AI-Driven Parametric Architecture as an Approach for Computational Design Innovation

Artificial intelligence (AI) is increasingly acknowledged as a transformative driver in design processes, yet its integration in architectural education often remains limited to image generation and generic productivity tasks. This study explores the feasibility and perceived usefulness of integrating AI into architectural modeling and 3D parametric workflows through an exploratory, observational workshop evaluation. The article reports on an AI-driven parametric architecture workshop held at Smart and Future Cities Laboratory for Sustainable Urban Solutions [SFCL] designed as a research setting aimed at introducing architects to the integration of artificial intelligence within computational design and situating AI as an augmentative, rather than substitutive, design agent. Over five days, participants engaged in a hands-on, design-thinking workflow using Rhino 8 and Grasshopper 1.0 in combination with AI tools including large language models and 3D translation tools to support prompt writing, concept generation, parametric modeling, and high-quality visualization. Data were collected through direct observation of workshop activities and a post-workshop survey assessing participants’ perceptions of usability, relevance, and attitudinal change across design stages, as well as how their views on AI changed from the early design stages to the final visualization. All survey respondents rated the workshop methodology as practical and relevant, and 66.7% reported an improved understanding of AI’s role within parametric workflows, highlighting AI-assisted prompt scripting and visualization as key learning outcomes. The findings indicate that embedding AI tools within architectural education may lower technical barriers and support students’ engagement with contemporary computational design practice; as an exploratory case study with a small convenience sample, these findings are preliminary and not generalized. It introduces a modern methodological framework for AI-driven parametric design.