The Generative Systems: The Computed Environment course at the University of North Carolina Charlotte teaches critical digital design skills within architectural practice.

At the UNC Charlotte School of Architecture, the computed environment is a central theme that runs throughout the curriculum, highlighting computational thinking, parametric modelling and artificial intelligence as transformative approaches to architectural practice.

This emphasis ensures that students not only gain technical skills but also engage critically with emerging tools and methods that are reshaping the discipline.

The school's Design Computation MS programme equips students with advanced expertise and critical perspectives to guide the future of design. Coursework and research span computational geometry, algorithmic design, digital fabrication and generative AI.

Students are encouraged to build custom workflows while examining how new technologies reframe fundamental architectural questions of form, structure and materiality.

Experimental studios form a key part of the programme. In one course, students investigated Gothic morphology as a generative system – analysing cathedral geometries, extracting proportional rules and re-encoding them into parametric models, transforming historical logics into frameworks for contemporary invention.

Research and coursework also focus on automating synthetic dataset creation, advancing AI pipelines for conceptual exploration and integrating machine learning into parametric design.

Together, these efforts prepare students to lead in generative design while advancing the school's mission to unite innovation, research and pedagogy.

School: David R Ravin School of Architecture, University of North Carolina Charlotte
Course: Master of Science in Design Computation
Type: Postgraduate
Location: Charlotte, NC, USA
Course dates: September 2026 to May 2027

This course is no longer accepting applications.

What will I learn during this course?

– Computational techniques, including scripting, parametric modelling and AI-assisted generative workflows
– Methods for creating and curating synthetic datasets for AI research
– Critical and historical inquiry into form, materiality and digital culture
– Advanced research techniques combining historical precedents, generative systems and digital fabrication

What are the requirements?

– The course is open to undergraduate and graduate students across design disciplines
– No prior coding or parametric design experience required
– Regular participation in workshops, critiques and project reviews
– Access to a personal laptop for in-class exercises and assignment

What facilities and resources are available?

– Design studios and fabrication labs equipped with CNC routers, 3D printers, Kuka KR-60 robot and laser cutters
– Access to Rhinoceros, Grasshopper, AI/ML toolkits and GPU servers to support advanced computational workflows
– Resources include shared code repositories, tutorial datasets and template libraries, complemented by technical sessions, guest tutorials and peer-led workshops that foster both skill-building and collaboration

What career prospects can I expect upon graduating?

Graduates are prepared for advanced roles in computational architecture firms and generative design studios, as well as opportunities in research labs or academia combining design and AI.

Many pursue careers in digital fabrication, computational consulting and tech-driven design practices, with the skills to lead innovation in emerging areas of architecture and design.

Who teaches this course?

– Sabri Gokmen, assistant professor of architecture at University of North Carolina Charlotte

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