EDUCATION CLASSES FOR PRE-SERVICE TEACHERS USING TEXT-VIDEO-BASED AI

Climate change and sustainability pose abstract, interdisciplinary challenges that demand innovative pedagogy. This study aimed to examine how generative text-to-video (TTV) tools influence pre-service teachers’ lesson-planning competencies and what educational values and affordances pre-service teachers attribute to the technology. A convergent mixed-methods design was employed with 34 pre-service teachers. Quantitative pre- and post-surveys measuring 18 competencies revealed statistically significant gains in 17 items; five exhibited large effects (e.g., d = 1.01 for selecting instructional materials). Qualitative thematic analysis of group-designed lessons identified four recurrent themes: enhanced creativity, stronger curriculum alignment, heightened student engagement, and differentiated use of TTV for diverse learners. Participants valued TTV’s capacity to visualize complex processes, reduce cognitive load, and integrate multiple subjects around climate issues. The converged findings demonstrate that TTV makes abstract content more tangible and motivates learners while expanding teachers’ design repertoires. These results offer an empirical foundation for incorporating TTV into teacher-education programmes and suggest directions for future research on scalable, ethically grounded AI applications in education. Keywords: Text-to-Video AI, educational design, pre-service teacher education, educational technology