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.
The Role of Drawing in Data Analysis and Data Representation
This paper discusses the role of Drawing in representing and designing new epistemological models during the current AI spread, especially in relation to new advanced tools available to scientists. The accelerated development of technologies for data visualization and immersive and augmented expe-riences consolidates shared workflows, while criticism and genealogies of such tools are too often left aside. Therefore, scholars have not only to develop new technological tools and applied meth-odologies but also to work on shared sets of theoretical concepts that are necessary foundations to designed contents. The history of representation – especially maps on one side, perspective on the other – provides most of the necessary common ground, which is much needed if we choose that it is still worth governing AIs as much as we can, instead of abandoning ourselves to ‘the end of theory’, which would imply the end of any meaningful scientific drawing made by and for humans.