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.

Shared autonomous vehicles (SAVs) will be an important force to in reshaping urban morphology. The high operation rate and sharing degree of SAV are considered to result in a great reduction in parking area in future cities. Parking space is now a huge and widely distributed urban stock space type, which is bound to become a major challenge and opportunity for sustainable urban renewal in the digital era. Based on the SAV scenario, this paper reviews the current research on the sustainable renewal of urban public parking spaces, and proposes the four key issues involved: how much to renew (i.e., demand forecast analysis), when to renew (i.e., update time series evaluation), what to renew (i.e., function replacement decision) and how to update (i.e., design empirical research). Furthermore, it puts forward a preliminary idea on, and constructs a research framework for, the sustainable renewal methods of parking space under the SAV scenario. Finally, the theoretical, practical and policy implications of the research on sustainable renewal methods of urban public parking space are discussed. It will have great reference value for the redevelopment and reuse of the urban space types including fragmented, widely distributed and large-scale.

Despite emerging empirical findings and computational tools that extend city image research to include social dimensions beyond visual perception, methodologies for effectively identifying and analyzing the relationships between the five city image elements remain underdeveloped. This paper addresses the gap by proposing a big data-driven method, integrating Weibo check-in data, Baidu Map POI, and ArcGIS algorithms to identify city image elements and further reveal a city’s overall morphological characteristics. Based on different modes of observation, city image elements are categorized as spatial descriptors (“districts”, “nodes”, and “paths”) and symbolic descriptors (“landmarks” and “edges”). Taking Hangzhou as a case study, the findings show a strong alignment between urban development achievements and the distribution patterns of city image elements. “Districts” and “landmarks” stand out as the most prominent, reflecting functional zoning and urban maturity, while “nodes” emphasize the city’s polycentric structure. “Paths” offer clear insight into the city’s development trajectory, while “edges” appear to be legible only in relation to other elements. This method innovates cognitive mapping by merging real-world perceptions with algorithmic precision, offering a valuable tool for understanding urban morphology, monitoring development changes, and fostering participatory urban design.

Based on the mapping of nine historical maps of the ancient capital of Suzhou from 1880 to 1949, this paper examines the early spatial modernization of Suzhou in the late-Qing and Republican eras. The mapping is interpreted as four periods to provide a coherent narrative for the spatial transformation of Suzhou, following a discussion of its historico-geographical features and morphological influences that define the course of the city’s modernity by extracting three key components: the modern road network, public building utilizations of significant modernity, and the growth and fringe of built-up areas. To do so the paper draws upon current debates on the feasibility of urban morphological study of Chinese cities in early-modern times, particularly the idea that applications of typical urban morphological approaches are more limited than those of comparable studies in Europe, to provide a new approach to observe the morphological features and spatial cognition of Chinese traditional cities.

Integrating generative artificial intelligence into architectural education raises a question not only of whether AI helps, but of how its use should be structured. This paper empirically examines the AI Sparring model—a dialogic, critical approach to student–AI interaction—through a workshop in which architecture students designed a pavilion in a fortress. The three self-selected groups differed in the role AI played in the formation of the concept, not in its final presentation: Group A developed concepts traditionally, through sketches and physical working models, with AI serving only as a rendering tool at the end; Group B used AI directly as a generative tool; and Group C worked through structured critical dialogue with AI. The works were independently rated by three evaluators from three countries, alongside keyword analysis and assessment of reflective depth and theoretical references. Group C produced the most consistent concept quality, with no weak works, yet the single highest-rated work came from the traditional group—Sparring raises the “floor” rather than the “ceiling” of quality. At the same time, Group C reported the lowest sense of authorship, and qualitative analysis revealed “simulated depth”: theoretical references cited without genuine engagement. Because of the small, self-selected samples, the findings are read as exploratory and hypothesis-generating. The paper sheds light on the limits of reflective AI pedagogy and proposes concrete revisions to the model.