Potential applications of AI-generated doll imagery for unique agricultural products

Authors

  • Shiu-Hua Wu Department of Digital Media Design, Assistant Professor, Shiuping University of Science and Technology
  • Kuo-Shan Yao

Keywords:

Artificial intelligence generated image technology, Doll design, Agricultural special products

Abstract

Faced with the rapid advancement of AI-generated image technology (AIGIT) and its ability to lower learning barriers, traditional design-education still has numerous challenges, such as limited time, varying artistic abilities, and creative bottlenecks. The authors tried to explore the application of AIGIT (specifically using the Stable Diffusion (SD) platform) in doll design education and the promotion of Taiwanese agricultural products (guava). A three-hour AI image generation training (prompt design to optimization) was assessed for its impact on boosting student creativity and efficiency. The final doll designs further were evaluated by industry experts across five dimensions: visual appeal, character characteristics, emotional expression, consistency, and functionality. Questionnaire results, visualized via R-programmed radar charts, revealed AI-assisted design fostered culturally resonant, visually appealing mascots, with iterative prompt refinement boosting success rates. AI emerged as both a creative and pedagogical tool, enhancing innovation and cultural branding. Besides, practical implications include strengthening local agricultural products through character design, while educational implications highlight AI’s role in lowering barriers to creative expression. The originality of this study lies in its integration of AI-generated doll imagery with agricultural product promotion, offering a novel model for design education and regional agricultural cultural development.

References

Barthes, R. (1977). Image Music Text (London, Fontana Collins). Fictional-Critical Writing, 67.

Calvert, S. L. (1999). Children's journeys through the information age. McGraw-Hill.

Chen, M. (2018). Talking about the Generative Adversarial Network (GAN) for elementary school students (In Chinese). https://ithelp.ithome.com.tw/articles/10196257.

Chen, A.Y., Wu, S.H., & Lee, S.J. (2025). Potential of Artificial Intelligence-Generated Image Technology in Mascot Design - A Case Study of Organic Fertilizer Brand Design. The 18th Information Education and Technological Applications Conference (IETAC 2025) held in Taichung City, Taiwan, on November 19, 2025.

Gatys, L. A., Ecker, A. S., & Bethge, M. (2015). A neural algorithm of artistic style. arXiv preprint arXiv:1508.06576.

Gong, Y. J., & Jiang, H. (2020). A brief analysis of the impact of artificial intelligence on industrial design. Industrial Design, 3, 53-54.

Guo, Z., & Shin, J. (2025). Research on the Design Path of Cultural and Creative Products for Cultural Heritage from the Perspective of AIGC—Taking the Shanxi Region as an Example. Design, 10(1), 13-25. https://doi.org/https://doi.org/10.12677/design.2025.101003.

Haenlein, M., & Kaplan, A. (2019). A brief history of artificial intelligence: On the past, present, and future of artificial intelligence. California management review, 61(4), 5-14.

Ho, J., Jain, A., & Abbeel, P. (2020). Denoising diffusion probabilistic models. Advances in neural information processing systems, 33, 6840-6851.

Jeng, C. C. (2019). Future imagination about Artificial Intelligence in Instructional Design (In Chinese). Journal of Education Research (307), 39-47.

Jenkins, H. (2006). Convergence culture: Where old and new media collid. New York, NY: New, 1-2.

Liang, D., Krishnan, R. G., Hoffman, M. D., & Jebara, T. (2018). Variational autoencoders for collaborative filtering. Proceedings of the 2018 world wide web conference.

McCarthy, J., Minsky, M. L., Rochester, N., & Shannon, C. E. (2006). A proposal for the dartmouth summer research project on artificial intelligence, august 31, 1955. AI magazine, 27(4), 12-12.

Popovic, M. (2023). The Ultimate Guide to Write Great Prompts for Stable Diffusion. Kanaries Blog, . https://docs.kanaries.net/articles/stable-diffusion-prompt-guide.

Rocca, J. (2019). Understanding generative adversarial networks (gans). Medium, 7, 20.

Yano, C. R. (2013). Pink globalization: Hello Kitty's trek across the Pacific. Duke University Press.

Yao, X., Zhong, Y., & Cao, W. (2025). The analysis of generative artificial intelligence technology for innovative thinking and strategies in animation teaching. Scientific Reports, 15(1), 18618.

Yao, Y. C., & Wu, S. (2024). Application of Artificial Intelligence (AI) in Symbol Design of Specialty Products in Tourism Destinations: A Case Study of the Guava Mascot Designed for Shetou's Specialty Agricultural Product. 2024 Symposium on Thinking and Management of Tourism and Hospitality Industry, Tainan City.

Yao, Y. C., & Wu, S. H. (2023). Exploring consumers' preferences for the main visual symbols in cultural and creative product design—A case study of Tian-Tou-Shui cultural and creative products The 16th Information Education and Technological Applications Conference (IETAC 2023) held on 10, Nov., 2023, Taichung City, Taiwan.

Zou, Z., Shi, T., Qiu, S., Yuan, Y., & Shi, Z. (2021). Stylized neural painting. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.

Downloads

Published

2026-08-21