Generative AI Framework for Digital Motif Design in Vocational Education: Integrating Pedagogy and Ethics

Nadira Nurannisa 1 Indonesia , Hamidah Suryani 1 Indonesia , Anas Arfandi 1 Indonesia
1 Department of Vocational Technology Education, Universitas Negeri Makassar, Indonesia
Link Copied!
Published 2026-09-20
Vol. 6 No. 1 (2026) PAGES 361-371
DOI 10.51574/ijrer.v6i1.5412
Views / Downloads 0 0

Conventional digital motif design instruction in higher vocational education often prioritizes complex software technicalities over conceptual design thinking. Generative AI (GenAI) can bypass manual drawing barriers, yet structured frameworks integrating prompt pedagogy and AI ethics remain lacking. This study conceptualizes and evaluates an ADDIE-based instructional e-module, enhanced by Borg & Gall protocols, that incorporates Midjourney prompt engineering, ethical AI frameworks, and interactive video tutorials within the Fashion Design program at Universitas Negeri Makassar. Expert validation confirmed exceptional content (92.56%) and media (91.27%) validity. Practicality testing demonstrated high user acceptance across individual (85.76%), small-group (89.77%), and large-group (84.17%) trials. A quasi-experimental evaluation (Experimental n=35 vs. Control n=22) revealed significant post-test learning gains in the experimental group (M=84.03, SD=6.42) compared to the control group (M=71.95, SD=5.88), confirmed by an Independent Samples t-test (t(55)=7.12, p<0.001) with a large effect size (Cohen's d=1.95). By shifting student cognitive load from manual illustration mechanics toward prompt syntax optimization and ethical human-AI co-creativity, this validated e-module bridges technical execution gaps, offering a scalable model for AI-driven vocational curricula.

Nurannisa, N., Suryani, H., & Arfandi, A. (2026). Generative AI Framework for Digital Motif Design in Vocational Education: Integrating Pedagogy and Ethics . ETDC: Indonesian Journal of Research and Educational Review , 6(1), 361–371. https://doi.org/10.51574/ijrer.v6i1.5412
More Citation Formats
Download Citation

Downloads

Download data is not yet available.
  1. Asdarina, A., Muhyidin, A., & Juniardi, Y. (2025). Digital E-Modules in Elementary Science Education: A Systematic Review on Supporting Education for Sustainable Development. Bio Educatio: The Journal of Science and Biology Education, 10(2), 71-87. https://doi.org/10.31949/be.v10i2.17940
  2. Belkina, M., Daniel, S., Nikolic, S., Haque, R., Lyden, S., Neal, P., ... & Hassan, G. M. (2025). Implementing generative AI (GenAI) in higher education: A systematic review of case studies. Computers and Education: Artificial Intelligence, 8, 100407. https://doi.org/10.1016/j.caeai.2025.100407
  3. Bergdahl, N., Sjöberg, J., & Nouri, J. (2025). Integrating a Student Wellbeing Tool for Mentors. In International Conference on Human-Computer Interaction (pp. 3-21). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-93502-2_1
  4. Bezcioğlu-Göktolga, I., Hsiao, Y. P., & Grootjans, L. (2025). Transforming group work: the impact of generative AI integration in an introductory research methods course. Journal of Research in Innovative Teaching & Learning, 1-14. https://doi.org/10.1108/JRIT-08-2025-0198
  5. Cahya, T., Muljani, S., & Purwanto, B. E. (2026). Development of a Differentiated Art Painting E-Module Integrating Environmental Education through Arts for Vocational Students. Journal of Educational Sciences, 10(2), 3054-3063. https://doi.org/10.31258/jes.10.2.p.3054-3063
  6. Daher, W., & Hussein, A. (2024). Higher education students’ perceptions of GenAI tools for learning. Information, 15(7), 416. https://doi.org/10.3390/info15070416
  7. Fang, C., Zhu, Y., Fang, L., Long, Y., Lin, H., Cong, Y., & Wang, S. J. (2025). Generative AI-enhanced human-AI collaborative conceptual design: A systematic literature review. Design Studies, 97, 101300. https://doi.org/10.1016/j.destud.2025.101300
  8. Hagendorff, T. (2024). Mapping the Ethics of Generative AI: A Comprehensive Scoping Review: T. Hagendorff. Minds and Machines, 34(4), 39. https://doi.org/10.1007/s11023-024-09694-w
  9. He, Y., & Zhang, S. (2025). Enhancing art creation through ai-based generative adversarial networks in educational auxiliary system: Y. he, s. zhang. Scientific Reports, 15(1), 29202. https://doi.org/10.1007/s11023-024-09694-w
  10. Herath, S., Bashardoust, A., Bole, Y., & Shrestha, Y. R. (2026). Design principles for text-to-image generative artificial intelligence creativity support tools for visual design. European Journal of Information Systems, 35(4), 679–704. https://doi.org/10.1080/0960085X.2026.2616042
  11. Huang, C., Zhang, Z., Mao, B., & Yao, X. (2022). An overview of artificial intelligence ethics. IEEE transactions on artificial intelligence, 4(4), 799-819. https://doi.org/10.1109/TAI.2022.3194503
  12. Jin, S., Tu, H., Li, J., Fang, Y., Qu, Z., Xu, F., ... & Lin, Y. (2024). Enhancing architectural education through artificial intelligence: a case study of an AI-assisted architectural programming and design course. Buildings, 14(6), 1613. https://doi.org/10.3390/buildings14061613
  13. Liu, Y., Liang, C., Xu, H., Wang, F., Hao, Y., Dong, J., ... & Yu, P. (2022). Digital art pattern design based on visual material colouring intelligent programming system. Mathematical Problems in Engineering, 2022(1), 2450074. https://doi.org/10.3390/buildings14061613
  14. Olson, M. (2025). Error as insight: AI hallucinations and the pedagogical possibilities of generative visual misrepresentation. Journal of Visual Literacy, 44(4), 454-470. https://doi.org/10.1080/1051144X.2025.2569015
  15. Özorhon, G., Nitelik Gelirli, D., Lekesiz, G., & Müezzinoğlu, C. (2025). AI-assisted architectural design studio (AI-a-ADS): How artificial intelligence join the architectural design studio?. International Journal of Technology and Design Education, 35(5), 1999-2023. https://doi.org/10.1007/s10798-025-09975-0
  16. Putland, E., Chikodzore-Paterson, C., & Brookes, G. (2025). Artificial intelligence and visual discourse: a multimodal critical discourse analysis of AI-generated images of “Dementia.” Social Semiotics, 35(2), 228–253. https://doi.org/10.1080/10350330.2023.2290555
  17. Saragi, D., Sinaga, O., & Tarigan, N. (2021). Application of Artistic Research Models to Improve the Variety of Research and the Quality of Student's Thesis in the Department of Fine Arts FBS Unimed. Budapest International Research and Critics in Linguistics and Education (BirLE) Journal, 4(4), 1206-1216. https://www.bircu-journal.com/index.php/birle/article/view/2760
  18. Sari, A. M., Ferdian, R., Pratama, O. Y., Efendi, N., & Dhari, B. W. (2024). Interactive E-modules for arts education: Improving comprehension and engagement in nusantara music courses. Jurnal Edutech Undiksha, 12(2), 346-354. https://ejournal.undiksha.ac.id/index.php/JEU/article/view/86881
  19. Sayem, A. S. M. (2022). Digital fashion innovations for the real world and metaverse. International Journal of Fashion Design, Technology and Education, 15(2), 139–141. https://doi.org/10.1080/17543266.2022.2071139
  20. Sholikhah, R., Triyono, M. B., Pramudita, D. A., & Nasrullah, H. (2026). Mapping Digital Fashion Skills in Green TVET: Foundations for Future Vocational Teacher Competency Framework through Systematic Review and Bibliometric Analysis. F1000Research, 15, 196. https://doi.org/10.12688/f1000research.176893.2
  21. Sweller, J. (2020). Cognitive load theory and educational technology: J. Sweller. Educational technology research and development, 68(1), 1-16. https://doi.org/10.1007/s11423-019-09701-3
  22. Tsidilo, I. M., Kravets, O., & Tkachuk, V. (2023). Artificial intelligence as a methodological innovation in the training of future designers: Midjourney tools. Information Technologies and Learning Tools, 95(3), 1–18. https://doi.org/10.33407/itlt.v95i3.5338
  23. Wang, J., Shi, Y., Chen, X., Lan, Y., & Liu, S. (2025). Teaching with artificial intelligence in architecture: Embedding technical skills and ethical reflection in a core design studio. Buildings, 15(17), 3069. https://doi.org/10.3390/buildings15173069
  24. Xu, C., & Huang, Y. (2024). Technological innovation in architectural design education: Empirical analysis and future directions of midjourney intelligent drawing software. Buildings, 14(10), 3288. https://doi.org/10.3390/buildings14103288
  25. Xu, C., Huang, Y., & Dewancker, B. (2020). Art inheritance: an education course on traditional pattern morphological generation in architecture design based on digital sculpturism. Sustainability, 12(9), 3752. https://doi.org/10.3390/su12093752
  26. Zheng, W. (2024). Intelligent e-learning design for art courses based on adaptive learning algorithms and artificial intelligence. Entertainment Computing, 50, 100713. https://doi.org/10.1016/j.entcom.2024.100713
  27. Zhou, E., & Lee, D. (2024). Generative artificial intelligence, human creativity, and art. PNAS nexus, 3(3), pgae052. https://doi.org/10.1093/pnasnexus/pgae052

Copyright (c) 2026 Nadira Nurannisa, Hamidah Suryani, Anas Arfandi

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Similar Articles

<< < 31 32 33 34 35 36 37 38 39 40 > >> 

You may also start an advanced similarity search for this article.

Dimension Badge

Downloads

Downloads
Crossmark Badge
Crossmark Badge
Section
Articles
Keywords
AI Ethics Digital Motif Design Generative AI Higher Vocational Education Prompt Engineering