This study examines the structured integration of Artificial Intelligence (AI) in architectural education through a bifocal pedagogical framework across two institutions, aimed at developing a replicable workflow that merges data-driven decision making with the generative potential of text-to-image prompting. The workflow incorporates Large Language Models (LLMs) and text-to-image synthesizers—particularly Stable Diffusion via ComfyUI—for iterative stylistic and material exploration, alongside environmental analysis with LadyBugTools to emphasize sustainability through microclimate optimization and building performance. A total of 37 students participated in parallel trajectories: an intensive one-week workshop and a semester-long CAAD course. Students independently selected sites and programs, though general contexts were provided. Outcomes were assessed using five indicators: conceptual maturity, adaptability, contextualization, critical AI integration, and generativity. Findings highlight that exposure duration significantly shapes students’ perception of AI—not as a replacement for design agency but as an augmentative logic—making temporal structuring pivotal in framing machine agency pedagogically.
Synaptic Synergies: A dual perspective on AI integration in architectural education and CAAD pedagogy
Fulvio Papadhopulli
Co-primo
;
2025
Abstract
This study examines the structured integration of Artificial Intelligence (AI) in architectural education through a bifocal pedagogical framework across two institutions, aimed at developing a replicable workflow that merges data-driven decision making with the generative potential of text-to-image prompting. The workflow incorporates Large Language Models (LLMs) and text-to-image synthesizers—particularly Stable Diffusion via ComfyUI—for iterative stylistic and material exploration, alongside environmental analysis with LadyBugTools to emphasize sustainability through microclimate optimization and building performance. A total of 37 students participated in parallel trajectories: an intensive one-week workshop and a semester-long CAAD course. Students independently selected sites and programs, though general contexts were provided. Outcomes were assessed using five indicators: conceptual maturity, adaptability, contextualization, critical AI integration, and generativity. Findings highlight that exposure duration significantly shapes students’ perception of AI—not as a replacement for design agency but as an augmentative logic—making temporal structuring pivotal in framing machine agency pedagogically.I documenti in SFERA sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
