Maayan Shay Sayag, Ina Blau, Orit Avidov-Ungar E51 engagement with AI tools rather than focusing primarily on teacher-centered resource creation. Keywords: Teacher Professional Development, Artificial Intelligence in Education, SAMR Framework, Educational Technology. Literature review The integration of Generative Artificial Intelligence (GenAI) has fundamentally altered teachers' professional responsibilities, necessitating frameworks for understanding how technology integration evolves in authentic classroom contexts (Kasneci et al., 2023). The SAMR model (Puentedura, 2012) provides such a framework for conceptualizing technology integration depth, distinguishing between enhancement (Substitution and Augmentation) and transformation (Modification and Redefinition), with each level representing increasingly advanced pedagogical integration. Recent empirical research applying SAMR to GenAI contexts reveals that higher-order integration remains relatively rare, with most documented activities concentrated at Augmentation levels (Shamir Inbal et al., 2024; Jiménez-García et al., 2024). This pattern raises critical questions about what factors enable progression toward transformative integration and why identical training produces such varied outcomes among teachers. Research identifies internal factors (perceived usefulness, self-efficacy, pedagogical beliefs) and external factors (organizational support, professional learning communities) as critical enablers (Viberg et al., 2024; Yang et al., 2024). However, teachers face significant challenges translating AI capabilities into pedagogically sound implementations, particularly when designing innovative AIintegrated lessons (Ding et al., 2024; Kong & Yang, 2024). Teacher Professional Development (TPD) represents the primary institutional response. Recent scholarship emphasizes personalization, including differentiated learning paths, ongoing support, and institutional backing (Avidov-Ungar, 2024; Skantz-Åberg et al., 2022). Yet research documents substantial variability in outcomes, with identical programs producing divergent results (Luo et al., 2024) and facing challenges related to technical limitations, insufficient guidance, and inadequate structure (Sayag et al., 2025). A critical gap exists in understanding GenAI integration following formal TPD. While most activities remain at enhancement levels (Shamir Inbal et al., 2024; Jiménez-García et al., 2024), longitudinal documentation across pedagogical domains is lacking. Moreover, despite documented challenges in moving from training to practice (Ding et al., 2024; Kong & Yang, 2024), implementation approaches enabling transformative integration remain unexplored. This study addresses this gap through longitudinal mixed-methods examination of GenAI integration patterns among high-school teachers following formal TPD. The study aims to identify not only how teachers integrate GenAI across pedagogical domains, but specifically, what factors enable transformative rather than enhancement-level integration. The study explores the following research questions: RQ1: How do high-school teachers integrate GenAI tools into their professional practice following formal TPD across different pedagogical domains and SAMR levels? RQ2: What factors are associated with different levels of GenAI integration following formal TPD? Methodology This mixed-methods study utilized semi-structured interviews to examine how teachers integrated GenAI tools following participation in an AI-focused TPD program (Creswell & Poth, 2018). This approach captured the progression from initial intentions to classroom implementation.
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