E52 From Design to Enactment: GenAI Integration through SAMR Participants and Context This study examined an entry-level AI-focused TPD program conducted by the Israeli Ministry of Education's Pedagogical Secretariat, implemented through multiple parallel courses across subject areas. The curriculum, designed by educational technology experts, was adapted by subject-area instructors to meet discipline-specific needs. Course sizes varied, ranging from approximately 30 to 60 participants per subject area, with hundreds of teachers participating across all courses. Each course comprised 30 hours of instruction delivered through both synchronous and asynchronous sessions. The curriculum covered fundamental GenAI concepts, text-to-text and text-to-image tools, GenAI applications in educational design software, and subject-specific implementations, emphasizing responsible AI use in education. The participants completed the program during springsummer 2024. From this broader sample, 20 high-school teachers were selected using purposive sampling to ensure representation across disciplines (sciences, humanities, social sciences). The participants represented varied career stages: early career (0-5 years, n=4), middle career (6-12 years, n=7), and late career (13+ years, n=9). Many held leadership roles such as subject-matter or pedagogical coordinators. The sample represented various geographic regions and socioeconomic contexts, with teachers in schools classified as high (n=8), medium-high (n=8), and medium-low (n=4) socioeconomic status. While a few participants had limited initial experience with AI tools, for the vast majority this TPD program served as their entry point into AI integration in education. Research Tools and Procedure This longitudinal study employed semi-structured interviews via Zoom videoconferencing at two time points. The first round occurred within three weeks of program completion (spring-summer 2024), and the second 7-8 months later (winter 2025), examining sustained implementation patterns. Each interview lasted 40-60 minutes and explored participants' TPD experiences, GenAI tool use, and implementation of AI-enhanced activities. All interviews were recorded and transcribed for analysis. Twenty high-school teachers participated in both interview rounds. Data analysis followed a mixed-methods approach combining qualitative and quantitative methods. Qualitative data from semi-structured interviews were analyzed using inductive thematic analysis and deductive content analysis (Fereday & Muir-Cochrane, 2006). Activities were classified by the SAMR framework (Puentedura, 2012), distinguishing between enhancement levels (Substitution and Augmentation) and transformation levels (Modification and Redefinition), and categorized by implementation level: Future Planning (conceptual ideas not yet developed), Design of Learning Materials (resources for teacher or student use), and Enactment with Students (direct application in learning environments). Coded activities were then quantified, and chi-square tests for independence assessed changes over time between rounds in SAMR distribution and associations between implementation levels and SAMR levels. Qualitative analysis provided representative examples illustrating how teachers implemented GenAI tools across different SAMR levels and implementation approaches. To ensure inter-rater reliability, 25% of the data was independently analyzed by a second rater, with Cohen's Kappa coefficient of 0.81 indicating substantial agreement between raters for SAMR classification. Findings and Discussion This study examined GenAI integration patterns among 20 high-school teachers following formal TPD, analyzing 181 documented activities across two interview rounds conducted 7-8 months apart (Round 1: N=91, Round 2: N=90). The first section examines how teachers integrated GenAI tools across different pedagogical domains and SAMR levels (RQ1), mapping the breadth of integration across teaching, learning, and assessment contexts. The second section investigates what factors are
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