近年來，隨著人工智慧（AI）技術在設計領域的迅速發展，對於設計者的角色和工作方式帶來嶄新的挑戰與機遇。在設計流程中，設計者在設計初期需進行大量設計發想及草圖繪製，使得設計效率與品質至關重要。由於生成式AI工具能在短時間內生成大量設計草圖，並且不限於設計者的使用，對於設計界的影響掀起一片討論。因此，本研究聚焦於產品設計領域，探討在生成式AI工具的使用中，專業產品設計者和新手設計者使用prompt生成產品草圖的模式與策略差異，以及兩者生成草圖的品質差異。本研究採用實驗方法，招募專業產品設計者與新手設計者各15位，兩組受測者利用生成式AI工具DALL-E生成產品草圖。在確保受測者對生成式AI工具有一定的使用經驗後，提供具體的設計概要和評分標準，要求在有限時間內生成產品草圖。為了分析設計成果品質，本研究邀請各一位產品設計專業的博士與業師，分別對設計成果進行盲評。實驗結果將有助於評估AI工具對設計者角色的重要性和取代性，探討新手設計者使用AI工具後能否達到設計者的水平。

In recent years, the rapid development of artificial intelligence (AI) technologies in the field of design has introduced both challenges and opportunities for how designers work and define their roles. During the early phase of the design process, designers are often required to generate many initial concepts and sketches, making the efficiency and quality of this stage especially critical. Because generative AI tools can produce a high volume of design sketches in a short time and are accessible to users beyond trained designers, their impact on the design field has become a widely discussed topic. This study focuses on product design and examines the differences in the use of prompts between professional designers and novice designers when generating product sketches with generative AI tools. It also examines differences in the quality of the resulting sketches. An experimental study was conducted with 15 professional product designers and 15 novice designers. All participants used the generative AI tool DALL-E to create design sketches. After confirming that participants had a baseline level of experience with AI tools, they were given a structured design brief and evaluation criteria. Each participant was asked to generate sketches within a fixed time limit. To evaluate the design outcomes, one academic expert with a PhD in design and one experienced industry professional were invited to conduct blind reviews. The results provide insights into the evolving role of AI tools in design, particularly whether novice users can achieve outcomes comparable to those of professionals when supported by AI.

Benbya, H., Strich, F., & Tamm, T. (2024). Navigating generative artificial intelligence: Promises and perils for knowledge and creative work. Journal of the Association for Information Systems, 25(1), 23–36. https://doi.org/10.17705/1jais.00861
Bouschery, S. G., Blazevic, V., & Piller, F. T. (2023). Augmenting human innovation teams with artificial intelligence: Exploring transformer-based language models. Journal of Product Innovation Management, 40(2), 139–153. https://doi.org/10.1111/jpim.12656
Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa
Braun, V., & Clarke, V. (2021). Thematic analysis: A practical guide. London: SAGE.
Cai, A., Rick, S. R., Heyman, J., Zhang, Y., Filipowicz, A., Hong, M. K., Klenk, M., & Malone, T. (2023). DesignAID: Using generative AI and semantic diversity for design inspiration. In Proceedings of the Collective Intelligence Conference (CI ’23), November 06–09, 2023, Delft, Netherlands. ACM. https://doi.org/10.1145/3582269.3615596
Chandrasegaran, S., Badam, S. K., Zhou, N., Zhao, Z., Kisselburgh, L., Peppler, K., Elmqvist, N., & Ramani, K. (2017). Merging sketches for creative design exploration: An evaluation of physical and cognitive operations. Proceedings of Graphics Interface 2017. https://www.researchgate.net/publication/315655302
Chandrasekera, T., Hosseini, Z., & Perera, U. (2024). Can artificial intelligence support creativity in early design processes? International Journal of Architectural Computing. https://doi.org/10.1177/14780771241254637
Chen, S.-C., Mathew, J. J., Feng, C.-T., & Hsu, T. (2022). An Innovative Method to Monitor and Control an Injection Molding Process Condition using Artificial Intelligence based Edge Computing System. International Conference on Applied System Innovation, 41–45. https://doi.org/10.1109/ICASI55125.2022.9774445
Chen, X. (2024). Advancements and technical challenges in generative models of artificial intelligence painting. Proceedings of the 2nd International Conference on Machine Learning and Automation. https://doi.org/10.54254/2755-2721/101/20241001
Clancy, S. M., Murphy, L. R., Daly, S. R., & Seifert, C. M. (2023). Iterative transformations for deeper exploration during concept generation. International Journal of Technology and Design Education. https://doi.org/10.1007/s10798-023-09813-1
Dellermann, D., Ebel, P., Söllner, M., & Leimeister, J. M. (2019). Hybrid intelligence. Business & Information Systems Engineering, 61(5), 637–643. https://doi.org/10.1007/s12599-019-00595-2
Engawi, D., Gere, C., & Richards, D. (2022). The impact of artificial intelligence on graphic design: Exploring the challenges and possibilities of AI-driven autonomous branding. In G. Bruyns & H. Wei (Eds.), IASDR 2021: With design: Reinventing design modes (pp. 3567-3576). Springer. https://doi.org/10.1007/978-981-19-4472-7_238
Gallagher, C. L. (2017). Sketching for ideation: A structured approach for increasing divergent thinking. Proceedings of CHI 2017 Student Research Competition, May 6–11, Denver, CO, USA. ACM. https://doi.org/10.1145/3027063.3048424
Gatys, L. A., Ecker, A. S., & Bethge, M. (2016). Image style transfer using convolutional neural networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2414–2423. https://doi.org/10.1109/CVPR.2016.265
Goldschmidt, G. (1991). The dialectics of sketching. Creativity Research Journal, 4(2), 123–143. https://doi.org/10.1080/10400419109534381
Goldschmidt, G. (2014). Modeling the role of sketching in design idea generation. In J. S. Gero (Ed.), Design creativity research (pp. 225–235). Springer. https://doi.org/10.1007/978-1-4471-6338-1_21
Hähnlein, F., Gryaditskaya, Y., Sheffer, A., & Bousseau, A. (2022). Symmetry-driven 3D reconstruction from concept sketches. Proceedings of the SIGGRAPH 2022 - International Conference & Exhibition on Computer Graphics & Interactive Techniques, August 7–11, Vancouver, Canada. ACM. https://doi.org/10.1145/3528233.3530723
Hanna, D. M. (2023). The use of artificial intelligence art generator “Midjourney” in artistic and advertising creativity. Journal of Design Studies and Applied Arts, 4(2), 42-58. https://doi.org/10.21608/jdsaa.2023.169144.1231
Holliman, A. F., & Brisco, R. (2024). Is pen-to-paper the buggy whip of design? Assessing the use of AI tools for design sketching. In C. Gray, E. Ciliotta Chehade, P. Hekkert, L. Forlano, P. Ciuccarelli, & P. Lloyd (Eds.), DRS2024: Boston, 23–28 June. Design Research Society. https://doi.org/10.21606/drs.2024.289
Hong, M. K., Hakimi, S., Chen, Y.-Y., Toyoda, H., Wu, C., & Klenk, M. (2023). Generative AI for product design: Getting the right design and the design right. Proceedings of the ACM CHI Conference on Human Factors in Computing Systems (CHI ’23), April 23–28, Hamburg, Germany. https://doi.org/10.1145/nnnnnnn.nnnnnnn
Houde, S., & Hill, C. (1997). What do prototypes prototype? In M. Helander, T. Landauer, & P. Prabhu (Eds.), Handbook of Human-Computer Interaction (2nd ed.) (pp. 367-382). Elsevier Science B.V.
Hutson, J., & Robertson, B. (2023). Exploring the educational potential of AI generative art in 3D design fundamentals: A case study on prompt engineering and creative workflows. Global Journal of Human-Social Science: Arts & Humanities – Psychology, 23(2), 42-58. https://digitalcommons.lindenwood.edu/faculty-research-papers/485
Iorliam, A., & Ingio, J. A. (2024). A comparative analysis of generative artificial intelligence tools for natural language processing. Journal of Computing Theories and Applications, 2(1). https://doi.org/10.62411/jcta.9447
Jilka, M. (2018). Application of the Double Diamond framework to prepare the communication strategy of a great sports event. Proceedings of CORE. Retrieved from https://core.ac.uk/display/230619781
Joosten, J., Bilgram, V., Hahn, A., & Totzek, D. (2024). Comparing the ideation quality of humans with generative artificial intelligence. IEEE Engineering Management Review, 52(2), 153-162. https://doi.org/10.1109/EMR.2024.3353338
Kaljun, K. K., & Kaljun, J. (2024). Enhancing creativity in sustainable product design: The impact of generative AI tools at the conceptual stage. Proceedings of the 47th MIPRO Convention on ICT and Electronics, 451-456. https://doi.org/10.1109/MIPRO60963.2024.10569541
Karras, T., Laine, S., & Aila, T. (2019). A style-based generator architecture for generative adversarial networks. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 4401–4410. https://doi.org/10.1109/CVPR.2019.00453
Kress, G., & van Leeuwen, T. (2006). Reading images: The grammar of visual design (2nd ed.). Routledge. https://www.routledge.com/Reading-Images-The-Grammar-of-Visual-Design/Kress-vanLeeuwen/p/book/9780415319157
Leclercq, P., & Heylighen, A. (2002). Analogies per hour: A designer's view on analogical reasoning. In J. S. Gero (Ed.), Artificial Intelligence in Design '02 (pp. 285-303). Kluwer Academic Publishers. https://doi.org/10.1007/978-94-017-0795-4_14
Lee, S., Hoover, B., Strobelt, H., Wang, Z. J., Peng, S., Wright, A., Li, K., Park, H., Yang, H., & Chau, D. H. (2024). Diffusion Explainer: Visual explanation for text-to-image Stable Diffusion. Proceedings of the ACM CHI Conference on Human Factors in Computing Systems (CHI ’24). ACM. https://doi.org/10.48550/arXiv.2305.03509
Lee, S.-Y., Law, M., & Hoffman, G. (2024). When and how to use AI in the design process? Implications for human-AI design collaboration. International Journal of Human–Computer Interaction. https://doi.org/10.1080/10447318.2024.2353451
Lee, Y.-H., & Lin, T.-H. (2023). The feasibility study of AI image generator as shape convergent thinking tool. In H. Degen & S. Ntoa (Eds.), HCII 2023, LNAI 14050 (pp. 575-589). Springer. https://doi.org/10.1007/978-3-031-35891-3_36
Li, Y. (2024). Will artificial intelligence platforms replace designers in the future? Analyzing the impact of artificial intelligence platforms on the engineering design industry through color perception. Engineering Applications of Artificial Intelligence, 138, Article 109369. https://doi.org/10.1016/j.engappai.2024.109369
Lim, Y.-K., Stolterman, E., & Tenenberg, J. (2008). The anatomy of prototypes: Prototypes as filters, prototypes as manifestations of design ideas. ACM Transactions on Computer-Human Interaction, 15(2), 1–27. https://doi.org/10.1145/1375761.1375762
Liu, X., Fan, H., Hansen, P., Zou, N., & Chai, C. (2024). Exploring the efficacy of hybrid-sketch: An investigation into conceptual support in the early design stage. Journal of Engineering Design. https://doi.org/10.1080/09544828.2024.2365118
Moreira da Silva, A. (2023). Sketches versus artificial intelligence systems in the design creative process. Proceedings of Intelligent Human Systems Integration (IHSI 2023), 69, 459–467. https://doi.org/10.54941/ahfe1002866
Nagapushpa, B. M., Kanchana, P., & Monish, P. (2024). Revolutionizing art and design through AI: Balancing innovation, ethics, and future preparedness. International Journal of Scientific Research in Engineering and Management, 8(11). https://doi.org/10.55041/IJSREM38443
Owen, A. E., & Roberts, J. C. (2024). Towards a generative AI design dialogue. EG UK Computer Graphics & Visual Computing 2024 Proceedings. Eurographics. https://doi.org/10.2312/egv20241012
Pamuk, Z. (2023). Artificial intelligence and the problem of judgment. Ethics & International Affairs, 37(2), 232-243. https://doi.org/10.1017/S089267942300014X
Perez, V., Millon, E., Petrescu, M., Ebbecke, T., Gu, T., & Rodriguez Prado, D. (2024). KREA: Real-time AI-powered creative suite for next-generation digital art. Proceedings of SIGGRAPH Real-Time Live! '24, July 27–August 01, 2024, Denver, CO, USA. ACM. https://doi.org/10.1145/3641520.3665303
Ramesh, A., Pavlov, M., Goh, G., Gray, S., Voss, C., Radford, A., Chen, M., & Sutskever, I. (2021). Zero-shot text-to-image generation. arXiv preprint arXiv:2102.12092. https://arxiv.org/abs/2102.12092
Rathwad, G., Yadav, P., & Jain, J. (2024). Generative AI: Shaping the future while disrupting the present. International Journal for Multidisciplinary Research, 6(5), 1–7. https://doi.org/10.36948/ijfmr.2024.v06i05.28085
Rodriguez, C. (2024). AI in graphic design: Here to help or replace us? [Master of Fine Arts Thesis, Liberty University]. Liberty University School of Communication and the Arts.
Rodgers, P. A., Green, G., & McGown, A. (2000). Using concept sketches to track design progress. Design Studies, 21(5), 451–464. https://doi.org/10.1016/S0142-694X(00)00018-1
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., & Ommer, B. (2022). High-resolution image synthesis with latent diffusion models. arXiv preprint arXiv:2112.10752. https://arxiv.org/abs/2112.10752
Sai, S., Sai, R., & Chamola, V. (2024). Generative AI for Industry 5.0: Analyzing the impact of ChatGPT, DALLE, and other models. IEEE Open Journal of the Communications Society. https://doi.org/10.1109/OJCOMS.2024.3400161
Schleith, J., & Tsar, D. (2022). Triple diamond design process: Human-centered design for data-driven innovation. In M. Kurosu (Ed.), HCII 2022: Design, user experience, and usability (pp. 136–146). Springer Nature Switzerland AG. https://doi.org/10.1007/978-3-031-17615-9_9
Şen Bayram, A. K., & Körükcü, B. A. (2024). A biomimetic sketch-based form-finding tool. Kent Akademisi Dergisi, 17(4), 1285-1297. https://doi.org/10.35674/kent.1462381
Sevier, D. C., McKilligan, S., Baker, I. N., Jablokow, K., Daly, S. R., & Silk, E. M. (2017). Towards the development of an elaboration metric for concept sketches. Proceedings of the ASME 2017 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (IDETC/CIE 2017), August 6-9, Cleveland, Ohio. https://doi.org/10.1115/DETC2017-67375
Silva, B., Marques, R., Faustino, D., Ilheu, P., Santos, T., Sousa, J., & Rocha, A. D. (2023). Enhance the injection molding quality prediction with artificial intelligence to reach zero-defect manufacturing. Processes, 11(62). https://doi.org/10.3390/pr11010062
Song, S., & Agogino, A. M. (2004). Insights on designers’ sketching activities in new product design teams. Proceedings of the ASME 2004 Design Engineering Technical Conference and Computers and Information in Engineering Conference (DETC2004), Salt Lake City, Utah, September 28 – October 2. https://doi.org/10.1115/DETC2004-57474
Sreenivasan, A., & Suresh, M. (2024). Design thinking and artificial intelligence: A systematic literature review exploring synergies. International Journal of Innovation Studies, 8, 297-312. https://doi.org/10.1016/j.ijis.2024.05.001
Strickfaden, M., & Rodgers, P. A. (2002). A comparative study of iconic influences amongst British and Canadian design students. Proceedings of the Common Ground – Design Research Society International Conference 2002. Napier University.
Taniguchi, S., & Nagai, Y. (2023). Creative exploration through idea sketches in the product design process: A case of the designers highly skilled in digital technologies. IIAI Letters on Informatics and Interdisciplinary Research, 3(LIIR073). https://doi.org/10.52731/liir.v003.073
Tran Luciani, D. (2013). Designing for sketching to support concept exploration. Linköping Studies in Science and Technology, Dissertation No. 2013. Linköping University. ISBN 978-91-7519-009-9.
van den Broek, S. J. C. E., Sankaran, S., de Wit, J. M. S., & de Rooij, A. (2024). Exploring the supportive role of artificial intelligence in participatory design: A systematic review. Proceedings of the Participatory Design Conference 2024 (PDC ’24 Vol. 2), August 11–16, Sibu, Malaysia. ACM. https://doi.org/10.1145/3661455.3669868
Weavely. (2024). What AI tools do designers use the most? Weavely AI Blog. Retrieved from https://www.weavely.ai/blog/what-ai-tools-do-designers-use-the-most
Ye, Y., Hao, J., Hou, Y., Wang, Z., Xiao, S., Luo, Y., & Zeng, W. (2024). Generative AI for visualization: State of the art and future directions. Visual Informatics, 8, 43–66. https://doi.org/10.1016/j.visinf.2024.04.003
Yuan, P. F. (2023). Toward a generative AI-augmented design era. Architectural Intelligence, 2(16). https://doi.org/10.1007/s44223-023-00038