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Abstract Engineering design is a continuous and iterative process, where early-stage decisions significantly impact subsequent design outcomes. This study investigates the influence of AI-assistance 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. 

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. 

Present bias—the tendency to favor immediate gains over long-term benefits—can negatively affect design decisions in construction engineering. Designers often prioritize short-term economic gains that compromises the resilience of the asset, leading to increased cost of remediation in the future. This dissertation explores how mental visualization through future thinking and the use of generative AI tools can help reduce present bias during early-stage design tasks. Three experimental conditions were tested: present thinking (control), future thinking, and AI-assisted future thinking. Civil engineering students (n = 90) participated in constraints identification and concept design tasks for a campus redevelopment project, while their verbal responses and brain activity were recorded. Functional near-infrared spectroscopy (fNIRS) was used to measure cognitive load. To analyze design narrative, qualitative coding and natural language processing (NLP) techniques such as semantic similarity and text network analysis were used. Results show that future thinking and AI assistance improved the quality and future orientation of design outputs. The AI-assisted group identified more climate-related risks, demonstrated higher alignment with futureproofing concepts, and showed more coherent design narratives. These improvements were achieved with reduced cognitive load. Notably, the influence of AI assistance extended beyond the phase in which it was used and enhanced performance in subsequent design stage. The findings support the role of AI as a cognitive support tool that can enhance design thinking, reduce cognitive load, and lead to more resilient and sustainable design outcomes in construction engineering. 

This research paper examines the patterns of inter-brain synchrony among engineering student teams and the relationship between inter-brain synchrony and team cooperation and performance. A pilot study was conducted with eight two-person teams of fourth-year undergraduate civil engineering students. Three collaborative design and build tasks were assigned to each team. Two independent raters carried out the behavioral analysis, scoring team cooperation. Each team member wore a functional near-infrared spectroscopy (fNIRS) device to measure inter-brain synchrony during the tasks. The results showed that inter-brain synchrony occurred during the team task, but the patterns varied between groups and tasks. Elevated levels of inter-brain synchrony were observed in the left ventrolateral prefrontal cortex (VLPFC) and left dorsolateral prefrontal cortex (DLPFC). The left VLPFC and left DLPFC are often associated with cognitive processes such as problem-solving, working memory, decision-making, and coordinated verbal exchange. Inter-brain synchrony was positively correlated with task performance and cooperation when teams were asked to design and build a structure given limited time and money but negatively correlated with cooperation and performance on other more open-ended design sketching tasks. The study’s findings suggest that inter-brain synchrony exists when engineering students work together as a team, but the results are inconsistent between task types. Inter-brain synchrony could be a useful metric for measuring team cooperation and performance, particularly in tasks that require coordinated verbal exchange, problemsolving, and decision-making. However, the study’s small sample size limits the generalizability of the results. Future studies with a larger sample size and more diverse groups of engineers are needed to validate the findings and explore their implications further. 

Ideation is a key phase in engineering design and brainstorming is an established method for ideation. A limitation of the brainstorming process is idea production tends to peak at the beginning and quickly decreases with time. In this exploratory study, we tested an innovative technique to sustain ideation by providing designers feedback about their neurocognition. We used a neuroimaging technique (fNIRS) to monitor students’ neurocognitive activations during a brainstorming task. Half received real-time feedback about their neurocognitive activation in their prefrontal cortex, a brain region associated with working memory and cognitive flexibility. Students who received the neurocognitive feedback maintained higher cortical activation and longer sustained peak activation. Students receiving the neurocognitive feedback demonstrated a higher percentage of right-hemispheric dominance, a region associated to creative processing, compared to the students without neurocognitive feedback. The increase in right-hemispheric dominance positively correlated with an increase in the number of solutions during concept generation and a higher design idea fluency. These results demonstrate the prospective use of neurocognitive feedback to sustain the cognitive activations necessary for idea generation during brainstorming. Future research should explore the effect of neurocognitive feedback with a more robust sample of designers and compare neurocognitive feedback with other types of interventions to sustain ideation.