13.00.00 - Pedagogical Sciences
ARTIFICIAL INTELLIGENCE COMPETENCY IN TEACHER EDUCATION: A PEDAGOGICAL MODEL FOR PREPARING FUTURE TEACHERS
- Vol. 10 No. 31 (2026): Social Science and Innovation — 2026-yil oktabr soni
- 2026-11-01
- Open access
Keywords:
teacher education; AI literacy; pedagogical competence; pre-service teachers; technological pedagogical content knowledge; professional ethicsAbstract
Teacher preparation programs increasingly face a mismatch between widespread access to artificial intelligence and novice teachers' ability to judge its pedagogical value. This conceptual article synthesizes teacher-knowledge theory, AI literacy research, and UNESCO's teacher competency guidance to propose an integrated model for pre-service teacher education. The Teacher AI Pedagogical Readiness (TAPR) model combines foundational AI literacy, subject-specific design, critical evaluation, ethical and inclusive practice, and reflective professional judgment. A staged teacher education pathway is proposed, progressing from analyzing AI limitations to designing, simulating, and critically reflecting on lessons. Assessment is based on defensible pedagogical decisions rather than prompt fluency alone. The article provides a sample analytic rubric and researchable propositions for program evaluation. It does not claim that the model has been experimentally validated. Its contribution is a curriculum-oriented account of how to prepare teachers to use AI as a bounded professional resource while safeguarding student learning, privacy, and teacher autonomy.
References
Miao, F., & Cukurova, M. (2024). AI competency framework for teachers. UNESCO. https://www.unesco.org/en/articles/ai-competency-framework-teachers
Miao, F., & Holmes, W. (2023). Guidance for generative AI in education and research. UNESCO. https://www.unesco.org/en/articles/guidance-generative-ai-education-and-research
Mishra, P., & Koehler, M. J. (2006). Technological pedagogical content knowledge: A framework for teacher knowledge. Teachers College Record, 108(6), 1017–1054. https://doi.org/10.1111/j.1467-9620.2006.00684.x
Shulman, L. S. (1986). Those who understand: Knowledge growth in teaching. Educational Researcher, 15(2), 4–14. https://doi.org/10.3102/0013189X015002004
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
Kasneci, E., et al. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, 102274. https://doi.org/10.1016/j.lindif.2023.102274
Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign.
Selwyn, N. (2019). Should robots replace teachers? AI and the future of education. Polity Press.
Saranya, E., Sethuraman, P., Olimjonov, H., Nagendram, S., Ganesh, M., & Sapaev, I. (2026, August). Deep Reinforcement Learning for Cloud Resource Allocation and Energy-Efficient Data Center Operations. In 2026 International Conference on Next-Gen Intelligent Computing and Applications (ICONICA) (Vol. 1, pp. 1-11). IEEE.
Kannan, N., Kollinal, R. K., Olimjonov, H., Ahammad, S. H., Kanagaraju, M., & Sapaev, I. (2026, August). Intelligent Ensemble Learning for Climate-Aware Agricultural Planning and Resource Allocation. In 2026 International Conference on Next-Gen Intelligent Computing and Applications (ICONICA) (Vol. 1, pp. 1-10). IEEE.
Guru, K. V., Rajan, C., Olimjonov, H., Lakshmi, N. V., Joseph, A. V., & Nazarova, S. (2026, August). Capsule Network-Based Historical Document Digitization for Intelligent Knowledge Organization and Digital Archiving. In 2026 International Conference on Next-Gen Intelligent Computing and Applications (ICONICA) (Vol. 1, pp. 1-9). IEEE.
Downloads
Share this article
