Bio-inspired design (BID) fosters innovations in engineering. Learning BID is crucial for developing multidisciplinary innovation skills of designers and engineers. Current BID education aims to enhance learners’ understanding and analogical reasoning skills. However, it often heavily relies on the teachers’ expertise. When learners pursue independent learning using some educational tools, they face challenges in understanding and reasoning practice within this multidisciplinary field. Additionally, evaluating their learning outcomes comprehensively becomes problematic. Addressing these challenges, we introduce a LLMs-driven BID education method based on a structured ontology and three strategies: enhancing understanding through LLMs-enpowered "learning by asking", assisting reasoning by providing hints and feedback, and assessing learning outcomes through benchmarking against existing BID cases. Implementing the method, we developed BIDTrainer, a BID education tool. User studies indicate that learners using BIDTrainer understood BID knowledge better, reason faster with higher interactivity than the baseline, and BIDTrainer assessed the learning outcomes consistent with experts.

Rapid creation of novel product appearance designs that align with consumer emotional requirements poses a significant challenge. Text-to-image models, with their excellent image generation capabilities, have demonstrated potential in providing inspiration to designers. However, designers still encounter issues including aligning emotional needs, expressing design intentions, and comprehending generated outcomes in practical applications. To address these challenges, we introduce AutoSpark, an interactive system that integrates Kansei Engineering and generative AI to provide creativity support for designers in creating automobile appearance designs that meet emotional needs. AutoSpark employs a Kansei Engineering engine powered by generative AI and a semantic network to assist designers in emotional need alignment, design intention expression, and prompt crafting. It also facilitates designers’ understanding and iteration of generated results through fine-grained image-image similarity comparisons and text-image relevance assessments. The design-thinking map within its interface aids in managing the design process. Our user study indicates that AutoSpark effectively aids designers in producing designs that are more aligned with emotional needs and of higher quality compared to a baseline system, while also enhancing the designers’ experience in the human-AI co-creation process.