Copertina

Education Classes for Pre-Service Teachers Using Text-Video-Based AI

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Pubblicato in:Journal of Baltic Science Education vol. 24, no. 3 (2025), p. 538
Autore principale: Oh, Sejun
Pubblicazione:
Scientia Socialis Ltd
Soggetti:
South Korea
Preservice Teachers
Teacher Education Programs
Artificial Intelligence
Interdisciplinary Approach
Barriers
Teaching Methods
Instructional Innovation
Video Technology
Lesson Plans
Teacher Competencies
Climate
Sustainability
Student Attitudes
Instructional Materials
Media Selection
Alignment (Education)
Creativity
Cognitive Ability
Instructional Design
Computer Software
Technology Integration
Inservice Teacher Education
Masters Degrees
Doctoral Degrees
Doctoral Students
Graduate Students
Foreign Countries
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Abstract: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.
ISSN:1648-3898
Fonte:ERIC