AI-SUPPORTED ANİMATED VİDEO ARTİCLES: A NEXT-GENERATİON PRESENTATİON OF ACADEMİC TEXTS

Yıl 2025, Sayı: 4, 46 - 58, 24.09.2025

Ülkü Sönmez , Doç. Dr. Ali Kılıç

Öz

This study examines the transformation of academic content from traditional text-based formats into audiovisual presentations, with a particular focus on AI-assisted animated video articles. As information consumption habits shift and digital media becomes more dominant, the need to present academic knowledge not only in readable but also in watchable and experiential formats is growing. Within the framework of multimedia learning theory, the study discusses the pedagogical value of animation and evaluates the roles of natural language processing, text-to-speech synthesis, and automation technologies in the production process. It also explores human–AI collaboration and related ethical considerations. The study’s most original contribution lies in emphasizing the importance of transforming previously published academic works into AI-generated animated video articles using current technologies. This approach is presented as an innovative and transformative method for transmitting academic heritage into the future through new media formats.

Anahtar Kelimeler

Animation , Video Article , Artificial Intelligence , Science Communication , Academic Content Visualization

YAPAY ZEKÂ DESTEKLİ ANİMASYON VİDEO MAKALELER: AKADEMİK METİNLERİN YENİ NESİL SUNUMU

Öz

Bu çalışma, akademik içeriklerin geleneksel metin formatından görsel-işitsel sunumlara dönüşümünü ele almakta; özellikle yapay zekâ destekli animasyon video makale formatının olanaklarını incelemektedir. Değişen bilgi tüketim alışkanlıkları ve dijitalleşmenin etkisiyle, akademik bilginin yalnızca okunabilir değil, izlenebilir ve deneyimlenebilir biçimlerde sunulması giderek önem kazanmaktadır. Çalışmada, multimedya öğrenme kuramı çerçevesinde animasyonun öğrenmeye katkısı açıklanmış; doğal dil işleme, metinden sese ve otomasyon teknolojilerinin üretim sürecindeki işlevi değerlendirilmiştir. Ayrıca insan-yapay zekâ iş birliğine dayalı üretim modeli ve etik boyutlar tartışılmıştır. En özgün katkı olarak, geçmişte üretilmiş akademik çalışmaların günümüz teknolojileriyle yapay zekâ destekli animasyon video makalelere dönüştürülmesinin önemi vurgulanmıştır. Bu yaklaşım, akademik mirasın yeni medya biçimleriyle geleceğe aktarılması açısından yenilikçi ve dönüştürücü bir yöntem olarak değerlendirilmektedir.

Anahtar Kelimeler

Animasyon , Video Makale , Yapay Zekâ , Bilim İletişimi , Akademik İçerik Görselleştirme

Kaynakça

• Carr, N. (2010). The shallows: What the Internet is doing to our brains. W. W. Norton & Company.
• Chung, Y., Lee, W., Lee, J., & Lee, H. (2023). Text-to-video generation: A review of recent methods. ACM Computing Surveys, 56(3), 1–35. https://doi.org/10.1145/3583553
• COPE. (2023). Guidelines on the use of AI tools in scholarly publishing. Committee on Publication Ethics. https://publicationethics.org/news/cope-position-statement-use-artificial-intelligence-ai-tools-scholarly-publishing
• Daft, R. L., & Lengel, R. H. (1986). Organizational information requirements, media richness and structural design. Management Science, 32(5), 554–571. https://doi.org/10.1287/mnsc.32.5.554
• European Science Communication Institute. (2022). Science communication in the digital age: Strategies for engaging the public. https://www.esci.eu
• Hirschberg, J., & Manning, C. D. (2015). Advances in natural language processing. Science, 349(6245), 261–266. https://doi.org/10.1126/science.aaa8685
• Höffler, T. N., & Leutner, D. (2007). Instructional animation versus static pictures: A meta-analysis. Learning and Instruction, 17(6), 722–738. https://doi.org/10.1016/j.learninstruc.2007.09.013
• Kaushal, K., & Panda, B. N. (2019). Effectiveness of instructional animation on academic achievement of students: A meta-analysis. Malaysian Journal of Learning and Instruction, 16(2), 55–79. https://eric.ed.gov/?id=EJ1219792
• Klein-Avraham, I., Neuberger, C., & Kusters, G. (2023). Ethical dilemmas of AI-generated content: Ownership, transparency and credibility. Journal of Science Communication, 22(3), A03. https://doi.org/10.22323/2.22030203
• Kusters, G., Klein-Avraham, I., & Neuberger, C. (2023). Ethical issues in AI-generated content: Between transparency and responsibility. Journal of Science Communication, 22(3), A03. https://doi.org/10.22323/2.22030203
• Leiker, M. J., Gyllen, J., Eldesouky, H., & Cukurova, M. (2023). AI-generated vs human-made instructional videos: Comparative impacts on learning outcomes and engagement. arXiv preprint arXiv:2304.03784. https://arxiv.org/abs/2304.03784
• Lowe, R. K. (2004). Interrogation of a dynamic visualization for learning about a complex system. Learning and Instruction, 14(3), 257–274. https://doi.org/10.1016/j.learninstruc.2004.06.002
• Lynch, M. (2006). The production of scientific images: Vision and re-vision in the history, philosophy, and sociology of science. MIT Press.
• Mayer, R. E. (2001). Multimedia learning. Cambridge University Press.
• Mayer, R. E. (2008). Applying the science of learning: Evidence-based principles for the design of multimedia instruction. American Psychologist, 63(8), 760–769. https://doi.org/10.1037/0003-066X.63.8.760
• Mayer, R. E. (2009). Multimedia learning (2nd ed.). Cambridge University Press.
• OECD. (2020). Science for policy: Principles and practices for effective communication. https://www.oecd.org/science/science-communication-2020.htm
• Pellas, N. (2024). The impact of AI generated instructional videos on problem based learning in science teacher education. Education Sciences, 15(1), 102. https://doi.org/10.3390/educsci15010102
• Pew Research Center. (2014). The audience for digital news videos. Washington, DC: Pew Internet & American Life Project. https://www.pewresearch.org/journalism/2014/03/26/the-audience-for-digital-news-videos/
• Ploetzner, R., Berney, S., & Bétrancourt, M. (2021). Learning from dynamic visualizations: A meta-analysis. Instructional Science, 49(5), 661–691. https://doi.org/10.1007/s11251-021-09541-w
• Priest, S. (2010). Communicating climate change: The path forward. In L. M. Milfont (Ed.), Climate Change and Psychology (pp. 55–67). American Psychological Association.
• Sakthi, S. S., & Arulmurugan, R. (2020). An assistive voice-based text-to-speech model for visually impaired individuals. International Journal of Speech Technology, 23(4), 947–957. https://doi.org/10.1007/s10772-020-09737-3
• Schäfer, M. S., Taddicken, M., Metag, J., & Kristiansen, S. (2023). Communicating science in the age of artificial intelligence: Risks and recommendations. Public Understanding of Science, 32(1), 3–19. https://doi.org/10.1177/09636625221115545
• Schaffhauser, D. (2021). Study finds use of video boosts learning. Campus Technology. https://campustechnology.com/articles/2021/02/24/study-finds-use-of-video-boosts-learning.aspx
• Stavesand, H., & Schröder, D. (2024). The blurred line between deepfake and AI-enhanced media: Ethical considerations in science communication. AI & Society. https://doi.org/10.1007/s00146-024-01721-2
• Sweller, J. (1994). Cognitive load theory, learning difficulty, and instructional design. Learning and Instruction, 4(4), 295–312. https://doi.org/10.1016/0959-4752(94)90003-5
• Tan, X., Qin, T., Wen, Z., Zhou, Y., Liu, L., & Zhao, R. (2021). A survey on neural text-to-speech synthesis. ACM Transactions on Intelligent Systems and Technology, 12(2), 1–35. https://doi.org/10.1145/3447381
• Yao, L., Wang, C., & Zhang, Y. (2021). Effectiveness of animations in multimedia learning: A meta-analysis. Educational Technology & Society, 24(1), 233–247.
• Zhang, Y., & Zhou, Z. (2020). Video abstracts: A new way for scholarly communication. Journal of the Association for Information Science and Technology, 71(2), 151–164. https://doi.org/10.1002/asi.24232