Conference Agenda

This work addresses a gap in design education by providing a tailored tool—a "canvas"—to facilitate the practical and ethical integration of generative AI tools into the design process. It brings a human-centered approach to preparing design students for real-world encounters with AI. It’s especially useful for design educators and students who want to prepare for real-world challenges involving AI.

The purpose of this study is to clarify the changes in foundational studies of Japanese undergraduate industrial design education under the influence of technological advancements.

Some scholars believe that design acts as a bridge between technology and human needs, and that human-centered design can make technology more emotionally and culturally meaningful. Research on design education mainly focuses on its history and current state, with limited discussion on the future trends of design education. With the continuous development of artificial intelligence, the impact of technology on design education is a topic worth exploring. In industrial design education, research is relatively limited, and the existing studies often focus on a single institution. However, industrial design education in Japanese universities is diverse, and the geographic and cultural limitations of a single sample may lead to the generalizability of conclusions. Therefore, such studies cannot provide an effective reference for forecasting the development of Japanese undergraduate industrial design education in the intelligent era.To be able to forecast the future needs, it is necessary to clarify the changes and patterns of industrial design education under the influence of technological development. With the advent of the intelligent era, how industrial design education will evolve is a topic worth exploring.

Japanese design has gone through several phases, including functionalist design, commercialist design, and a shift from designing "objects" to designing "experiences" that address social, cultural, and ecological needs.

With the rapid advancement of AI in design, researchers have proposed that generative AI can enhance human creativity in design, particularly through AI-generated images as stimuli. To explore this, we conducted an exercise in a creative design course. The exercise began with an introduction to creative methods, followed by group ideation using a collaborative sketching method. Each participant then received an AI-generated image stimulus tailored to the design task. Finally, participants developed their best ideas and reflected on the process. The reflections were analyzed alongside evaluations of the final ideas by external evaluators, who assessed the novelty and usefulness of the 18 best ideas and determined their sources of inspiration: group collaboration, the AI-generated image, both, or neither.

Results revealed that participants generally viewed the AI-generated image stimuli as unhelpful or irrelevant for ideation. Evaluators found that group collaboration significantly contributed to the best ideas, while AI-generated stimuli played a minimal role. These findings underscore the critical role of human interaction in collaborative ideation and suggest that AI tools, while promising, require further refinement to support creativity in group settings effectively.

As advancements in language model-powered text-to-image AI platforms accelerate, individuals can increasingly generate high-fidelity visual content more efficiently, regardless of background. These platforms are powerful tools for rapidly iterating and visualizing packaging design concepts. This study assesses whether AI-generated milk package designs, steered by academic packaging research, will produce packaging outcomes that perform equal to or better than human-designed outcomes with minimal designer input.

For this study, researchers curated, summarized, and combined leading academic articles on packaging design into textual AI-prompts. The textual prompts were input into the platform RunDiffusion to generate visual milk packaging designs. The images created by the platform were reviewed by 48 human participants and compared to existing, human-made milk packaging designs to determine which designs perform better according to metrics used in a previously published study at E&PDE ‘23.

The survey results indicate that the human designs slightly outperformed the AI designs in purchase likelihood and most other categories when including all survey participants. However, when non-milk users (those who rarely or never drink milk) were excluded from the results, the AI designs slightly outperformed the human designs regarding purchase likelihood. This outcome suggests that AI platforms can efficiently produce packaging design outcomes that can compete with human designs. Further, it is important that design educators understand the implications of these results, suggesting that AI platforms will increasingly be used in design workflows and academic training.