BIDTrainer: An LLMs-driven Education Tool for Enhancing the Understanding and Reasoning in Bio-inspired Design
Liuqing Chen, Zhaojun Jiang, Duowei Xia, Zebin Cai, Lingyun Sun, Peter R. N. Childs, Haoyu Zuo
Abstract
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.
Ask about this paper
Ask your agent about it.
Lune has read the top-tier papers around this one, so every answer names the papers it rests on.
Your agent calls
Lunesearch_papers
Free to start. No credit card required.
Terminal
Install the CLIlune papers get f83ef777-ed92-4e4b-90f7-d54d19dcca30Cited by top-tier papers8
- How CO2STLY Is CHI? The Carbon Footprint of Generative AI in HCI Research and What We Should Do About ItNanna Inie, Jeanette Falk, Raghavendra SelvanCHI 2025 · 33 citations
- Breaking Barriers or Building Dependency? Exploring Team-LLM Collaboration in AI-infused Classroom DebateZihan Zhang, Black Sun, Pengcheng AnCHI 2025 · 24 citations
- Unlocking Scientific Concepts: How Effective Are LLM-Generated Analogies for Student Understanding and Classroom Practice?Zekai Shao, Siyu Yuan, Lin Gao, Yixuan He et al.CHI 2025 · 12 citations
- I-Card: A Generative AI-Supported Intelligent Design Method Card DeckLiuqing Chen, Wengteng Cheang, Zhaojun Jiang, Yuan Xu et al.CHI 2025 · 11 citations
- SCENIC: A Location-based System to Foster Cognitive Development in Children During Car RidesLiuqing Chen, Yaxuan Song, Ke Lyu, Shuhong Xiao et al.UIST 2025 · 2 citations
Related papers
- BioSpark: Beyond Analogical Inspiration to LLM-augmented TransferHyeonsu B. Kang, David Chuan-En Lin, Yan-Ying Chen, Matthew K. Hong et al.CHI 2025 · 9 citations
- Can LLMs Learn by Teaching for Better Reasoning? A Preliminary StudyXuefei Ning, Zifu Wang, Shiyao Li, Zinan Lin et al.NeurIPS 2024 · 14 citations
- Open-ended Structured Question Assessment with Human-LLM CollaborationFengyan Lin, Yanna Lin, Kai Cao, Zikun Deng et al.CHI 2026 · 1 citation
- ReVisor: A Reflective Design Tool for Instructional Designers to Improve Teacher Training Materials via AI DiscussionsJeongyeon Kim, Miroslav Suzara, John C. MitchellCHI 2026
- Exploring Challenges and Opportunities to Support Designers in Learning to Co-create with AI-based Manufacturing Design ToolsFrederic Gmeiner, Humphrey Yang, Lining Yao, Kenneth Holstein et al.CHI 2023 · 125 citations
