This work investigates the application of artificial intelligence (AI) technologies within the framework of the discipline of architectural representation. It challenges the typical AI workflow, which often applies AI to unknown territories resulting in unpredictable, yet too easily accepted, outcomes. The case study of the experiment, partly described here, is the lost Church of Santa Maria Assunta dei Crociferi, in Venice, Italy. Despite the lack of scholarly attention, this site is noted for its historical significance. The experimentation begins by analysing the research made by two architecture historians: Sherman (The Lost Venetian Church of Santa Maria Assunta dei Crociferi: Form, Decoration, Patron-age. Independent Publishing Network, London, [1]) and Galeazzo (Dinamiche di crescita di un margine urbano: l’insula dei Gesuiti a Venezia dalle soglie dell’età moderna alla fine della Repubblica, Università Ca’ Foscari, Venezia, pp. 455–692, [2]), then DALL-E is used to generate images based on the same textual information, adapted to prompts. Finally, the results are evaluated in relation to traditional approaches belonging to the methodologies of history of architecture and architectural drawing. The aim is to observe how the AI model responds to the gaps in visual documentation, without necessarily expecting a precise reconstruction of the church. A key factor impacting this methodology is AI’s real-time evolution, which may shift research parameters. Most of all, this study discusses what the AI-generated images reveal about the databases used to train these models, as the experiment identifies patterns that reflect aspects of architectural knowledge widely available online. The paper investigates the still blurred connections between AI research and architectural practice, approaching both possibilities and limitations, providing architects with critical knowledge and enhancing awareness of their newest instruments and tools.
Representation Advances And CHallenges association