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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. 

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. 

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.