Abstract
The purpose of this study is to explore if OpenAI's SORA, an AI program that creates video storyboards, can better provide writing related affective outcomes than traditional hand drawn sequences in a Content and Language Integrated Learning (CLIL) situation. A total of seventy-two undergraduate participants who enrolled in an English preparation program were randomly assigned to either a control or experimental group. The experimental group utilized SORA to transfigure written prompts into a brief video, while the control group produced nine hand images in a session for thirty minutes using contextual prompts. While both groups did not result in significant pre–post changes on the main scales, the control group had a statistically significant decline in motivation, while the experimental group held steady at baseline motivation levels. The engagement scores in the SORA condition were high even though students expressed concern with time and complications designing prompts. Taken together, these results provide evidence that a generative AI to complete storyboards can impact learners' motivation and engagement during CLIL writing tasks in a positive manner because it is a positive multimodal support that uncouples cognitive capacity from the drawing technicality and provides an opportunity for learners to communicate and elaborate linguistic ideas.
References
Ardasheva, Y., Tong, S. S., & Tretter, T. R. (2012). Validating the English Language Learner Motivation Scale (ELLMS). Learning and Individual Differences, 22(4), 473–483 https://doi.org/10.1016/j.lindif.2012.03.001
Behrens, K. A., Marbach-Ad, G., & Kocher, T. D. (2024). AI in the genetics classroom: A useful tool but not a replacement for creative writing. Journal of Science Education and Technology. https://doi.org/10.1007/s10956-024-10160-6
Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa
Chan, C. K. Y., & Tsi, L. H. Y. (2023). The AI revolution in education: Will AI replace or assist teachers in higher education? arxiv. https://doi.org/10.48550/arXiv.2305.01185
Chen, L., Chen, P., & Lin, Z. (2020). Artificial intelligence in education: A review. IEEE Access, 8, 75264–75278. https://doi.org/10.1109/ACCESS.2020.2988510
Chen, Y.C. (2024). Perceptions of AI-facilitated creativity in language education: A study on digital storytelling. The JALT CALL Journal, 20(3), xx–xx. https://doi.org/10.29140/jaltcall.v20n3.2089
Cheng, Y. S. (2004). A measure of second language writing anxiety. Journal of Second Language Writing, 13(4), 313–335. https://doi.org/10.1016/j.jslw.2004.07.001
Cheung, L., & Shi, H. (2025). Co-creating stories with generative AI. Australian Review of Applied Linguistics. https://doi.org/10.1075/aral.24101.che
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum.
Coyle, D., Hood, P., & Marsh, D. (2010). CLIL: Content and language integrated learning. Cambridge University Press.
Creswell, J. W. (2014). Research design: Qualitative, quantitative, and mixed methods approaches (4th ed.). Sage.
Deci, E. L., & Ryan, R. M. (2000). The “what” and “why” of goal pursuits: Human needs and the self-determination of behavior. Psychological Inquiry, 11(4), 227–268. https://doi.org/10.1207/S15327965PLI1104_01
Dewaele, J.-M., & MacIntyre, P. D. (2014). The two faces of Janus? Anxiety and enjoyment in the foreign-language classroom. Studies in Second Language Learning and Teaching, 4(2), 237–274. https://doi.org/10.14746/ssllt.2014.4.2.5
Field, A. (2013). Discovering statistics using IBM SPSS statistics (4th ed.). Sage.
Fraenkel, J. R., & Wallen, N. E. (2006). How to design and evaluate research in education (6th ed.). McGraw-Hill.
Gardner, R. C. (1985). Social psychology and second language learning: The role of attitudes and motivation. Edward Arnold.
Gordon, B. M. (2011). Artificial intelligence: Approaches, tools and applications. Nova Science Publishers.
Hayes, J. R. (2012). Modeling and remodeling writing. Written Communication, 29(3), 369–388. https://doi.org/10.1177/0741088312451260
Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign.
Huang, X., Zou, D., Cheng, G., Chen, X., & Xie, H. (2023). Trends, research issues and applications of artificial intelligence in language education. Educational Technology & Society, 26(1), 112–131. https://doi.org/10.30191/ETS.202301_26(1).0009
Isik, A., & Isik, B. (2021). We are reading the stories we write. Electronic Journal of Foreign Language Teaching, 18. https://doi.org/10.56040/isik1812
Ivanytska, N., Koliasa, O., Kovalevska, T., Matsera, O., & Tkachuk, T. (2024). Analyzing the possibilities of implementation of AI and social networks in teaching foreign-language students: Ukrainian universities case study. Arab World English Journal (Special Issue on ChatGPT), 306–318. https://doi.org/10.24093/awej/ChatGPT.21
Krashen, S. D. (1982). Principles and practice in second language acquisition. Pergamon Press.
Lee, H., & Lee, J. H. (2024). The effects of AI-guided individualized language learning: A meta-analysis. Language Learning & Technology, 28(2), 134–162. https://hdl.handle.net/10125/73575
Li, C., Zhang, L. J., & Jiang, G. Y. (2021). Conceptualisation and measurement of foreign-language learning burnout among Chinese EFL students. Journal of Multilingual and Multicultural Development, 45(4), 906–922. https://doi.org/10.1080/01434632.2021.1931246
López-Medina, E. F., & Casado, J. M. (2024). CLIL, a competence-based coping strategy against foreign-language anxiety in higher education. Discover Psychology, 4, 68. https://doi.org/10.1007/s44202-024-00187-6
MacIntyre, P. D., & Gardner, R. C. (1991). Investigating language-class anxiety using the focused-essay technique. The Modern Language Journal, 75(3), 296–304. https://doi.org/10.1111/j.1540-4781.1991.tb05358.x
McCarthy, J., Minsky, M. L., Rochester, N., & Shannon, C. E. (1955). A proposal for the Dartmouth summer research project on artificial intelligence. AI Magazine, 27(4), 12–14.
Mettewie, L., Van Mensel, L., De Smet, A., & Galand, B. (2024). A longitudinal test of the impact of CLIL on language emotions and learning motivation. System, 117, 103355. https://doi.org/10.1016/j.system.2024.103355
Mohammed, S. J., & Khalid, M. W. (2025). Under the world of AI-generated feedback on writing: Mirroring motivation, foreign language peace of mind, trait emotional intelligence, and writing development. Language Testing in Asia, 15(7), Article 43. https://doi.org/10.1186/s40468-025-00343-2
Niloy, A. C., Akter, S., Sultana, N., Sultana, J., & Rahman, S. I. U. (2024). Is ChatGPT a menace for creative-writing ability? An experiment. Journal of Computer Assisted Learning, 40(2), 919–933. https://doi.org/10.1111/jcal.12929
Noels, K. A. (2001). Learning Spanish as a second language: Learners’ orientations and perceptions of their instructors’ communication style. Language Learning, 51(1), 107–144.
O’Brien, H. L., Cairns, P., & Hall, M. (2018). A practical approach to measuring user engagement. International Journal of Human–Computer Studies, 112, 28–39. https://doi.org/10.1016/j.ijhcs.2018.01.004
Ouyang, F., & Jiao, P. (2021). Artificial-intelligence in education: The three paradigms. Computers and Education: Artificial Intelligence, 2, 100020. https://doi.org/10.1016/j.caeai.2021.100020
Paivio, A. (1991). Dual-coding theory: Retrospect and current status. Canadian Journal of Psychology, 45(3), 255–287. https://doi.org/10.1037/h0084295
Printer, L. (2023). Positive emotions and intrinsic motivation: A self-determination theory perspective on using co-created stories in the language-acquisition classroom. Language Teaching Research. Advance online publication. https://doi.org/10.1177/13621688231204443
Ramezanzadeh, A., & Ebadi, S. (2024). AI-generated storytelling for socially just EFL pedagogy: Exploring learner emotions through Fraser’s justice theory. System, 116, 103588. https://doi.org/10.1016/j.system.2024.103588
Roll, I., & Wylie, R. (2016). Evolution and revolution in artificial intelligence in education. International Journal of Artificial Intelligence in Education, 26(2), 582–599. https://doi.org/10.1007/s40593-016-0110-3
Song, C., & Song, Y. (2023). Enhancing academic-writing skills and motivation: Assessing the efficacy of ChatGPT in AI-assisted language learning for EFL students. Frontiers in Psychology, 14, Article 1260843. https://doi.org/10.3389/fpsyg.2023.1260843
Stewart, N., & Zheng, Y. (2024). Generative AI’s recolonization of EFL classrooms. Australian Review of Applied Linguistics. Advance online publication. https://doi.org/10.1075/aral.24091.ste
Sweller, J. (2010). Cognitive load theory: Recent theoretical advances. In J. L. Plass, R. Moreno, & R. Brünken (Eds.), Cognitive load theory (pp. 29–47). Cambridge University Press.
Tsz Kit, N. G. D., Luo, W., Chan, H., & Chu, S. (2022). An examination of primary students’ development in AI literacy through digital-story writing. Computers and Education: Artificial Intelligence, 3, 100054. https://doi.org/10.1016/j.caeai.2022.100054
Verdenhofa, O., Kinderis, R., & Berjozkina, G. (2024). AI as a creative partner: How artificial intelligence impacts student creativity and innovation. Baltic Journal of Economic Studies, 10(5), 111–119. https://doi.org/10.30525/2256-0742/2024-10-5-111-119
Villabona, N., & Cenoz, J. (2022). The integration of content and language in CLIL: A challenge for content-driven and language-driven teachers. Language, Culture & Curriculum, 35(1), 36–50. https://doi.org/10.1080/07908318.2021.1910703
Wang, D. (2024). Teacher- versus AI-generated (Poe application) corrective feedback and language learners’ writing anxiety, complexity, fluency, and accuracy. International Review of Research in Open and Distributed Learning, 25(3), Article 7646. https://doi.org/10.19173/irrodl.v25i3.7646
Wang, H., Fu, T., Du, Y., Gao, W., Huang, K., Liu, Z., … Marks, D. (2023). Scientific discovery in the age of artificial intelligence. Nature, 620(7972), 47–56. https://doi.org/10.1038/s41586-023-06221-2
Wang, H.-C. (2021). Exploring the relationships of achievement motivation and state anxiety to creative-writing performance in English as a foreign language. Thinking Skills and Creativity, 42, 100948. https://doi.org/10.1016/j.tsc.2021.100948
Woo, D. J., Guo, K., & Salas-Pilco, S. Z. (2024). Writing creative stories with AI: Learning designs for secondary-school students. Innovation in Language Learning and Teaching. https://doi.org/10.1080/17501229.2024.2384884
Woo, D. J., Guo, K., & Susanto, H. (2023). Exploring EFL students’ prompt engineering in human-AI story writing: An Activity-Theory perspective. arXiv. https://doi.org/10.48550/arXiv.2306.01798
Wu, X. Y. (2024). Artificial intelligence in L2 learning: A meta-analysis of contextual, instructional, and social-emotional moderators. System, 116, 103498. https://doi.org/10.1016/j.system.2024.103498
Zhu, X., Zhan, J., & Yao, Y. (2022). A latent-profile analysis of Chinese EFL learners’ enjoyment and anxiety in reading and writing: Associations with imaginative capacity and story-continuation writing performance. International Review of Applied Linguistics in Language Teaching. Advance online publication. https://doi.org/10.1515/iral-2022-0105
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