Enhancing Parametric Design Education Through Rhinoceros/Grasshopper: Visual Perception Principles, Student Learning, and Future Integration with AI

The paper presents a pedagogical workflow integrating parametric design, computer vision, and AI text-to-image generation within architectural education. Using Rhinoceros/Grasshopper, Python-based computer vision tools, and Midjourney, the research proposes a methodology that teaches students to interpret architectural form through visual perception principles such as vectorial displacement, edge structures, surface discretization, and mass identification. The study combines parametric redrawing exercises with image segmentation, edge detection, and AI prompt design to help students critically understand generative AI processes rather than using them as opaque black-box systems. The workflow is intended to support conscious AI-assisted architectural design education by linking geometric reasoning, computational analysis, and generative image production.