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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. 

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. 

Abstract The research presented in this paper investigated the changes that occur in the prefrontal cortex (PFC) when new ideas are introduced during engineering design. Undergraduate and graduate engineering students (n = 25) were outfitted with a functional near-infrared spectroscopy (fNIRS) headband. Students were asked to design a personal entertainment system while thinking aloud. New ideas were timestamped with the fNIRS data across 48 channels grouped into eight regions within the PFC. The data were preprocessed using temporal derivative distribution repair motion correction, finite impulse response bandpass filter, and the modified beer-lambert law to convert optical density into hemoglobin concentration. Baseline neurocognitive activation and physiological noise were removed. The study found a significant decrease in oxygenated hemoglobin in the left dorsolateral prefrontal cortex and a subregion of the left ventrolateral prefrontal cortex when new ideas were introduced during design. This finding begins to provide a neurocognitive signature of what a new idea looks like as it arises in the brain. This could be used to develop tools and techniques to inhibit this brain region or use this insight to predict when designers will experience a new idea based on their neural activation. 

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. 

Distributed market structures for local, transactive energy trading can be modeled with ecological systems, such as mycorrhizal networks, which have evolved to facilitate interplant carbon exchange in forest ecosystems. However, the complexity of these ecological systems can make it challenging to understand the effect that adopting these models could have on distributed energy systems and the magnitude of associated performance parameters. We therefore simplified and implemented a previously developed blueprint for mycorrhizal energy market models to isolate the effect of the mycorrhizal intervention in allowing buildings to redistribute portions of energy assets on competing local, decentralized marketplaces. Results indicate that the applied mycorrhizal intervention only minimally affects market and building performance indicators—increasing market self-consumption, decreasing market self-sufficiency, and decreasing building weekly savings across all seasonal (winter, fall, summer) and typological (residential, mixed-use) cases when compared to a fixed, retail feed-in-tariff market structure. The work concludes with a discussion of opportunities for further expansion of the proposed mycorrhizal market framework through reinforcement learning as well as limitations and policy recommendations considering emerging aggregated distributed energy resource (DER) access to wholesale energy markets. 

Challenges associated with the design and construction of the built environment are complex. Students need training to help them deal with this complexity and to help them explore and reframe problems early during project planning and design. Concept maps provide a visual representation of complex information and the relationships between this information. The research presented in this paper tested whether priming students to think in systems by asking them to draw concept maps changes how they construct problem statements. In total, 40 engineering students participated in the study. Half were asked to draw a concept map before constructing a problem statement about how to improve mobility systems around campus. The cognitive effort (i.e., time and words) students spent on the task and the number of unique system elements included in their problem statement were measured. Students that received the concept mapping intervention spent significantly more time thinking about the problem, developed longer problem statements, and included more unique elements of systems. These findings suggest using concept mapping can aid students’ conceptualization of complex problems. 

Engineers play an important role in implementing the Sustainable Development Goals defined by the United Nations, which aim to provide a more sustainable environment for future generations. Through design thinking, creativity, and innovation, sustainable engineering solutions can be developed. Future engineers need to acquire skills in their engineering curriculum to feel equipped to address sustainable design challenges in their career. This paper focuses on the impact of perceived design thinking traits and active learning strategies in design courses to increase senior engineering students’ motivation to engage in energy sustainability in their career. A national survey was distributed to senior engineering students in the United States (n = 4364). The survey asked students about their motivation to engage in sustainable design, their perceived design thinking traits (i.e., integrative feedback, collaboration), and if they experienced active learning strategies in design courses (i.e., learning by doing). The results highlight that higher perceived design thinking ability increases senior engineering students’ interests in designing solutions related to energy sustainability. Active learning experiences positively influence senior engineering students’ interests in designing solutions related to energy sustainability. These findings show the importance of teaching design thinking in engineering courses to empower future engineers to address sustainable challenges through design and innovation.