Main Article Content

Abstract

Artificial intelligence (AI) integration in higher education has increased the need for context-sensitive AI literacy frameworks aligned with ethical and educational values. This study aimed to develop and validate the Artificial Intelligence Literacy Scale for Indonesian Islamic University Students (AILS-IIUS) within the framework of Islamic AI literacy. Grounded in the principles of adab, amanah, ‘adl, maslahah, and tabayyun, the study conceptualized AI literacy through four dimensions: Awareness, Usage, Evaluation, and Ethics. Using a quantitative scale-development design, data were collected from 361 undergraduate students enrolled at Islamic higher education institutions in Indonesia. Furthermore, the instrument development process involved expert validation, contextual refinement, exploratory factor analysis (EFA), confirmatory factor analysis (CFA), and regression analysis. Additionally, the findings produced a valid 15-item instrument explaining 72.38% of the total variance. The four-factor model demonstrated satisfactory model fit, reliability, convergent validity, and discriminant validity. Furthermore, the frequency of AI use significantly predicted all dimensions of AI literacy. Based on these results, the study concludes that AI literacy in Islamic higher education should incorporate conceptual understanding, evaluative judgment, ethical engagement, and responsible AI-supported learning. Ultimately, the AILS-IIUS provides an Islamically grounded framework to support ethical AI pedagogy and curriculum development in Islamic higher education.

Keywords

AI Literacy Ethical AI Education Islamic Educational Values Islamic Higher Education Responsible Knowledge Practice Scale Development and Validation

Article Details

How to Cite
Syamsudduha, S., Suwandi , S., & ZA, T. (2026). Islamic AI Literacy as Responsible Knowledge Practice: Development and Validation of AILS-IIUS in Indonesian Islamic Higher Education. Jurnal Pendidikan Islam, 12(1), 187–201. https://doi.org/10.15575/jpi.v12i1.54254

References

  1. Alamäki, A., Nyberg, C., Kimberley, A., & Salonen, A. O. (2024). Artificial intelligence literacy in sustainable development: A learning experiment in higher education. Frontiers in Education, 9, Article 1343406. https://doi.org/10.3389/feduc.2024.1343406
  2. Al-Attas, S. M. N. (Ed.). (1979). Aims and objectives of Islamic education. London: Hodder and Stoughton.
  3. Atika, Q., Najmul, H., Olusola, A. A., Hardaker, G., Ronny, S., Yeahia, S., Paul, S. K., & Zubairu, M. J. (2022). Gender differences in information and communication technology use & skills: a systematic review and meta-analysis. Education and Information Technologies, 27(3), 4225-4258. https://doi.org/10.1007/s10639-021-10775-x
  4. Brennan, K., & Resnick, M. (2012). New frameworks for studying and assessing the development of computational thinking. Proceedings of the 2012 Annual Meeting of the American Educational Research Association, 1-25. Retrieved from http://scratched.gse.harvard.edu/ct/files/AERA2012.pdf
  5. Du, X., & Wang, X. (2023). Play by design: developing artificial intelligence literacy through game-based learning. Journal of Computer Science Research, 5(4), 1-12. https://doi.org/10.30564/jcsr.v5i4.5999
  6. Firdaus, S., Suwendi, S., Nafi’a, I., Gumiandari, S., Huriyah, H., & Juanda, A. (2025). Transforming Islamic higher education: Integrating Islamic values and digital technology at UIN Siber Cirebon. Jurnal Ilmiah Peuradeun, 13(3), 2337–2362. https://doi.org/10.26811/peuradeun.v13i3.2330
  7. Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.1177/002224378101800104
  8. Gokce, A.T, Deveci Topal, A., Kolburan Geçer, A., & Dilek Eren, C. (2025). Investigating the level of artificial intelligence literacy of university students using decision trees. Education and Information Technologies, 30(5), 6765-6784. https://doi.org/10.1007/s10639-024-13081-4
  9. Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate data analysis (8th ed.). Boston: Cengage Learning.
  10. Halstead, J. M. (2004). An Islamic concept of education. Comparative Education, 40(4), 517–529. https://doi.org/10.1080/0305006042000284510
  11. Hargittai, E., & Shafer, S. (2006). Differences in actual and perceived online skills: The role of gender. Social Science Quarterly, 87(2), 432–448. https://doi.org/10.1111/j.1540-6237.2006.00389.x
  12. Hwang, H. S., Zhu, L. C., & Cui, Q. (2023). Development and validation of a digital literacy scale in the artificial intelligence era for college students. KSII Transactions on Internet and Information Systems, 17(8), 2241–2258. https://doi.org/10.3837/tiis.2023.08.016
  13. Kline, R. B. (2016). Principles and practice of structural equation modeling (4th ed.). New York: Guilford Press.
  14. Kong, S.-C., Cheung, W. M.-Y., & Zhang, G. (2023). Evaluating an Artificial Intelligence Literacy Programme for Developing University Students’ Conceptual Understanding, Literacy, Empowerment and Ethical Awareness. Educational Technology & Society, 26(1), 16–30. Retrieved from https://www.jstor.org/stable/48707964
  15. Kuhlman, C., Jackson, L., & Chunara, R. (2020). No computation without representation: Avoiding data and algorithm biases through diversity. arXiv preprint arXiv:2002.11836. Retrieved from https://arxiv.org/abs/2002.11836
  16. Laupichler, D., Ng, D. T. K., & Pinski, F. (2023). Development of the scale for the assessment of non-experts’ AI literacy: An empirical study. Education and Information Technologies. Advance online publication. https://doi.org/10.1007/s10639-023-11234-2
  17. Long, D., & Magerko, B. (2020). What is AI literacy? Competencies and design considerations. Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, 1–16. https://doi.org/10.1145/3313831.3376727
  18. Ma, S., & Chen, Z. (2024). The development and validation of the Artificial Intelligence Literacy Scale for Chinese College Students (AILS-CCS). IEEE Access, PP(99), 1-1. https://doi.org/10.1109/ACCESS.2024.3468378
  19. Mansoor, H. M. H., Bawazir, A., Alsabri, M. A., Alharbi, A., & Okela, A. H. (2024). Artificial intelligence literacy among university students—a comparative transnational survey. Frontiers in Communication, 9, 1478476. https://doi.org/10.3389/fcomm.2024.1478476
  20. Ng, D. T. K., Leung, J. K. L., Chu, S. K. W., & Qiao, M. S. (2021). Conceptualizing AI literacy: An exploratory review. Computers and Education: Artificial Intelligence, 2, 100041. https://doi.org/10.1016/j.caeai.2021.100041
  21. Ng, D. T. K., Wu, W., Leung, J. K. L., & Chu, S. K. W. (2023). Artificial intelligence (AI) literacy questionnaire with confirmatory factor analysis. Proceedings of the IEEE International Conference on Advanced Learning Technologies, 233–235. https://doi.org/10.1109/ICALT58380.2023.00080
  22. Ng, W. (2021). AI literacy: Enhancing students’ abilities to critically understand and evaluate AI in their everyday lives. Computers & Education, 168, 104209. https://doi.org/10.1016/j.compedu.2021.104209
  23. Nong, Y., Cui, J., He, Y., Zhang, P., & Zhang, T. (2024). Development and Validation of an AI Literacy Scale. Journal of Artificial Intelligence Research, 1(1), 33-54. https://doi.org/10.70891/JAIR.2024.100029
  24. Rafida, T., Suwandi, S., & Ananda, R. (2024). EFL students’ perception in Indonesia and Taiwan on using artificial intelligence to enhance writing skills. Jurnal Ilmiah Peuradeun, 12(3), 987–1016. https://doi.org/10.26811/peuradeun.v12i3.1520
  25. Sahin, A. (2018). Critical issues in Islamic education studies: Rethinking Islamic and Western liberal secular values of education. Religions, 9(11), 335. https://doi.org/10.3390/rel9110335
  26. Schermelleh-Engel, K., Moosbrugger, H., & Müller, H. (2003). Evaluating the fit of structural equation models: Tests of significance and descriptive goodness-of-fit measures. Methods of Psychological Research Online, 8(2), 23–74. Retrieved from https://www.stats.ox.ac.uk/~snijders/mpr_Schermelleh.pdf
  27. Singh, E., Vasishta, P., & Singla, A. (2024). AI-enhanced education: Exploring the impact of AI literacy on generation Z’s academic performance in Northern India. Quality Assurance in Education, 33(2), 185–202. https://doi.org/10.1108/QAE-02-2024-0037
  28. Wang, B., & Chuang, Y.-W. (2023). Artificial intelligence self-efficacy: Scale development and validation. Education and Information Technologies, 29, 4785–4808. https://doi.org/10.1007/s10639-023-12015-w
  29. Wang, B., Rau, P.-L. P., & Yuan, T. (2023). Measuring user competence in using artificial intelligence: Validity and reliability of artificial intelligence literacy scale. Behaviour & Information Technology, 42(9), 1324–1337. https://doi.org/10.1080/0144929X.2022.2072768
  30. Wang, L., & Li, W. (2024). The impact of AI usage on university students’ willingness for autonomous learning. Behavioral Sciences, 14(10), 956. https://doi.org/10.3390/bs14100956
  31. Wang, S., Chen, M., & Zhang, Q. (2023). The development and validation of an AI literacy scale for college students. Educational Technology Research and Development, 71(2), 215–233. https://doi.org/10.1007/s11423-022-10100-4
  32. Zhou, Y., & Schofield, S. (2024). Developing a conceptual framework for artificial intelligence (AI) literacy in higher education. Journal of Learning Development in Higher Education, 26. https://doi.org/10.47408/jldhe.vi26.832