E56 From Design to Enactment: GenAI Integration through SAMR assumptions that time and exposure automatically drive progression toward higher-order integration. Practically, these findings indicate that TPD programs should emphasize orchestrating meaningful student engagement with AI tools rather than focusing primarily on teacher-centered resource creation. The path to transformative integration runs through authentic classroom integration with students. Limitations and Future Research This study's findings are limited by its focus on high-school teachers in Israel who participated in an entry-level AI-focused TPD program. Reliance on self-report data may introduce biases like social desirability or recall inaccuracies. Future research should broaden the scope to include teachers from diverse educational levels and cultural contexts, as well as integrate instruments for collecting behavioral data such as classroom observations Investigating individual factors that enable or prevent successful transition from teacher-centered design to student-centered enactment could provide deeper insights into pathways toward transformative integration. Additionally, examining how different TPD program designs specifically support this transition could inform more effective professional development models. References Avidov-Ungar, O. (2024). The Personalized Continuing Professional Learning of Teachers: A Global Perspective (1st ed.). Routledge. https://doi.org/10.4324/9781003424390 Creswell, J. W., & Poth, C. N. (2018). Qualitative inquiry and research design: Choosing among five approaches (4th ed.). SAGE Publications. Ding, A.-C. E., Shi, L., Yang, H., & Choi, I. (2024). Enhancing teacher AI literacy and integration through different types of cases in teacher professional development. Computers and Education Open, 6, 100178. https://doi.org/10.1016/j.caeo.2024.100178 Fereday, J., & Muir-Cochrane, E. (2006). Demonstrating rigor using thematic analysis: A hybrid approach of inductive and deductive coding and theme development. International Journal of Qualitative Methods, 5(1), 80–92. https://doi.org/10.1177/160940690600500107 Jiménez-García, E., Orenes-Martínez, N., & López-Fraile, L. A. (2024). Rueda de la Pedagogía para la Inteligencia Artificial: adaptación de la Rueda de Carrington. RIED-Revista Iberoamericana de Educación a Distancia, 27(1), 87–113. https://doi.org/10.5944/ried.27.1.37622 Kasneci, E., Seßler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., Krusche, S., Kutyniok, G., Michaeli, T., Nerdel, C., Pfeffer, J., Poquet, O., Sailer, M., Schmidt, A., Seidel, T., ... Kasneci, G. (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 Kong, S.-C., & Yang, Y. (2024). A human-centred learning and teaching framework using generative artificial intelligence for self-regulated learning development through domain knowledge learning in K–12 settings. IEEE Transactions on Learning Technologies, 1–13. https://doi.org/10.1109/TLT.2024.3392830 Luo, Z., Abbasi, B. N., Yang, C., Li, J., & Sohail, A. (2024). A systematic review of evaluation and program planning strategies for technology integration in education: Insights for evidencebased practice. Education and Information Technologies. https://doi.org/10.1007/s10639-024-12707-x Puentedura, R. (2012). The SAMR model: Six exemplars. http://www.hippasus.com/rrpweblog/archives/2012/08/14/SAMR_SixExemplars.pdf Sayag, M. S., Blau, I., & Avidov-Ungar, O. (2025). "We need more than tools": Examining AI-focused professional development challenges through the DigCompEdu AI supplement framework. In
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