Perceptions and preparedness of K-12 educators in adopting generative AI

  • Juhee Kim Department of Leadership & Counseling, University of Idaho, Moscow, ID, USA
Keywords: generative artificial intelligence, K-12 education, educator preparedness, AI integration, professional development

Abstract

The integration of generative artificial intelligence (AI) tools, such as ChatGPT and DALL-E, presents transformative opportunities and challenges for K-12 education. This mixed-methods study investigates educators’ perceptions, familiarity, and preparedness for AI adoption, as well as institutional strategies and barriers. Quantitative findings indicate strong relationships between AI familiarity, perceived readiness, and institutional planning stages. Qualitative analysis highlights challenges such as insufficient professional development, ethical concerns, and infrastructural inequities, alongside opportunities for enhancing personalised learning and operational efficiency. The findings underscore the need for targeted training, equitable resource access, and clear institutional policies to ensure effective and ethical AI integration. This research offers actionable insights for educators, policymakers, and leaders seeking to navigate AI’s potential in K-12 education.

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References


Akanzire, B. N., Nyaaba, M., & Nabang, M. (2025). Generative AI in teacher education: Teacher educators’ perception and preparedness. Journal of Digital Educational Technology, 5(1), ep2508. https://doi.org/10.30935/jdet/15887




Bautista, A., Estrada, C., Jaravata, A.M., Mangaser, L.M., Narag, F., Soquila, R., & Asuncion, R.J. (2024). Preservice teachers’ readiness towards integrating AI-Based tools in education: A TPACK approach. Educational Process:International Journal, 13(3), 40–68. https://doi.org/10.22521/edupij.2024.133.3




Braaten, E. & Farnsworth, K. (2024). Educators’ Perspectives on Generative AI in K-12: Informing AI in Education Guidance. Friday Institute for Educational Innovation, North Carolina State University. Retrieved from https://fi.ncsu.edu/resource-library/perspectives-ai-in-k12/




Braun, V. & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa




Celik, I. et al. (2022). The promises and challenges of artificial intelligence for teachers: a systematic review of research. TechTrends, 66(4), 616–630. https://doi.org/10.1007/s11528-022-00715-y




Chakraborty, S. (2024). Generative AI in modern education society. arXiv preprint arXiv:2412.08666. https://doi.org/10.48550/arXiv.2412.08666




Cheah, Y. & Kim, J. (2025). STEM teachers’ perceptions, familiarity, and support needs for integrating generative artificial intelligence in K-12 education. School Science and Mathematics, 1–16. https://doi.org/10.1111/ssm.18334




Cheah, Y. H., Lu, J. & Kim, J. (2025). Integrating generative artificial intelligence in K-12 education: examining teachers’ preparedness, practices, and barriers. Computers and Education: Artificial Intelligence, 8, Article 100363. https://doi.org/10.1016/j.caeai.2025.100363




Creswell, J. W. & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). Sage Publications.




Dai, Y. et al. (2022). Collaborative construction of artificial intelligence curriculum in primary schools. Journal of Engineering Education, 112(1), 23–42. https://doi.org/10.1002/jee.20503




Dillman, D. A., Smyth, J. D. & Christian, L. M. (2014). Internet, Phone, Mail, and Mixed-Mode Surveys: The Tailored Design Method (4th ed.). Wiley.




Ding, A. C. E. et al. (2024). Enhancing teacher AI literacy and integration through different types of cases in teacher professional development. Computers and Education Open, 6, Article 100178. https://doi.org/10.1016/j.caeo.2024.100178




Ertmer, P. A., Ottenbreit-Leftwich, A. T. & Sadik, O. (2012). Teacher beliefs and technology integration practices: a critical relationship. Computers & Education, 59(2), 423–435. https://doi.org/10.1016/j.compedu.2012.02.001




Gârdan, I. P., Manu, M. B., Gârdan, D. A., Negoiță, L. D. L., Paștiu, C. A., Ghiță, E., & Zaharia, A. (2025). Adopting AI in education: optimizing human resource management considering teacher perceptions. Frontiers in Education, 10, 1488147, Frontiers Media SA. https://doi.org/10.3389/feduc.2025.1488147




Gestson, C. & Core Team. (2024). Generative Artificial Intelligence in K-12 Education: Guidance for Arizona Schools and School Systems. Northern Arizona University.




Goodman, L. A. (1961). Snowball sampling. Annals of Mathematical Statistics, 32(1), 148–170. https://doi.org/10.1214/aoms/1177705148




Heckathorn, D. D. (2011). Snowball versus respondent-driven sampling. Sociological Methodology, 41(1), 355–366. https://doi.org/10.1111/j.1467-9531.2011.01244.x




Holmes, W., Bialik, M. & Fadel, C. (2019). Artificial Intelligence in Education: Promises and Implications for Teaching and Learning. Center for Curriculum Redesign.




Hsu, T. C., Chang, S. C. & Hung, Y. T. (2019). How to learn and how to teach computational thinking: suggestions based on a review of the literature. Computers & Education, 126, 296–310. https://doi.org/10.1016/j.compedu.2018.07.004




Kim, J. & Wargo, E. (2025). Empowering educational leaders for AI integration in rural STEM education: challenges and strategies. Frontiers in Education, 10, 1–13. https://doi.org/10.3389/feduc.2025.1567698




Kimberlin, C. L. & Winterstein, A. G. (2008). Validity and reliability of measurement instruments used in research. American Journal of Health-System Pharmacy, 65(23), 2276–2284. https://doi.org/10.2146/ajhp070364




Klopfer, E., Reich, J., Abelson, H., & Breazeal, C. (2024). Generative AI and K-12 Education: An MIT Perspective. An MIT Exploration of Generative AI. https://doi.org/10.21428/e4baedd9.81164b06




Liu, S.-H. (2011). Factors related to pedagogical beliefs of teachers and technology integration. Computers & Education, 56(4), 1012–1022. https://doi.org/10.1016/j.compedu.2010.12.001




Manrique, P. C. J. & Palomares, N. R. (2024). Embracing the future: exploring teachers’ perspective and readiness for integrating artificial intelligence (AI) in mathematics classrooms in selected public and private senior high schools. Ignatian International Journal for Multidisciplinary Research, 2(5), 2654–2675.




Miao, F. & Holmes, W. (2023). Guidance for Generative AI in Education and Research. UNESCO. Retrieved from https://unesdoc.unesco.org/ark:/48223/pf0000386693




Mishra, P., Warr, M. & Islam, R. (2023). TPACK in the age of ChatGPT and generative AI. Journal of Digital Learning in Teacher Education, 39(4), 235–251. https://doi.org/10.1080/21532974.2023.2247480




Noroozi, O. et al. (2024). Generative AI in education: pedagogical, theoretical, and methodological perspectives. International Journal of Technology in Education (IJTE), 7(3), 373–385. https://doi.org/10.46328/ijte.845




Obeysekare, E. (2024). Responsible use of Generative AI in K-12 STEAM Education (RAISE). Creative Inquiry. Retrieved from https://creativeinquiry.lehigh.edu/impact-fellowships/global-social-impact-fellowship/responsible-use-generative-ai-k-12-steam




Oh, S. & Sanfilippo, M. (2024). University governance for responsible AI. In Proceedings of the ALISE Annual Conference. https://doi.org/10.21900/j.alise.2024.1706




Pangrazio, L. & Selwyn, N. (2021). Towards a school-based ‘critical data education’. Pedagogy, Culture & Society, 29(3), 431–448. https://doi.org/10.1080/14681366.2020.1747527




Perrotta, C. & Selwyn, N. (2019). Deep learning goes to school: toward a relational understanding of AI in education. Learning, Media and Technology, 45(3), 251–269. https://doi.org/10.1080/17439884.2020.1686017




Sanusi, I. T., Ayanwale, M. A. & Chiu, T. K. F. (2024). Investigating the moderating effects of social good and confidence on teachers’ intention to prepare school students for artificial intelligence education. Education and Information Technologies, 29, 273–295. https://doi.org/10.1007/s10639-023-12250-1




Shin, W. S. & Shin, D. H. (2021). A case study on the application of plant classification learning for 4th grade elementary school using machine learning in online learning. Journal of Korean Elementary Science Education, 40(1), 66–80.




Wargo, E. & Hoke, I. (2022). Revisiting rural education access. Educational Considerations, 48(2), Article 5. https://doi.org/10.4148/0146-9282.2333




World Economic Forum. (2024). Shaping the Future of Learning: The Role of AI in Education 4.0. Insight report. Retrieved from https://www3.weforum.org/docs/WEF_Shaping_the_Future_of_Learning_2024.pdf




Zafar, S. et al. (2025). The effect of ChatGPT on the critical thinking skills of secondary students: a survey-based study. Journal of Social Signs Review, 3(03), 243–259.
Published
2025-06-24
How to Cite
Kim , J. (2025). Perceptions and preparedness of K-12 educators in adopting generative AI. Research in Learning Technology, 33. https://doi.org/10.25304/rlt.v33.3448
Section
Original Research Articles