Design Thinking Methodology and Text-To-Image Artificial Intelligence: A Case Study in the Context of Furniture Design Education

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Abstract

The design thinking methodology is a problem-solving approach that involves empathising with end-users, (re)defining problems, brainstorming solutions creatively, and experimenting with prototypes and testing. It has been widely adopted in education to help students develop critical thinking, creativity, and problem-solving skills in design. On the other hand, text-to-image artificial intelligence is a method used to generate images from natural language descriptors (usually referred to as prompts). Design thinking methodology can teach students to think creatively and critically about real-world problems when applied in the classroom. In the context of design teaching at the University of Saint Joseph, Macao, students use the design thinking methodology to develop innovative proposals for furniture design solutions. Combining design thinking methodologies with text-to-image artificial intelligence can further enhance the learning experience by allowing students to generate visual representations of their ideas during the ideation phase. The authors developed a systematic approach to generate images for ideation on furniture design based on prompting text-to-image (PTI). The analysis related students’ results who applied the design thinking methodology without using AI tools and the results generated using a standard text-to-image programme. By combining both methods, teachers can help students develop critical thinking, creativity, and problem-solving skills, while also allowing them to generate visual representations in a different paradigm and, by so, being able to communicate their ideas with the most appropriate support for them.

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APA

Caires, C. S., Estadieu, G., & Olga Ng Ka Man, S. (2024). Design Thinking Methodology and Text-To-Image Artificial Intelligence: A Case Study in the Context of Furniture Design Education. In Springer Series in Design and Innovation (Vol. 33, pp. 113–134). Springer Nature. https://doi.org/10.1007/978-3-031-41770-2_7

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