Exploratory Indonesian Version of the Artificial Intelligence Literacy Scale (AILQ).
DOI:
https://doi.org/10.62260/causalita.v4i1.455Keywords:
AI literacy, instrument adaptation, construct validity, exploratory factor analysis, AILQAbstract
This study aims to linguistically and culturally adapt the Artificial Intelligence Literacy Questionnaire (AILQ) into Indonesian and to evaluate its validity and reliability using an Exploratory Factor Analysis (EFA) approach. The participants consisted of 351 students aged 16 to 18 years, selected through purposive sampling from urban areas. The data eligibility test showed a Kaiser-Meyer-Olkin (KMO) value of 0.823 and a significant Bartlett’s Test result (p < .001), indicating that the data were suitable for factor analysis. The EFA identified four main factors aligned with the ABCE theoretical framework (Affective, Behavioural, Cognitive, Ethical), which collectively explained 24.6% of the total variance. All items showed adequate factor loadings and uniqueness values, supporting the multidimensional interpretation of the scale. Reliability testing revealed excellent internal consistency, with Cronbach’s Alpha of 0.848 and Omega coefficient of 0.849. These findings support that the Indonesian version of AILQ is a valid, reliable, and culturally appropriate tool for measuring AI literacy among secondary school students. This study contributes to the development of technology-based assessment instruments that are contextually relevant and responsive to the educational demands of the digital era.
References
Agarwal, P. K. (2019). Powerful teaching: Unleash the science of learning. Jossey-Bass.
Aschbacher, P. R., Li, E., & Roth, E. J. (2014). Is science me? High school students’ identities, participation and aspirations in science, engineering, and medicine. Journal of Research in Science Teaching, 51(7), 845–878. https://doi.org/10.1002/tea.21141
Audrin, C., & Audrin, C. (2022). Digital literacy as a predictor of well-being: A scoping review. Education and Information Technologies, 27, 4911–4935. https://doi.org/10.1007/s10639-021-10736-5
Bandalos, D. L., & Finney, S. J. (2018). Exploratory and confirmatory factor analysis. In G. R. Hancock, L. M. Stapleton, & R. O. Mueller (Eds.), The reviewer’s guide to quantitative methods in the social sciences (2nd ed., pp. 97–122). Routledge.
Ben-Eliyahu, A., Moore, D., Dorph, R., & Schunn, C. D. (2018). Investigating the multidimensionality of engagement: Affective, behavioral, and cognitive engagement across science activities and contexts. Contemporary Educational Psychology, 53, 87–105. https://doi.org/10.1016/j.cedpsych.2018.01.002
Borenstein, J., & Howard, A. (2021). Emerging challenges in AI and the need for AI ethics education. AI and Ethics, 1, 61–65. https://doi.org/10.1007/s43681-020-00002-7
Celik, I. (2023). AI ethics education: A framework for promoting ethical literacy in AI use. Technology, Knowledge and Learning. https://doi.org/10.1007/s10758-023-09641-0
Chiu, T. K. F., & Chai, C. S. (2020). Digital teaching and learning: A systematic review of theory and practice. Education and Information Technologies, 25, 5115–5147. https://doi.org/10.1007/s10639-020-10261-6
Chiu, T. K., Sun, J. C. Y., & Ismailov, M. (2022). Investigating the relationship of technology learning support to digital literacy from the perspective of self-determination theory. Educational Psychology, 42(10), 1263–1282.
Choi, M., Glassman, M., & Cristol, D. (2017). What it means to be a citizen in the internet age: Development of a reliable and valid digital citizenship scale. Computers & Education, 107, 100–112. https://doi.org/10.1016/j.compedu.2017.01.002
Coşgun Ögeyik, M. (2022). Using Bloom’s digital taxonomy as a framework to evaluate webcast learning experience in the context of Covid-19 pandemic. Education and Information Technologies, 27(8), 11219–11235. https://doi.org/10.1007/s10639-022-11064-x
Dai, D. Y., Wang, X., & Chen, F. (2020). Artificial intelligence literacy development and assessment in K–12: A review. Computers & Education: Artificial Intelligence, 1, 100002. https://doi.org/10.1016/j.caeai.2020.100002
Hambleton, R. K., & Kanjee, A. (1995). Increasing the validity of cross-cultural assessments: Use of improved methods for test adaptations. European Journal of Psychological Assessment, 11(3), 147–157. https://doi.org/10.1027/1015-5759.11.3.147
International Test Commission. (2017). The ITC guidelines for translating and adapting tests (Second edition). https://www.intestcom.org/files/guideline_test_adaptation_2ed.pdf
Jin, K. Y., Reichert, F., Cagasan, L. P., Jr., de La Torre, J., & Law, N. (2020). Measuring digital literacy across three age cohorts: Exploring test dimensionality and performance differences. Computers & Education, 157, 103968. https://doi.org/10.1016/j.compedu.2020.103968
Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature Machine Intelligence, 1(9), 389–399. https://doi.org/10.1038/s42256-019-0088-2
Jung, Y., & Lee, J. (2018). Learning engagement and persistence in massive open online courses (MOOCs). Computers & Education, 122, 9–22. https://doi.org/10.1016/j.compedu.2018.02.013
Kandlhofer, M., Steinbauer, G., Hirschmugl-Gaisch, S., & Huber, P. (2016, October). Artificial intelligence and computer science in education: From kindergarten to university. In IEEE Frontiers in Education Conference (FIE) (pp. 1–9). IEEE. https://doi.org/10.1109/FIE.2016.7757570
Kell, H. J., & Motowidlo, S. J. (2012). Deconstructing organizational commitment: Associations among its affective and cognitive components, personality antecedents, and behavioral outcomes. Journal of Applied Social Psychology, 42(1), 213–251.
Kim, H. L., & Han, J. (2019). Do employees in a “good” company comply better with information security policy? A corporate social responsibility perspective. Information Technology & People, 32(4), 858–875. https://doi.org/10.1108/ITP-09-2017-0298
Kim, M., & Choi, D. (2018). Development of youth digital citizenship scale and implication for educational setting. Journal of Educational Technology & Society, 21(1), 155–171. https://www.jstor.org/stable/26273877
Long, D., & Magerko, B. (2020). What is AI literacy? Competencies and design considerations. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (pp. 1–16). https://doi.org/10.1145/3313831.3376727
Marsh, H. W., Pekrun, R., Parker, P. D., Murayama, K., Guo, J., Dicke, T., & Lüdtke, O. (2023). Dimensional comparison processes in students’ self-concepts and achievements: An integrative framework and new insights. American Psychologist, 78(2), 226–245. https://doi.org/10.1037/amp0000994
Ng, D. T. K., Wu, W., Leung, J. K. L., Chiu, T. K. F., & Chu, S. K. W. (2023). Design and validation of the AI literacy questionnaire: The affective, behavioural, cognitive and ethical approach. British Journal of Educational Technology, 55(3), 1082–1104. https://doi.org/10.1111/bjet.13411
Pradana, R. (2023). Penggunaan Artificial Intelligence dalam pendidikan dan tantangan pembelajaran di era digital. Jurnal Teknologi dan Pendidikan, 6(2), 102–114.
UNESCO. (2022). AI and education: Guidance for policy-makers. https://unesdoc.unesco.org/ark:/48223/pf0000376709
Younis, S. (2025). Psychometric evaluation of the AI Literacy Questionnaire in a cross-national sample. Journal of Educational Measurement and Assessment, 18(1), 45–59.
Yuliana, S., & Santoso, H. B. (2024). Analisis perilaku siswa dalam pemanfaatan AI tools untuk kebutuhan belajar di sekolah menengah atas. Jurnal Teknologi dan Pendidikan, 9(1), 45–56.
Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education – Where are the educators? International Journal of Educational Technology in Higher Education, 16(1), 1–27. https://doi.org/10.1186/s41239-019-0171-0
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