This case study examines the integration of artificial intelligence (AI) into undergraduate architectural education through a 2024–25 core studio teaching experiment at Zhejiang University. A dual-module framework was implemented, comprising a 20 h AI skills training module and in-class ethics discussions, without altering the existing studio structure. The AI skills module introduced deep learning models, LLMs, AIGC image models, LoRA fine-tuning, and ComfyUI, supported by a dedicated technical instructor. Student feedback indicated phase-dependent and tool-sensitive engagement, and students expressed a preference for embedded ethical discussion within the design studio rather than separate formal instruction. The experiment demonstrated that modular AI education is both scalable and practical, highlighting the importance of phase-sensitive guidance, balanced technical and ethical framing, and institutional support such as cloud platforms and research-based AI tools. The integration enhanced students’ digital adaptability and strategic thinking while prompting reflection on issues such as authorship, algorithmic bias, and accountability in human–AI collaboration. These findings offer a replicable model for AI-integrated design pedagogy that balances technical training with critical awareness.

This study focuses on the topic of “Urban Village Renovation Design” under the complex and diversified social needs in the third year of the architecture undergraduate program at Zhejiang University, China. Based on the theory and method of “situational teaching,” this study proposes a teaching framework integrating the investigation and cognition of nonmorphological elements, such as historical background, economic structure, social structure, public service, and human needs. The study aims to reveal the analysis and response of site investigation and architectural programming to social needs in the realistic context, and take nonmorphological elements as one of the important factors to promote the rationality and authenticity of architectural design, standardize the teaching process in the form of the teaching framework, and realize the teaching goal of solving social needs by design. Qualitative analyses are used to evaluate whether the proposed teaching framework achieves the expected teaching effects according to Bloom’s Taxonomy. We then use the Kirkpatrick model to quantitatively evaluate the specific effects of the framework, and the differences in the positive effects of nonmorphological elements on teaching are explored. In addition, regression analysis is used to discuss ways of obtaining nonmorphological elements. The results show that the teaching framework is a feasible method to improve students’ understanding of social problems and implement reasonable architectural programming that integrates nonmorphological elements in the architectural design course. To some extent, this teaching framework addresses the neglect of nonmorphological elements in traditional Chinese architectural design teaching, and forms an experience-based teaching methodology that can be used to guide architectural design teaching on other topics. This study is helpful in exploring the value and potential of nonmorphological elements in architectural design and provides a reference for college teachers engaged in architectural programming and design teaching.

As sustainable architecture is increasingly emphasizing material reuse, this study proposes a novel, interactive workflow that integrates robotic arms and artificial intelligence to transform waste materials from architectural models into creative design components. Unlike existing recycling efforts, which focus on the construction phase, this research uniquely targeted discarded architectural model materials, particularly polystyrene foam, that are often overlooked, despite their environmental impact. The workflow combined computer vision and machine learning, utilizing the YOLOv5 model, which achieved a classification accuracy exceeding 83% for the polygon, rectangle, and circle categories, demonstrating a superior recognition performance. Robotic sorting demonstrated the ability to process up to six foam blocks per minute under controlled conditions. By integrating Stable Diffusion, we further generated speculative architectural renderings, enhancing creativity and design exploration. Participant testing revealed that human interaction reduced stacking errors by 57% and significantly improved user satisfaction. Moreover, human–robot collaboration not only corrected robotic errors, but also fostered innovative and collaborative solutions, demonstrating the system’s potential as a versatile tool for education and industry while promoting sustainability in design. Thus, this workflow offers a scalable approach to creative material reuse, promoting sustainable practices from the model-making stage of architectural design. While these initial results are promising, further research is needed to adapt this technique for larger-scale construction materials, addressing real-world constraints and broadening its applicability.

Exploring the causes of the concave curved form of the roofs in traditional Chinese architecture is key to understanding its unique esthetics and structural logic. Regarding its causes, the academic community offers various explanations, including esthetics and function, but research that delves deeply into specific construction techniques and material limitations and systematically explains how they lead to the precise roof forms is relatively insufficient, which limits our comprehensive understanding of the deep generative logic of this unique form. This study aimed to bridge this gap by systematically exploring the causes of the concave curved form of roofs in traditional Chinese architecture (such as flying eaves, upturned corners, and Ju zhe) from the perspective of construction technology. Through a systematic review of historical literature (especially Yingzao fashi (Treatise on Architectural Methods)); the empirical investigation and analysis of typical architectural examples; detailed research on the structural practices, material properties (especially the creep behavior of timber), and construction techniques of key timber components such as flying rafters, hip rafters, and rafters; and mechanical principles and computational simulation, this study found that the concave curved forms of different parts of the roof, such as the eaves (flying rafters), corners (corner upturn), and main body (Ju zhe), are not purely esthetic choices but are, to a large extent, technical responses or inevitable results stemming from objective construction constraints of the time, including limitations on timber length, component connection methods, structural load distribution, and long-term deformation. Based on these findings, this study proposes the concept of “Passive Form” to summarize this form-generation mechanism, emphasizing that architectural forms are not solely determined by subjective will but are rooted in the adaptation and transformation of real constraints in construction, providing a technical perspective rooted in practice for understanding the forms of Chinese traditional architecture.

An important goal in the reform of architectural design education is to instruct students in ways of acquiring relevant site information quickly and efficiently during a design project, and then integrating that information into their architectural designs. This study focuses on a teaching experiment conducted within the “Urban Village Renovation Design” course for third-year undergraduates at Zhejiang University. This study aims to improve teaching efficiency by combining active and passive information acquisition methods during the site information acquisition stage. A teaching experiment on “Urban Village Renovation Design” was conducted with third-year undergraduates at Zhejiang University, comparing two experimental groups based on whether the teacher provides site information reports (i.e., passive information acquisition). The study explores efficient methods for acquiring different types of site information in architectural design teaching and develops a matching framework. It evaluates the impact of active vs. passive methods on students’ cognitive levels, using Bloom’s taxonomy, and quantitatively tests cognitive efficiency differences through the ROI model. Results show that combining both methods yields the highest teaching efficiency, with specific types of information corresponding to effective active or passive acquisition methods. This study explores which research methods can yield beneficial site information more efficiently and clarifies the role of previously overlooked passive information acquisition methods in site cognition, providing theoretical support for the design of teaching plans during the research phase. From a practical standpoint, it is suggested that instructors provide certain site information directly rather than have students acquire it independently, to shorten the research phase of teaching and simultaneously enhance site cognition efficiency.