Generative Artificial Intelligence (Generative AI) is a collection of AI technologies that can generate new information such as texts and images. With its strong capabilities, Generative AI has been actively studied in creative design processes. However, limited studies have explored the roles of humans and Generative AI in conceptual design processes, which leaves a gap for human–AI collaboration investigation. To address this gap, this study attempts to uncover the contributions of different Generative AI technologies in assisting humans in the conceptual design process. Novice designers were recruited to complete two design tasks in the condition of with or without the assistance of Generative AI. The results revealed that Generative AI primarily assists humans in the problem definition and idea generation stages, while the idea selection and evaluation stage remains predominantly human-led. Additionally, with the assistance of Generative AI, the idea selection and evaluation stages were further enhanced. Based on the findings, we discussed the role of Generative AI in human–AI collaboration and the implications for enhancing future conceptual design support with Generative AI’s assistance.

There are many facets to creativity, and the topic has a profound impact on society. Substantial and sustained study on creativity has been undertaken, and much is now known about the fundamentals and how creativity can be augmented. To draw these elements together, a framework was developed called the creativity diamond, formulated on the basis of reviews of prior work, as well as the consideration of 20 PhD studies on the topics of creativity, design, innovation, and product development. The framework embodies the principles that quantity of ideas breeds quality through selection, and that a range of creativity tools can provoke additional ideas to augment our innate creativity. The creativity diamond proposed is a tool consisting of a divergent phase associated with the development of many distinctive ideas and a convergent phase associated with the refinement of ideas. The creativity diamond framework can be used to prompt and help select which tool or approach to use in a creative environment for innovative tasks. The framework has now been used by many students and professionals in diverse contexts.

Introduction: Neurotechnology approaches, such as electroencephalography (EEG), can aid understanding of the cognitive processes behind creativity.Methods: To identify and compare the EEG characteristics of creativity-related cognitive factors (remote association, common association, combination, recall, and retrieval), 30 participants were recruited to conduct an EEG induction study.Results: From the event-related potential (ERP) results and spectral analysis, the study supports that creativity is related to the frontal lobe areas of the brain and common association is an unconscious process.Discussion: The results help explain why some creativity-related cognitive factors are involved either more or less readily than others in the creative design process from workload aspects. This study identifies the part of the brain that is involved in the combination cognitive factor and detects the ERP results on cognitive factors. This study can be used by designers and researchers to further understand the cognitive processes of creativity.

This study aims to take higher-education students as examples to understand and compare artistic and engineering mindsets in creative processes using EEG. Fifteen Master of Fine Arts (MFA) visual arts and fifteen Master of Engineering (MEng) design engineering students were recruited and asked to complete alternative uses tasks wearing an EEG headset. The results revealed that (1) the engineering-mindset students responded to creative ideas faster than artistic-mindset students. (2) Although in creative processes both artistic- and engineering-mindset students showed Theta, Alpha, and Beta wave activity, the active brain areas are slightly different. The active brain areas of artistic-mindset students in creative processes are mainly in the frontal and occipital lobes; while the whole brain (frontal, oriental, temporal, and occipital lobes) was active in creative processes of engineering-mindset students. (3) During the whole creative process, the brain active level of artistic-mindset students was higher than that of engineering-mindset students. The results of this study fills gaps in existing research where only active brain areas and band waves were compared between artistic- and engineering-mindset students in creative processes. For quick thinking in terms of fluency of generating creative ideas, engineering students have an advantage in comparison to those from the visual arts. Also, the study provided more evidence that mindset can affect the active levels of the brain areas. Finally, this study provides educators with more insights on how to stimulate students’ creative ability.

The reason why people have different creativity quality levels may depend on their different performances relating to other cognitive factors that are important for creativity. This study was designed to identify the performance of three cognitive factors (recall, association, and combination) that a designer may use in a creative process and then identify how the differing performance for these cognitive factors will affect creativity quality levels. Seventy-one participants were recruited to undertake a design task and complete a semi-structured interview. The results indicate that, in a creative design process, similar performances in recall, association, and combination can result in differences in creativity quality level.

Background: Pre-retrofit auditing and pre-demolition auditing (PRA/PDA) are important in material reuse, waste reduction, and regulatory compliance in the building sector. An emphasis on sustainable construction practices has led to a higher requirement for PRA/PDA. However, traditional auditing processes demand substantial time and manual effort and are more easily to create human errors. As a developing technology, artificial intelligence (AI) can potentially assist PRA/PDA processes. Objectives: This scoping review aims to review the potential of AI in assisting each sub-stage of PRA/PDA processes. Eligibility Criteria and Sources of Evidence: Included sources were English-language articles, books, and conference papers published before 31 March 2025, available electronically, and focused on AI applications in PRA/PDA or related sub-processes involving structured elements of buildings. Databases searched included ScienceDirect, IEEE Xplorer, Google Scholar, Scopus, Elsevier, and Springer. Results: The review indicates that although AI has the potential to be applied across multiple PRA/PDA sub-stages, actual application is still limited. AI integration has been most prevalent in floor plan recognition and material detection, where deep learning and computer vision models achieved notable accuracies. However, other sub-stages—such as operation and maintenance document analysis, object detection, volume estimation, and automated report generation—remain underexplored, with no PRA/PDA specific AI models identified. These gaps highlight the uneven distribution of AI adoption, with performance varying greatly depending on data quality, available domain-specific datasets, and the complexity of integration into existing workflows. Conclusions: Out of multiple PRA/PDA sub-stages, AI integration was focused on floor plan recognition and material detection, with deep learning and computer vision models achieving over 90% accuracy. Other stages such as operation and maintenance document analysis, object detection, volume estimation, and report writing, had little to no dedicated AI research. Therefore, although AI demonstrates strong potential in PRA/PDA, particularly for floor plan and material analysis, broader adoption is limited. Future research should target multimodal AI development, real-time deployment, and standardized benchmarking to improve automation and accuracy across all PRA/PDA stages.