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GOOGLE VEO 3 AS A PEDAGOGICAL PARTNER: AI-DRIVEN STORYTELLING FOR CONCEPTUAL, AFFECTIVE, AND CRITICAL LEARNING IN SCIENCE EDUCATION

Year 2025, Volume: 9 Issue: 2, 1443 - 1464
https://doi.org/10.30561/sinopusd.1749874

Abstract

The rapid emergence of generative artificial intelligence (AI) has redefined the landscape of educational media design, offering unprecedented opportunities for multimodal and narrative-based learning. This study examines the pedagogical potential of Google Veo 3, a next-generation text-to-video platform, within the context of secondary science education in Türkiye. Using a qualitative case study design, the research explores how an AI-generated narrative video—created to support a high school biology unit on climate change—affects students’ conceptual understanding, emotional engagement, and critical media literacy. Data were collected through classroom observations, semi-structured inter-views with 18 students, and reflective teacher notes, and were analyzed thematically. Find-ings reveal that Veo 3 enhanced students’ comprehension of complex scientific phenome-na by visualizing abstract processes such as ecosystem disruption and biodiversity loss, consistent with the Cognitive Theory of Multimedia Learning. Moreover, the narrative-driven video promoted affective engagement and ethical reflection aligned with socioscien-tific issues (SSI) pedagogy. Students demonstrated empathy toward environmental chal-lenges and developed a heightened awareness of media authenticity. The study concludes that Veo 3 can serve not merely as a content-generation tool, but as a pedagogical part-ner—fostering cognitive, affective, and critical learning dimensions. Recommendations are offered for teacher training, digital ethics education, and institutional policy to ensure the responsible integration of generative AI in classroom practice.

References

  • Braun, V., & Clarke, V. (2021). Thematic analysis: A practical guide. Sage.
  • CDW-G. (2024). Generative AI in K–12: Adoption, Readiness, and Concerns. CDW-G Research Brief.
  • Chinn, C. A., & Duncan, R. G. (2018). Learning to reason about scientific evidence: How epistemic cognition helps science education. Educational Psychologist, 53(2), 197–218.
  • Clark, J. M., & Paivio, A. (1991). Dual coding theory and education. Educational Psy-chology Review, 3(3), 149–210. https://doi.org/10.1007/BF01320076
  • Denzin, N. K. (2017). The research act: A theoretical introduction to sociological methods. Routledge.
  • Freire, P. (1970). Pedagogy of the Oppressed. New York: Herder and Herder.
  • Google. (2025). SynthID: Secure watermarking for AI-generated media. Retrieved from https://deepmind.google/discover/blog/synthid
  • Google. (2025a, May). Veo 3 and Flow: Revolutionizing storytelling with AI. https://cloud.google.com/blog/products/ai-machine-learning/introducing-veo-and-flow
  • Google. (2025b). SynthID: Protecting the integrity of AI-generated content. https://deepmind.google/discover/blog/synthid
  • Hassabis, D. (2025). Multimodal frontiers in generative AI. Nature Machine Intelli-gence, 7(3), 215–218. https://doi.org/10.1038/s42256-025-00777-0
  • Hmelo-Silver, C. E. (2004). Problem-based learning: What and how do students learn? Educational Psychology Review, 16(3), 235–266.
  • Jarman, R., & McClune, B. (2007). Developing scientific literacy: Using news media in the classroom. McGraw-Hill Education.
  • Karaarslan, E., & Aydın, Ö. (2024). Generate impressive videos with text instructions: A review of OpenAI Sora, Stable Diffusion, Lumiere and comparable models. SSRN.https://doi.org/10.2139/ssrn.4731634
  • Leiker, M. J., Sapp, J. D., & Mays, S. (2023). AI-generated educational videos: Perceived credibility and learning outcomes. Journal of Educational Technology Systems, 52(1), 5–22.
  • Lincoln, Y. S., & Guba, E. G. (1985). Naturalistic inquiry. Sage.
  • Livingstone, S., & Sefton-Green, J. (2016). The class: Living and learning in the digi-tal age. NYU.
  • Mayer, R. E. (2009). Multimedia learning (2nd ed.). Cambridge University.
  • OECD. (2020). PISA 2018 results (Volume VI): Are students ready to thrive in an interconnected world?. OECD.
  • Miles, M. B., Huberman, A. M., & Saldaña, J. (2019). Qualitative data analysis: A methods sourcebook (4th ed.). Sage.
  • Paivio, A. (1991). Images in mind: The evolution of a theory. New York: Harvester Wheatsheaf. Patton, M. Q. (2015). Qualitative research & evaluation methods (4th ed.). Sage.
  • Pellas, N. (2023). Integrating generative AI in STEM education: Opportunities and ethical challenges. Education and Information Technologies, 28(4), 4417–4434.
  • Sadler, T. D. (2011). Socio-scientific issues in the classroom: Teaching, learning, and research. Springer.
  • Selwyn, N. (2016). Education and technology: Key issues and debates (2nd ed.). Bloomsbury Academic.
  • Sreekantha, B., Khan, A., Mohamed, S. B., Asif, M. A., & Morya, A. M. (2024). Ad-vancements in text-to-video creation through AI models: A comprehensive review. Journal of Knowledge, Data Science & Information Management, 1, 22–29.
  • UNESCO. (2021). Reimagining our futures together: A new social contract for educa-tion. UNESCO.
  • Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. Cambridge, MA: Harvard University.
  • Williamson, B., & Piattoeva, N. (2021). Education governance and datafication. Euro-pean Educational Research Journal, 20(4), 435–456. https://doi.org/10.1177/1474904121993792
  • Yin, R. K. (2018). Case study research and applications: Design and methods. Sage.
  • Yükseköğretim Kurulu. (2023). Research ethics framework for educational studies in Türkiye. Ankara: YÖK.
  • Zeidler, D. L. (2014). Socioscientific issues as a curriculum emphasis. Science Education, 98(4), 745–770.
  • Zeidler, D. L., Sadler, T. D., Simmons, M. L., & Howes, E. V. (2005). Beyond STS: A research-based framework for socioscientific issues education. Science Education, 89(3), 357–377.

Google VEO 3 As A Pedagogical Partner AI-Driven Storytelling For Conceptual, Affective, And Critical Learning in Science Education

Year 2025, Volume: 9 Issue: 2, 1443 - 1464
https://doi.org/10.30561/sinopusd.1749874

Abstract

Üretken yapay zekâ teknolojilerinin hızlı gelişimi, eğitimde çoklu ortam temelli anlatı tasa-rımlarına yeni bir boyut kazandırmıştır. Bu çalışma, Google Veo 3 adlı metinden videoya dönüştürme platformunun fen bilimleri öğretiminde pedagojik bir araç olarak kullanım potansiyelini incelemektedir. Nitel bir durum çalışması deseninde yürütülen araştırma, Türkiye’deki bir lise biyoloji sınıfında iklim değişikliği konusunun öğretiminde kullanılan yapay zekâ temelli anlatı videosunun öğrencilerin kavramsal anlamaları, duyuşsal katı-lımları ve eleştirel dijital okuryazarlıkları üzerindeki etkisini araştırmıştır. Veriler, sınıf gözlemleri, 18 öğrenciyle yapılan yarı yapılandırılmış görüşmeler ve öğretmen yansıtma notları aracılığıyla toplanmış; tematik analiz yöntemiyle çözümlenmiştir. Bulgular, Veo 3’ün soyut biyolojik süreçlerin (örneğin ekosistem bozulması, biyoçeşitlilik kaybı) görselleş-tirilmesi yoluyla öğrencilerin kavramsal anlayışlarını geliştirdiğini ve Çoklu Ortamla Öğ-renme Kuramı ile uyumlu olduğunu göstermiştir. Ayrıca, anlatı temelli yapı duyuşsal katı-lımı artırmış, öğrencilerde çevresel farkındalık ve etik sorgulama becerileri geliştirmiştir. Öğrencilerin medya doğruluğu ve gerçeklik algısına ilişkin farkındalıklarının artması, ça-lışmanın eleştirel dijital okuryazarlık açısından da katkı sunduğunu göstermektedir. Sonuç olarak, Veo 3 yalnızca bir içerik üretim aracı değil; bilişsel, duyuşsal ve eleştirel öğrenme boyutlarını bütünleştiren yenilikçi bir pedagojik ortak olarak değerlendirilmiştir. Çalışma, öğretmen eğitimi, dijital etik ve eğitim politikası alanlarında sorumlu yapay zekâ entegras-yonu için somut öneriler sunmaktadır.

References

  • Braun, V., & Clarke, V. (2021). Thematic analysis: A practical guide. Sage.
  • CDW-G. (2024). Generative AI in K–12: Adoption, Readiness, and Concerns. CDW-G Research Brief.
  • Chinn, C. A., & Duncan, R. G. (2018). Learning to reason about scientific evidence: How epistemic cognition helps science education. Educational Psychologist, 53(2), 197–218.
  • Clark, J. M., & Paivio, A. (1991). Dual coding theory and education. Educational Psy-chology Review, 3(3), 149–210. https://doi.org/10.1007/BF01320076
  • Denzin, N. K. (2017). The research act: A theoretical introduction to sociological methods. Routledge.
  • Freire, P. (1970). Pedagogy of the Oppressed. New York: Herder and Herder.
  • Google. (2025). SynthID: Secure watermarking for AI-generated media. Retrieved from https://deepmind.google/discover/blog/synthid
  • Google. (2025a, May). Veo 3 and Flow: Revolutionizing storytelling with AI. https://cloud.google.com/blog/products/ai-machine-learning/introducing-veo-and-flow
  • Google. (2025b). SynthID: Protecting the integrity of AI-generated content. https://deepmind.google/discover/blog/synthid
  • Hassabis, D. (2025). Multimodal frontiers in generative AI. Nature Machine Intelli-gence, 7(3), 215–218. https://doi.org/10.1038/s42256-025-00777-0
  • Hmelo-Silver, C. E. (2004). Problem-based learning: What and how do students learn? Educational Psychology Review, 16(3), 235–266.
  • Jarman, R., & McClune, B. (2007). Developing scientific literacy: Using news media in the classroom. McGraw-Hill Education.
  • Karaarslan, E., & Aydın, Ö. (2024). Generate impressive videos with text instructions: A review of OpenAI Sora, Stable Diffusion, Lumiere and comparable models. SSRN.https://doi.org/10.2139/ssrn.4731634
  • Leiker, M. J., Sapp, J. D., & Mays, S. (2023). AI-generated educational videos: Perceived credibility and learning outcomes. Journal of Educational Technology Systems, 52(1), 5–22.
  • Lincoln, Y. S., & Guba, E. G. (1985). Naturalistic inquiry. Sage.
  • Livingstone, S., & Sefton-Green, J. (2016). The class: Living and learning in the digi-tal age. NYU.
  • Mayer, R. E. (2009). Multimedia learning (2nd ed.). Cambridge University.
  • OECD. (2020). PISA 2018 results (Volume VI): Are students ready to thrive in an interconnected world?. OECD.
  • Miles, M. B., Huberman, A. M., & Saldaña, J. (2019). Qualitative data analysis: A methods sourcebook (4th ed.). Sage.
  • Paivio, A. (1991). Images in mind: The evolution of a theory. New York: Harvester Wheatsheaf. Patton, M. Q. (2015). Qualitative research & evaluation methods (4th ed.). Sage.
  • Pellas, N. (2023). Integrating generative AI in STEM education: Opportunities and ethical challenges. Education and Information Technologies, 28(4), 4417–4434.
  • Sadler, T. D. (2011). Socio-scientific issues in the classroom: Teaching, learning, and research. Springer.
  • Selwyn, N. (2016). Education and technology: Key issues and debates (2nd ed.). Bloomsbury Academic.
  • Sreekantha, B., Khan, A., Mohamed, S. B., Asif, M. A., & Morya, A. M. (2024). Ad-vancements in text-to-video creation through AI models: A comprehensive review. Journal of Knowledge, Data Science & Information Management, 1, 22–29.
  • UNESCO. (2021). Reimagining our futures together: A new social contract for educa-tion. UNESCO.
  • Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. Cambridge, MA: Harvard University.
  • Williamson, B., & Piattoeva, N. (2021). Education governance and datafication. Euro-pean Educational Research Journal, 20(4), 435–456. https://doi.org/10.1177/1474904121993792
  • Yin, R. K. (2018). Case study research and applications: Design and methods. Sage.
  • Yükseköğretim Kurulu. (2023). Research ethics framework for educational studies in Türkiye. Ankara: YÖK.
  • Zeidler, D. L. (2014). Socioscientific issues as a curriculum emphasis. Science Education, 98(4), 745–770.
  • Zeidler, D. L., Sadler, T. D., Simmons, M. L., & Howes, E. V. (2005). Beyond STS: A research-based framework for socioscientific issues education. Science Education, 89(3), 357–377.
There are 31 citations in total.

Details

Primary Language Turkish
Subjects Development of Science, Technology and Engineering Education and Programs, Development of Environmental Education and Programs
Journal Section Research Article
Authors

Gamze Mercan 0000-0001-5515-999X

Early Pub Date November 27, 2025
Publication Date November 28, 2025
Submission Date July 24, 2025
Acceptance Date November 16, 2025
Published in Issue Year 2025 Volume: 9 Issue: 2

Cite

APA Mercan, G. (2025). Google VEO 3 As A Pedagogical Partner AI-Driven Storytelling For Conceptual, Affective, And Critical Learning in Science Education. Sinop Üniversitesi Sosyal Bilimler Dergisi, 9(2), 1443-1464. https://doi.org/10.30561/sinopusd.1749874