Despite widespread claims that Generative Artificial Intelligence (GenAI) will transform education, longitudinal empirical evidence on its pedagogical integration remains limited. This study examines how GenAI use shapes teaching and learning practices over time. Using a mixed methods longitudinal design, the study draws on 34 semi structured interviews conducted at two time points, six to eight months apart, with 17 secondary school teachers who independently adopted GenAI tools. The analysis was triangulated with 212 GenAI-supported teaching and learning activities. A theory-driven classification based on the SAMR framework was combined with inductive thematic analysis and quantitative pre-post comparisons. The findings, based on a thematic analysis of teacher discourse, reveal differentiated trends in opportunities and challenges. Opportunities related to fostering creativity increased over time, whereas efficiency, workload reduction, and teacher empowerment remained stable. Concerns regarding content quality and inherent biases showed a marginal increase, while references to prohibited or improper use declined. Regarding teaching and learning activities, a significant increase was observed in teaching-related uses of GenAI over time. In addition, a significant increase was identified at the Modification level, indicating a shift toward more advanced forms of pedagogical redesign, particularly through the development of personalized materials, AI-supported instructional planning, and adaptive feedback practices, while learning activities at higher levels remained comparatively stable. Taken together, these findings position the SAMR as a dynamic framework for examining longitudinal patterns of GenAI integration and suggest that GenAI currently accelerates instructional innovation more than it fundamentally restructures student learning paradigms.
Levy-Nadav et al. (2026) studied this question.