Abstract
In industrial design education, design critique plays a critical role in fostering students’ critical thinking and facilitating iterative design improvements. However, traditional critique methods are often constrained by time limitations, subjectivity bias, the challenge of addressing diverse student needs, and the pressures instructors face from managing multiple responsibilities. As multimodal large language models (MLLMs) become more advanced, there is potential for these tools to support the critique process. However, little research has been done on how instructors perceive AI-powered critique tools in industrial design scenario. This study explores industrial design instructors’ expectations for AI-driven critique tools by evaluating a prototype system, Design Critique AI (DCAI), which uses MLLMs to generate structured feedback. Ten design instructors, as experts, participated in reviewing critiques generated by DCAI through think-aloud protocols and interviews, resulting in 678 coded responses. The findings reveal that while instructors recognize the value of AI in providing structured and accessible feedback for novices, they envision future systems with greater contextual sensitivity, deeper reasoning, and stronger alignment with students’ design intent. Expectations for future AI critique systems include adaptability, pedagogical flexibility, and emotional support, emphasizing the importance of human-AI collaboration in design education.
Keywords
Design critique; Design education; AI-driven tools; Expert review
DOI
https://doi.org/10.21606/iasdr.2025.394
Citation
Li, C., Hu, Q.,and Liu, F.(2025) Design Critique Reimagined: Instructors' Expectations of AI Feedback, in Chang, C.-Y., and Hsu, Y. (eds.), IASDR 2025: Design Next, 02-05 December, Taiwan. https://doi.org/10.21606/iasdr.2025.394
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Conference Track
Track 12 - Design Education
Design Critique Reimagined: Instructors' Expectations of AI Feedback
In industrial design education, design critique plays a critical role in fostering students’ critical thinking and facilitating iterative design improvements. However, traditional critique methods are often constrained by time limitations, subjectivity bias, the challenge of addressing diverse student needs, and the pressures instructors face from managing multiple responsibilities. As multimodal large language models (MLLMs) become more advanced, there is potential for these tools to support the critique process. However, little research has been done on how instructors perceive AI-powered critique tools in industrial design scenario. This study explores industrial design instructors’ expectations for AI-driven critique tools by evaluating a prototype system, Design Critique AI (DCAI), which uses MLLMs to generate structured feedback. Ten design instructors, as experts, participated in reviewing critiques generated by DCAI through think-aloud protocols and interviews, resulting in 678 coded responses. The findings reveal that while instructors recognize the value of AI in providing structured and accessible feedback for novices, they envision future systems with greater contextual sensitivity, deeper reasoning, and stronger alignment with students’ design intent. Expectations for future AI critique systems include adaptability, pedagogical flexibility, and emotional support, emphasizing the importance of human-AI collaboration in design education.