Exploring Trends in STEM Education: Insights from a Pilot Survey of Educators Using Artificial Intelligence
Main Article Content
Abstract
As artificial intelligence (AI) continues to expand in education, schools must determine how to implement these tools effectively and ethically. This pilot study examined perspectives of 39 secondary STEM educators regarding teacher use, student use, and ethical scenarios surrounding AI in order to inform professional development for AI implementation in schools. Survey findings indicated that many educators saw value in AI for instruction, assessment, and student support, but a substantial portion felt unprepared to use it effectively. Participation in professional development was associated with greater confidence, understanding, and classroom use of AI. Educators also drew clear ethical distinctions between using AI as a tool for support, tutoring, and idea generation and using it in ways that undermine student thinking or academic integrity. These findings suggest that successful AI integration requires targeted professional development, clear district guidance, and support for ethical, pedagogically grounded implementation.
Article Details

This work is licensed under a Creative Commons Attribution 4.0 International License.
References
Akhmadieva, R. S., Udina, N. N., Kosheleva, Y. P., Zhdanov, S. P., Timofeeva, M. O., & Budkevich, R. L. (2023). Artificial intelligence in science education: A bibliometric review. Contemporary Educational Technology, 15(4), ep460. https://doi.org/10.30935/cedtech/13587
AlKanaan, H. M. N. (2022). Awareness regarding the implication of artificial intelligence in science education among pre-service science teachers. International Journal of Instruction, 15(3), 895–912. https://doi.org/10.29333/iji.2022.15348a
Antonenko, P., & Abramowitz, B. (2022). In-service teachers’ (mis)conceptions of artificial intelligence in K-12 science education. Journal of Research on Technology in Education, 55(1), 64–78. https://doi.org/10.1080/15391523.2022.2119450
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum Associates. https://doi.org/10.4324/9780203771587
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340. https://doi.org/10.2307/249008
De Winter, J. C., & Dodou, D. (2010). Five-point likert items: T test versus mann-whitney-wilcoxon. Practical Assessment, Research Evaluation, 15(11), 1-12. https://doi.org/10.7275/bj1p-ts64
Diáz, B., & Nussbaum, M. (2024). Artificial intelligence for teaching and learning in schools: The need for pedagogical intelligence. Computers & Education, 217. https://doi.org/10.1016/j.compedu.2024.105071
Dimitriadou, E., & Lanitis, A. (2023). A critical evaluation, challenges, and future perspectives of using artificial intelligence and emerging technologies in smart classrooms. Smart Learning Environments, 10(1), 1–26. https://doi.org/10.1186/s40561-023-00231-3
Donath, J. L., Lüke, T., Graf, E., Tran, U. S., & Götz, T. (2023). Does professional development effectively support the implementation of inclusive education? A meta-analysis. Educational Psychology Review, 35(30). https://doi.org/10.1007/s10648-023-09752-2
Erduran, S. (2023). AI is transforming how science is done. Science education must reflect this change. Science, 382(6677). https://doi.org/10.1126/science.adm9788
Ghamrawi, N., Shal, T., Ghamrawi, N. A. R. (2024). Exploring the impact of AI on teacher leadership: Regressing or expanding? Education and Information Technologies, 29, 8415–8433. https://doi.org/10.1007/s10639-023-12174-w
Jia, F., Sun, D., Looi, C. (2024). Artificial intelligence in science education (2013–2023): Research trends in ten years. Journal of Science Education and Technology, 33, 94–117. https://doi.org/10.1007/s10956-023-10077-6
Miao, F., & Holmes, W. (2023). Guidance for generative AI in education and research. UNESCO. https://unesdoc.unesco.org/ark:/48223/pf0000386693
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
Nazaretsky, T., Ariely, M., Cukurova, M., & Alexandron, G. (2022). Teachers’ trust in AI-powered educational technology and a professional development program to improve it. Br. J. Educ. Technol., 53, 914–931. https://doi.org/10.1111/bjet.13232
OECD. (2025). Results from TALIS 2024: The state of teaching, TALIS. OECD Publishing. https://doi.org/10.1787/90df6235-en
OECD. (2026). Reimagining teaching in an accelerating world, international summit on the teaching profession. OECD Publishing. https://doi.org/10.1787/d0edfe8c-en
Park, J., Teo, T. W., Teo, A., Chang, J., Huang, J. S., & Koo, S. (2023). Integrating artificial intelligence into science lessons: Teachers’ experiences and views. International Journal of STEM Education, 10(61). https://doi.org/10.1186/s40594-023-00454-3
Tan, X., Cheng, G., & Ling, M. H. (2025). Artificial intelligence in teaching and teacher professional development: A systematic review. Computers and Education: Artificial Intelligence, 8, 100355. https://doi.org/10.1016/j.caeai.2024.100355
UNESCO (2024). AI competency framework for teachers. Paris: UNESCO.
Watters, J., Hill, A., Weinrich, M., Supalo, C., & Jiang, F. (2020). An artificial intelligence tool for accessible science education. Journal of Science Education for Students with Disabilities, 24(1), https://doi.org/10.14448/jsesd.13.0010
Zhou, X., Shu, L., Xu, Z., & Padrón, Y. (2023). The effect of professional development on in-service STEM teachers’ self-efficacy: A meta-analysis of experimental studies. International Journal of STEM Education, 10(37). https://doi.org/10.1186/s40594-023-00422-x