Liuqing Chen
Zhejiang University, Hangzhou, China
Bio-inspired design (BID) fosters innovative solutions in engineering by drawing inspiration from biology. Learning BID is crucial for developing multidisciplinary innovation skills of designers and engineers. While current BID education has attempted to enhance learners' understanding and analogical reasoning skills in BID, it often relies much on teachers' expertise. When learners turn to learn independently through some educational tools, there are challenges in understanding and reasoning practice in such complex multidisciplinary environment, as well as evaluating learning outcomes comprehensively. Addressing these challenges, we introduce a Large Language Models (LLMs)-driven BID education method based on a structured ontology, as well as 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 knowledge. Implementing the method, we developed BIDTrainer, an interactive BID education tool. User studies indicate that learners using BIDTrainer understood BID cases better, reason faster with higher interactivity than the baseline, and BIDTrainer assessed the learning outcomes consistent with experts.
doi.org/10.1145/3613904.3642887
The ACM CHI Conference on Human Factors in Computing Systems (https://chi2024.acm.org/)