AI as a Third Party: Introducing Language, Authorship, and Code in Foundation Design Education

Abstract

In early architecture education, students often struggle to express ideas clearly. Years of standardized K-12 learning emphasize surface-level responses over critical inquiry, leaving many students unprepared to formulate and communicate conceptual thinking. A significant hurdle experienced by students is the difficulty in using language to translate ideas into diagrams. This challenge becomes an opportunity when paired with generative AI, which can act as an impartial third party, requiring clarity, precision, and iteration to produce a desired result. Rather than treating AI as a shortcut, we framed it as a tool to help students think, process, and revise their ideas critically. In Fall 2024, foundation studio introduced ChatGPT into a series of design exercises grounded in basic design principles. Students began by identifying a conceptual prompt through the pairing of a design element and a design principle, for example, line and symmetry simplified into the single word “balance.” These basic concepts became the foundation for the process of translation. Students wrote step-by-step instructions for the reproduction of their chosen concept. Those instructions were then handed off to a classmate, who used ChatGPT to generate Python code for Rhino. This began a cycle of testing, troubleshooting, and iteration, moving back and forth between the AI and Rhino, between code and diagram. This distance, created through the layering of authorship and digital translation, allowed iteration to grow spontaneously. Students who had never written code before learned to analyze outputs, revise logic, and reflect on the gaps between intention and result. The diagrams they produced served as the first generation of a series of 2D digital explorations. These were then used as the conceptual basis for their final semester projects: the fabrication of an architectural object and the design of a sacred landscape. Weekly pin-ups and peer critiques helped ground these outcomes in conversation, feedback, and revision. What emerged from this process was a new way of introducing both computation and authorship in early design education. By handing off authorship to others, both human and machine, students learned what parts of their process needed to stay constant and what could evolve. More importantly, they began to understand that architectural design is never a solitary act. It is iterative, distributed, and deeply social. Generative tools, when introduced critically and creatively, can expand a student’s capacity to imagine, collaborate, and think beyond the limitations of their previous experience.