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1. An Empirical Study Measuring the Effects of Generative AI on Early-Stage Engineering Design and Neurocognition

   https://doi.org/10.1115/DETC2025-169255  

   Aruon, Avinash; Shealy, Tripp; Gero, John (August 2025, American Society of Mechanical Engineers)

   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. 

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2. Overcoming Present Bias in Engineering Design and Construction with Future Thinking and Generative AI

   Aruon, Avinash (July 2025, Virginia Tech)

   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. 

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3. Exploration of the Dynamics of Neuro-Cognition During TRIZ

   https://doi.org/10.1115/DETC2021-70412  

   Milovanovic, Julie; Hu, Mo; Shealy, Tripp; Gero, John (August 2021, ASME 2021 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference)

   The Theory of Inventive Problem Solving (TRIZ) method and toolkit provides a well-structured approach to support engineering design with pre-defined steps: interpret and define the problem, search for standard engineering parameters, search for inventive principles to adapt, and generate final solutions. The research presented in this paper explores the neuro-cognitive differences of each of these steps. We measured the neuro-cognitive activation in the prefrontal cortex (PFC) of 30 engineering students. Neuro-cognitive activation was recorded while students completed an engineering design task. The results show a varying activation pattern. When interpreting and defining the problem, higher activation is found in the left PFC, generally associated with goal directed planning and making analytical. Neuro-cognitive activation shifts to the right PFC during the search process, a region usually involved in exploring the problem space. During solution generation more activation occurs in the medial PFC, a region generally related to making associations. The findings offer new insights and evidence explaining the dynamic neuro-cognitive activations when using TRIZ in engineering design. 

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4. Generative Design: Reframing the Role of the Designer in Early-Stage Design Process

   https://doi.org/10.1115/1.4056799  

   Saadi, Jana I.; Yang, Maria C. (April 2023, Journal of Mechanical Design)

   Abstract Generative design tools empowered by recent advancements in artificial intelligence (AI) offer the opportunity for human designers and design tools to collaborate in new, more advanced modes throughout various stages of the product design process to facilitate the creation of higher performing and more complex products. This paper explores how the use of these generative design tools may impact the design process, designer behavior, and overall outcomes. Six in-depth interviews were conducted with practicing and student designers from different disciplines who use commercial generative design tools, detailing the design processes they followed. From a grounded theory-based analysis of the interviews, a provisional process diagram for generative design and its uses in the early-stage design process is proposed. The early stages of defining tool inputs bring about a constraint-driven process in which designers focus on the abstraction of the design problem. Designers will iterate through the inputs to improve both quantitative and qualitative metrics. The learning through iteration allows designers to gain a thorough understanding of the design problem and solution space. This can bring about creative applications of generative design tools in early-stage design to provide guidance for traditionally designed products. 

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5. Exploration of the dynamics of neuro-cognition during TRIZ Paper DETC2021-70412

   Milovanovic, J.; Shealy, T.; Hu, M.; Gero, J. S. (October 2021, ASME-IDETC 2021)

   The Theory of Inventive Problem Solving (TRIZ) method and toolkit provides a well-structured approach to support engineering design with pre-defined steps: interpret and define the problem, search for standard engineering parameters, search for inventive principles to adapt, and generate final solutions. The research presented in this paper explores the neurocognitive differences of each of these steps. We measured the neuro-cognitive activation in the prefrontal cortex (PFC) of 30 engineering students. Neuro-cognitive activation was recorded while students completed an engineering design task. The results show a varying activation pattern. When interpreting and defining the problem, higher activation is found in the left PFC, generally associated with goal directed planning and making analytical judgement when interpreting and defining the problem. Neuro-cognitive activation shifts to the right PFC during the search process, a region usually involved in exploring the problem space. During solution generation more activation occurs in the medial PFC, a region generally related to making associations. The findings offer new insights and evidence explaining the dynamic neuro-cognitive activations when using TRIZ in engineering design. 

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