Browsing by Author "Carr-Harris, Philip"

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    Eleven Exercises in the Art of Augmented Design: Reflections on the Instrumentality of Generative AI in Navigating the Open-Closed Spectrum of Architectural Drawing.
    (University of Waterloo, 2024-10-17) Carr-Harris, Philip
    This thesis, inspired by Marco Frascari's eleven exercises1, explores the instrumentality of image-based generative AI in the context of architectural drawing. As image-based generative AI tools gain popularity, this research explores a series of 11 exercises translated from Frascari’s work into the age of generative AI. Primary research questions: RQ1: What is the historical and contemporary role of architectural drawing in education and practice? RQ2: How does generative AI disrupt traditional architectural drawing processes? RQ:3 How can generative AI be instrumentalized to empower architects? To address these questions, this thesis proposes and applies the Open-Closed Drawing Framework, which positions architectural drawings on a continuum from open, ambiguous sketches to closed, precise drawings. This framework is instrumental in understanding the varying degrees of ambiguity and precision in architectural representations and their potential augmentation through AI collaboration. A key component of this research is the development of a set of eleven exercises for engaging with generative AI in the production of architectural drawings. By beginning with Marco Frascari’s eleven exercises, and adapting them to engage with image-based generative AI, the translation between the two becomes an exciting challenge in its own right, underscoring the differences between traditional and generative creative processes. These eleven, translated exercises lean on the Open-Closed Drawing Framework to organize architectural drawings in relation to each other. By providing a structured framework and exploring a series of exercises, the thesis contributes to the ongoing discourse on AI's role in architectural drawing. It offers a nuanced perspective that views generative AI as a catalyst for innovation rather than a substitute for human creativity. This research invites architects to engage with the future of architectural drawing through a series of exercises exploring image-based generative AI.