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Computer-aided design (CAD) is a standard design tool used in engineering practice and by students. CAD has become increasingly analytic and inventive in incorporating artificial intelligence (AI) approaches to design, e.g., generative design (GD), to help expand designers' divergent thinking. However, generative design technologies are relatively new, we know little about generative design thinking in students. This research aims to advance our understanding of the relationship between aspects of generative design thinking and traditional design thinking. This study was set in an introductory graphics and design course where student designers used Fusion 360 to optimize a bicycle wheel frame. We collected the following data from the sample: divergent and convergent psychological tests and an open-ended response to a generative design prompt (called the generative design reasoning elicitation problem). A Spearman's rank correlation showed no statistically significant relationship between generative design reasoning and divergent thinking. However, an analysis of variance found a significant difference in generative design reasoning and convergent thinking between groups with moderate GD reasoning and low GD reasoning. This study shows that new computational tools might present the same challenges to beginning designers as conventional tools. Instructors should be aware of informed design practices and encourage students to grow into informed designers by introducing them to new technology, such as generative design.

 

Abstract There are three approaches to studying designers – through their cognitive profile, design behaviors, and design artifacts (e.g., quality). However, past work has rarely considered all three data domains together. Here we introduce and describe a framework for a comprehensive approach to engineering design, and discuss how the insights may benefit engineering design research and education. To demonstrate the proposed framework, we conducted an empirical study with a solar energy system design problem. Forty-six engineering students engaged in a week-long computer-aided design challenge that assessed their design behavior and artifacts, and completed a set of psychological tests to measure cognitive competencies. Using a machine learning approach consisting of k-means, hierarchical, and spectral clustering, designers were grouped by similarities on the psychological tests. Significant differences were revealed between designer groups in their sequential design behavior, suggesting that a designer's cognitive profile is related to how they engage in the design process.