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.

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.