AI LITERACY BEYOND STEM: RETHINKING TEACHER EDUCATION FOR THE AI ERA
DOI:
https://doi.org/10.15282/ijhtc.v10i2.13274Keywords:
AI literacy, STEM education, teacher education, AI ethics, pre-service teachers, Education 5.0Abstract
Artificial Intelligence (AI) is rapidly transforming education, demanding that educators possess a multidimensional literacy extending beyond technical skills to include affective, behavioural, cognitive, and ethical competencies. This study systematically investigates these four dimensions of AI literacy among 265 pre-service teachers (124 STEM, 141 non-STEM) at Institut Pendidikan Guru Kampus Pendidikan Teknik (IPGKPT) using the validated AI Literacy Questionnaire (AILQ) grounded in the ABCE framework. Results reveal that Ethical Learning (EL) and Affective Learning (AL) scored highest, indicating strong ethical awareness and motivation, while Cognitive Learning (CL) lagged, highlighting a persistent gap in foundational understanding. Notably, independent samples t-tests showed no significant differences between STEM and non-STEM groups in AL and BL, but a moderate advantage for STEM participants in CL and EL. These findings challenge the notion that AI literacy is exclusive to technical fields, underscoring the need for equitable, cross-disciplinary integration of AI literacy within teacher education. Building on previous research, the study identifies a disconnect between awareness and application, particularly in cognitive and behavioural domains. It recommends embedding hands-on, ethics-anchored, and discipline-inclusive AI training into teacher education curricula, aligned with the values of Education 5.0. The study further advocates for institutionalising a comprehensive AI literacy framework as a national competency model, ensuring that future educators are not only AI-literate but also equipped to lead ethical, inclusive, and transformative AI integration in Malaysian classrooms and beyond. This research offers actionable recommendations for curriculum reform and policy, aiming to empower educators with agency, adaptability, and ethical judgment in post-pandemic learning environments.
Downloads
References
Ajzen, I. (2011). The theory of planned behaviour: Reactions and reflections. Psychology & Health, 26(9), 1113-1127.
Anderson, L., & Krathwohl, D. E. (2001). A Taxonomy for learning, teaching, and assessing: A revision of Bloom's taxonomy of educational objectives [Abridged]. New York: Addison Wesley Longman, Inc.
Asio, J. M. R. (2024). Artificial intelligence (AI) literacy and academic performance of tertiary level students: A preliminary analysis. Social Sciences, Humanities and Education Journal (SHE Journal), 5(2), 309-321.
Badawy, H. R., Alkaabi, A. M., Qablan, A. M., Badawy, H. R., Almaazmi, K. M. M. H., & Ndlela, N. P. (2025). Nurturing future innovators integrating computational thinking, AI, and STEM in early education. In Empowering Early Education with Computational Thinking, AI, and STEM (pp. 97-134). IGI Global.
Bandura, A. (1986). Social foundations of thought and action. Englewood Cliffs, NJ, 1986(23-28), 2.
Bohari, A., Ghazali, Z., Loh, S. L., & Rasit, H. H. (2024). Empowering educators with AI: A study on literacy among IPGMKPT lecturers. In Persidangan Kebangsaan Pedagogi dan Pendidikan Guru (PedaTEd) zon Sarawak 2024. Kuching (23-24 Oktober 2024).
De Silva, D., Jayatilleke, S., El-Ayoubi, M., Issadeen, Z., Moraliyage, H., & Mills, N. (2024). The human-centred design of a universal module for artificial intelligence literacy in tertiary education institutions. Machine Learning and Knowledge Extraction, 6(2), 1114–1125.
Deci, E. L., & Ryan, R. M. (1985). The general causality orientations scale: Self-determination in personality. Journal of Research in Personality, 19(2), 109–134.
Guan, L., Zhang, Y., & Gu, M. M. (2025). Pre-service teachers preparedness for AI-integrated education: An investigation from perceptions, capabilities, and teachers’ identity changes. Computers and Education: Artificial Intelligence, 8, 100341.
Kolb, D. (1984). Experiential learning: Experience as the source of learning and development. Englewood Cliffs, NJ: Prentice-Hall.
Luckin, R. (2025). Nurturing human intelligence in the age of AI: rethinking education for the future. Development and Learning in Organizations: An International Journal, 39(1), 1–4.
Mikeladze, T., Meijer, P. C., & Verhoeff, R. P. (2024). A comprehensive exploration of artificial intelligence competence frameworks for educators: A critical review. European Journal of Education, 59(3), e12663.
Markauskaite, L., Marrone, R., Poquet, O., Knight, S., Martinez-Maldonado, R., Howard, S., Tondeur, J., De Laat, M., Shum, S. B., Gašević, D., & Siemens, G. (2022). Rethinking the entwinement between artificial intelligence and human learning: What capabilities do learners need for a world with AI? Computers and Education: Artificial Intelligence, 3, 100056.
Mathes, E. W. (2021). An evolutionary perspective on Kohlberg’s theory of moral development. Current Psychology, 40(8), 3908–3921.
Mikeladze, T., Meijer, P. C., & Verhoeff, R. P. (2024). A comprehensive exploration of artificial intelligence competence frameworks for educators: A critical review. European Journal of Education, 59(3), e12663.
Ng, D. T. K., Leung, J. K. L., Su, J., Ng, R. C. W., & Chu, S. K. W. (2023b). Teachers’ AI digital competencies and twenty-first century skills in the post-pandemic world. Educational Technology Research and Development, 71(1), 137-161.
Ng, D. T. K., Wu, W., Leung, J. K. L., Chiu, T. K. F., & Chu, S. K. W. (2024). Design and validation of the AI literacy questionnaire: The affective, behavioural, cognitive and ethical approach. British Journal of Educational Technology, 55(3), 1082-1104.
Okada, A., Sherborne, T., Panselinas, G., & Kolionis, G. (2025). Fostering transversal skills through open schooling supported by the CARE-KNOW-DO pedagogical model and the UNESCO AI competencies framework. International Journal of Artificial Intelligence in Education, 1-46.
Plate, D., & Hutson, J. (2024). Bridging Disciplines with AI-Powered Coding: Empowering Non-STEM Students to Build Advanced APIs in the Humanities. ISAR Journal of Arts, Humanities and Social Sciences, 2(9), 44-55.
Ribble, M. (2012). Digital citizenship for educational change. Kappa Delta Pi Record, 48(4), 148-151.
Rogers, E.M. (2003). Diffusion of innovations (5th ed.). New York: Free Press.
Scherer, R., Howard, S. K., Tondeur, J., & Siddiq, F. (2021). Profiling teachers' readiness for online teaching and learning in higher education: Who's ready? Computers in Human Behavior, 118, 106675.
Southworth, J., Migliaccio, K., Glover, J., Glover, J. N., Reed, D., McCarty, C., ... & Thomas, A. (2023). Developing a model for AI Across the curriculum: Transforming the higher education landscape via innovation in AI literacy. Computers and Education: Artificial Intelligence, 4, 100127.
Swargiary, K. (2025). Reimagining Education: A Personal Journey Toward a Future-Ready Curriculum. India: Scholar Press.
Tahar, A., Riyadh, H. A., Sofyani, H., & Purnomo, W. E. (2020). Perceived ease of use, perceived usefulness, perceived security and intention to use e-filing: The role of technology readiness. The Journal of Asian Finance, Economics and Business, 7(9), 537-547.
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.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 The Author(s)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
