Enhancing Inquiry-Based Learning in Human Factors Engineering with Generative AI: A Case Study in Industrial Design Education

This study examines the influence of generative artificial intelligence (AI) on inquiry-based learning (IBL) and design thinking in industrial design education. While previous research has extensively explored creativity and higher-order thinking skills in design thinking, a systematic analysis of the characteristics, sources, and motivations behind questioning—particularly in generative AI applications—remains lacking. Specifically, the impact of question sources, whether derived from personal knowledge, AI-generated inspiration, on students’ learning behaviors and design processes has not been thoroughly investigated. To address this gap, this study conducted a workshop involving 54 students who applied human factors engineering principles to identify deficiencies in existing products and formulate design questions using IBL. The findings indicate that generative AI primarily supports students during the “Ideate” phase, where it is more active, while students’ questions are concentrated in the “Define” and “Empathize” stages. This suggests that AI assistance may help students break fixed thinking patterns and encourage a greater variety of questions. Additionally, the number of sketches produced positively correlates with students’ scores on the IBL assessment rubric. However, neither the number of questions generated—by humans or AI—nor the AI dependency index substantially impacts IBL performance. These results provide empirical evidence to refine design thinking in industrial design education and offer practical insights for effectively integrating generative AI into educational practices.