doi:10.1007/s10791-025-09695-y · Cited by 16 · see citing works
“AIL was assessed using the validated AILQ Scale [61], which comprises four sub-factors grounded in affective, behavioral, cognitive, and ethical dimensions of AI competence.”
Influential
Artificial intelligence literacy at school: A systematic review with a focus on psychological foundations
Feng, S., & Carolus, A. (2026) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2026.100551 · Cited by 24 · see citing works
Exploring Students’ Multimodal Representations of Ideas About Epistemic Reading of Scientific Texts in Generative AI Tools
Cheung, K. K. C., Pun, J., Kenneth-Li, W., & Mai, J. (2024) · Journal of Science Education and Technology · doi:10.1007/s10956-024-10182-0 · Cited by 14 · see citing works
“E2, have gained an increasing popularity in science education (Alasadi & Baiz, 2024; Ng et al., 2023a), while research instruments that measure students’ competence of using AI in disciplinary learning are mostly in the form of Likert scale (e.g., Ng et al., 2023a, b).”
“Won’t somebody please (actually) think of the children?” AI Ethics for Children: A Scoping Review
Collyer-Hoar, G., & Rubegni, E. (2025) · ACM Journal on Responsible Computing · doi:10.1145/3745031 · Cited by 14 · see citing works
“…pedagogy in differing manners, such as through the creation of new educational materials [8, 186], and several proof-of-concept studies exploring new methodologies for teaching [48, 49, 69, 80, 84, 93, 121, 133, 142, 160, 161, 176, 200] or assessing [134] AI literacy in differing ages of children.”
Dimensions of Artificial Intelligence Literacy: A Qualitative Synthesis of Contemporary Research Literature
Kaplan, R., & Meylani, R. (2025) · Journal of Computer and Education Research · doi:10.18009/jcer.1648380 · Cited by 18 · see citing works
Integrating AI literacy into the TAM-TPB model to explain students’ intention to use educational AI through MASEM approach
Yang, H., Xu, N., Lin, X., & Zhang, W. (2025) · Computers in Human Behavior Reports · doi:10.1016/j.chbr.2025.100833 · Cited by 21 · see citing works
Conceptualizing AI literacies for children and youth: A systematic review on the design of AI literacy educational programs
Atias, O., & Mawasi, A. (2025) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2025.100491 · Cited by 21 · see citing works
Development and validation of moral ascendancy and dependency in AI integration (MAD-AI) scale for teachers
Jorolan, J., Cabillo, F., Batucan, R. R., Camansi, C. M., Etoquilla, A., Gapo, J., Hallarte, D. K., Milano, M. L., Gonzales, R. R., & Gonzales, G. G. (2025) · Computers & Education · doi:10.1016/j.compedu.2025.105346 · Cited by 13 · see citing works
Theorizing AI literacy development using Habermas' three cognitive knowledge interests from a systematic review: A STEM interdisciplinary perspective
Heung, Y. M. E., Yin, H., English, L. D., & Chiu, T. K. F. (2025) · Computers & Education · doi:10.1016/j.compedu.2025.105492 · Cited by 21 · see citing works
Self-citation
A Systematic Review Mapping of AI Literacy Progression in K–12
Yang, H., Rachmatullah, A., Alozie, N. M., Capan, S., & Cao, Q. (2026) · Journal for STEM Education Research · doi:10.1007/s41979-025-00166-z · Cited by 13 · see citing works
Empowered by AI: exploring the link between literacy, self-efficacy, and career expectations
Ho, N. T. T., & Le, H. V. (2026) · Acta Psychologica · doi:10.1016/j.actpsy.2026.106408 · Cited by 13 · see citing works
The development and validation of the artificial intelligence literacy scale for middle school English teachers
Gao, Y., & Wang, X. (2026) · Language Teaching Research · doi:10.1177/13621688261416208 · Cited by 17 · see citing works
Examining the behavioral determinants of AI adoption in higher education: a focus on perceptional factors and demographic differences
Syed, A. Z., Memon, Z. H., Khan, K., Hameed, I., & Nadeem, M. (2025) · On the Horizon The International Journal of Learning Futures · doi:10.1108/oth-02-2025-0019 · Cited by 12 · see citing works
A scoping review of empirical research on AI literacy assessments
Su, J., Chen, X., Chu, S. K. W., & Hu, X. (2025) · Educational Technology Research and Development · doi:10.1007/s11423-025-10515-9 · Cited by 13 · see citing works
“Five articles used tests to evaluate students’ AI literacy (e.g., Ng et al., 2023; Su & Yang, 2023a; Su, 2024; Zhang et al., 2025).”
InfluentialSelf-citation
Unlocking AI potential in primary mathematics: Teachers' technological pedagogical readiness in China
Zhao, G., Li, M., Long, Z., & Fan, H. (2025) · Acta Psychologica · doi:10.1016/j.actpsy.2025.105558 · Cited by 20 · see citing works
Artificial intelligence and students’ cognitive learning outcomes with bibliometric and content analysis for future research agenda
Ansari, S. R., & Qamari, I. N. (2025) · Discover Education · doi:10.1007/s44217-025-00865-0 · Cited by 32 · see citing works
Influence of perceived ethics, prejudice, and teacher concerns on artificial intelligence literacy and implementation: A cross-sectional study using path analysis
Acosta-Enriquez, B. G., Huamaní Jordan, O., Morales-Angaspilco, J. E., Campoverde Ventura, G., Ruiz Carrillo, J. A., Blanco- García, L. E., & Huaranga Rivera, L. (2025) · Computers in Human Behavior Reports · doi:10.1016/j.chbr.2025.100829 · Cited by 11 · see citing works
Toward a Coherent AI Literacy Pathway in Technology Education: Bibliometric Synthesis and Cross-Sectional Assessment
Rupnik, D., & Avsec, S. (2025) · Education Sciences · doi:10.3390/educsci15111455 · Cited by 11 · see citing works
“Embedding these into course sequences and practica should raise measured performance on the very constructs that define the field’s present phase (T. Chiu et al., 2024; Ng et al., 2024).”
Impacto de la Inteligencia Artificial en el Rendimiento Académico de Estu-diantes de Secundaria: Un Estudio Correlacional
Cobos-Gutierrez, C. E. (2024) · PURIQ · doi:10.37073/puriq.6.740 · Cited by 14 · see citing works
From Transformative Agency to AI Literacy: Profiling Slovenian Technical High School Students Through the Five Big Ideas Lens
Avsec, S., & Rupnik, D. (2025) · Systems · doi:10.3390/systems13070562 · Cited by 17 · see citing works
Artificial intelligence in academic libraries – a tool, a collaborator, an adversary? A Delphi study of university librarians in Poland
Kisilowska, M. (2025) · The Journal of Academic Librarianship · doi:10.1016/j.acalib.2025.103114 · Cited by 15 · see citing works
Digital Competencies for Effective GenAI Use in Secondary Schools: A Longitudinal Exploration of Teachers' Perspectives and Classroom Practices
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Background

Authors: Ng, D. T. K., Wu, W., Leung, J. K. L., Chiu, T. K. F., & Chu, S. K. W.
Affiliations: University of Hong Kong (HK)
Chinese University of Hong Kong (HK)
Hong Kong University of Science and Technology (HK)
Hong Kong Metropolitan University (HK)
Corresponding author: Ng, Davy Tsz Kit
ORCID: Ng, Davy Tsz Kit
Wu, Wenjie
Leung, Jac Ka Lok
Chiu, Thomas Kin Fung
Chu, Samuel Kai Wah
Venue: British Journal of Educational Technology
Publisher: Wiley
Published: 2023-12-13
Volume: 55
Issue: 3
Pages: 1082-1104
ISSN: 0007-1013 (ISSN-L)
1467-8535
DOI: 10.1111/bjet.13411
OpenAlex: W4389681621
Cited by: 390
References: 93
Citations by year: 2026: 230 · 2025: 134 · 2024: 26
FWCI: 91.53
Type: journal article
Language: en
Open access: hybrid
doi.org
Licence: cc-by-nc
creativecommons.org/licenses/by-nc/4.0/ (vor)
doi.wiley.com/10.1002/tdm_license_1.1 (tdm)
Locations: British Journal of Educational Technology (publishedVersion, cc-by-nc)
Rare & Special e-Zone (The Hong Kong University of Science and Technology) (submittedVersion)
Topics: Physical Sciences › Computer Science › Information Systems › Digital literacy in education
Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
Social Sciences › Library and Information Sciences › Library Science and Information Literacy
Keywords: AI literacy, high school students, affective learning, behavioural learning, cognitive learning, ethical learning, psychometric validation, self-efficacy, intrinsic motivation, collaboration, artificial intelligence in education, digital literacy

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Design and validation of the AI literacy questionnaire: The affective, behavioural, cognitive and ethical approach

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