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Abstract Understanding team diversity has become essential for modern-day organisations. This study explores the impact of knowledge diversity in design teams through computational simulations. By analysing design space characteristics, we study how diverse teams perform compared to less diverse counterparts. Results reveal that highly diverse teams exhibit increased efficiency, quicker convergence, and larger but sparser design spaces. This work contributes to understanding the impact of knowledge diversity in design teams and sets the stage for future systematic studies of diversity. 

Abstract The think-aloud protocol provides researchers an insight into the designer's mental state, but little is understood about how thinking aloud influences design. The study presented in this paper sets out to measure the cognitive and neurocognitive changes in designers when thinking aloud. Engineering students (n=50) were randomly assigned to the think-aloud or control group. Students were outfitted with a functional near-infrared spectroscopy band. Students were asked to design a personal entertainment system. The think-aloud group spent significantly less time designing. Their design sketches included significantly fewer words. The think-aloud group also required significantly more resources in the left and right dorsolateral prefrontal cortex (DLPFC). The left DLPFC is often recruited for language processing, and the right DLPFC is involved in visual representation and problem-solving. The faster depletion of neurocognitive resources may have contributed to less time designing. Thinking aloud influences design cognition and neurocognition, but these effects are only now becoming apparent. More research and the adoption of neuroscience techniques can help shed light on these differences. 

Engineering design is a continuous and iterative process, where early-stage decisions significantly impact subsequent design outcomes. This study investigates the influence of AIassistance during early stages of design on subsequent design stages and measures the change in both design outcomes and cognitive processing in the brain. Sixty undergraduate engineering students participated in a two-stage design task. Students were first asked to identify design constraints related to the sustainable redevelopment of a site on campus either using human imagination or utilizing generative AI to assist them. Students, in both groups, without the aid of generative AI, then developed conceptual design ideas for redevelopment. The results indicate that the AI-assisted group identified significantly more design constraints (p < 0.05) and subsequently without the aid of AI developed a greater number of design concepts related to environmental sustainability. Brain imaging analysis revealed that AI assistance reduced the neuro-cognitive effort during constraints identification and had a residual effect in reducing neuro-cognitive effort during the concept design phase, particularly in the right frontopolar prefrontal cortex – a region associated with complex, abstract thinking. These findings suggest that AI-assisted design can enhance design efficiency by optimizing reducing cognitive effort and improving early-stage design outcomes. Future research should explore human-AI collaboration strategies to maximize its benefits in engineering design workflows. 

processes used in designing and relate them to the metrics used in psychology for idea creativity, ie, novelty and fluency. Our goal was to test the reliability of psychometric measures of creativity to assess creativity in team design. We studied 19 teams of 3 professional engineers that engaged in a one hour-long design task. Design tasks have a greater ecological validity than single repetitive tasks like the AUT and the RAT. Engaging in a design task involves a wide range of cognitive activities, which contribute to creative ideation and to expanding the design space. This study focused on the relationship between the teams’ design idea creativity and design behaviors during the task. We explored to what extent design collaboration between teammates, design evaluation and the co-evolution of the problem-solution space relate to the psychometric measures of idea creativity. Results suggest no specific trend in the correlation between collaboration and idea creativity as measured by the metrics used in psychology, while more cognitive focus on problemsolution co-evolution negatively correlates with these measures of idea creativity. The paper concludes with potential explanations for this lack of correlation. 

To explore the connection between brain and behavior in engineering design, this study measured the change in neurocognition of engineering students while they developed concept maps. Concept maps help designers organize complex ideas by illustrating components and relationships. Student concept maps were graded using a pre-established scoring method and compared to their neurocognitive activation. Results show significant correlations between performance and neurocognition. Concept map scores were positively correlated with activation in students’ prefrontal cortex. A prominent sub-region was the right dorsolateral prefrontal cortex (DLPFC), which is generally associated with divergent thinking and cognitive flexibility. Student scores were negatively correlated with measures of brain network density. The findings suggest a possible neurocognitive mechanism for better performance. More research is needed to connect brain activation to the cognitive activi-ies that occur when designing but these results provide new evidence for the brain functions that support the development of complex ideas during design. 

In this paper, we explored changes in brain states over time while designers were generating concepts. Participants either used morphological analysis or TRIZ to develop a design concept for two design tasks. While designing, participants’ brain activation in their prefrontal cortex (PFC) was monitored with a functional Near Infrared Spectroscopy machine. To identify variation in brain states, we analyzed changes in brain networks. Using k-mean clustering to classify brain networks for each task revealed four brain network patterns. While using morphological analysis, the occurrence of each pattern was similar along the design steps. For TRIZ, some brain states dominated depending on the design step. Drain states changes suggests that designers alternate engaging certain subregions of the PFC. This approach to studying brain behavior provides a more granular understanding of the evolution of design brain states over time. Findings add to the growing body of research exploring design neurocognition.