The Application of an “AI Software + Creative Design” Teaching Model for Enhancing Creativity in University Design Education
Article Sidebar
Main Article Content
Abstract
This study examines the application of the “AI Software + Creative Design” teaching model in university-level creative design education, with the aim of exploring its implementation and pedagogical effectiveness. As artificial intelligence technologies rapidly advance, AI tools are increasingly adopted in creative design, offering new perspectives and methodological support for teaching innovation. In response to evolving educational demands, this research investigates how AI software can be effectively integrated into teaching practices. The study was conducted in three main steps. First, research participants were selected from six groups of undergraduate students majoring in creative design using a random sampling method to ensure representativeness. Second, various AI tools were introduced and applied in teaching activities, including automated design generation tools, intelligent image editing software, and virtual reality–based platforms, with attention to their educational applications. Third, teaching outcomes were evaluated through instructional experiments and classroom case studies, focusing on students’ creative thinking, design process optimization, and overall learning efficiency. The results indicate that integrating AI software significantly enhances students’ design skills and innovative capabilities while improving traditional teaching approaches. AI-supported instruction also promotes a more personalized and interactive learning environment, increasing students’ motivation and sense of achievement. This study concludes by highlighting the benefits and challenges of AI integration and offering recommendations for future research and teaching development in creative design education.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
References
Boden, M. A. (1998). Creativity and artificial intelligence. Artificial Intelligence, 103(1–2), 347–356. https://doi.org/10.1016/S0004-3702(98)00055-1
Higher Education Policy Institute. (2025). Student generative AI survey 2025: Changes in adoption and academic use. https://www.hepi.ac.uk/reports/student-generative-ai-survey-2025/
Bjorklund, T. A., Keipi, T., & Maula, H. (2020). Crafters, explorers, innovators, and co-creators: Narratives in designers’ identity work. Design Studies, 68, 82–112. https://doi.org/10.1016/j.destud.2020.03.003
Amershi, S., Weld, D., Vorvoreanu, M., Fourney, A., Nushi, B., Collisson, P., & Horvitz, E. (2019). Guidelines for human–AI interaction. Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, 1–13. https://doi.org/10.1145/3290605.3300233
Wang, Z., Li, M., Lu, J., & Cheng, X. (2022). Business innovation based on artificial intelligence and blockchain technology. Information Processing & Management, 59(1), Article 102759. https://doi.org/10.1016/j.ipm.2021.102759
An, T., & Oliver, M. (2021). What in the world is educational technology? Rethinking the field from the perspective of the philosophy of technology. Learning, Media and Technology, 46(1), 6–19. https://doi.org/10.1080/17439884.2020.1860196
Campbell, D. T. (1960). Blind variation and selective retention in creative thought as in other knowledge processes. Psychological Review, 67(6), 380–400. https://doi.org/10.1037/h0040373
Weisberg, R. W. (2006). Creativity: Understanding innovation in problem solving, science, invention, and the arts. John Wiley & Sons.
Nguyen, M., & Mougenot, C. (2022). A systematic review of empirical studies on multidisciplinary design collaboration: Findings, methods, and challenges. Design Studies, 81, Article 101103. https://doi.org/10.1016/j.destud.2022.101103
Miller, A. I. (2019). The artist in the machine: The world of AI-powered creativity. MIT Press.
Dulyan, A., & Edmonds, E. (2010). AUXie: Initial evaluation of a blind-accessible virtual museum tour. In Proceedings of the 22nd Conference of the Computer-Human Interaction Special Interest Group of Australia on Computer-Human Interaction (pp. 272–275). https://doi.org/10.1145/1952222.1952282
Wu, Z., Ji, D., Yu, K., Zeng, X. C., Wu, D., & Shidujaman, M. (2021). AI creativity and the human–AI co-creation model. In M. Kurosu (Ed.), Human-computer interaction: Theory, methods and tools (LNCS Vol. 12762, pp. 171–190). Springer. https://doi.org/10.1007/978-3-030-78462-1_13
Fetzer, J. H., & Dartnall, T. (2010). Artificial intelligence and creativity: An interdisciplinary approach. Springer Netherlands.
Booker, J. D., Raines, M., & Swift, K. G. (2001). Designing capable and reliable products. Butterworth-Heinemann.
Leinonen, T., Veermans, M., & Toikkanen, T. (2016). Mobile apps for reflection in learning: Design research in K–12 education. British Journal of Educational Technology, 47(1), 15–18. https://doi.org/10.1111/bjet.12320
Miller, A. I. (2012). Insights of genius: Imagery and creativity in science and art. Springer. https://doi.org/10.1007/978-1-4614-1093-5
Dartnall, T. (Ed.). (1994). Artificial intelligence and creativity: An interdisciplinary approach. Springer.
