This article originally appeared on Archinect and has been slightly modified for the Chaos blog.
How should architecture schools teach artificial intelligence to students? At Western Michigan University, one studio offers a case study in how AI tools can be woven into a familiar design process to expand the horizon of possibilities in the eyes of students, encourage them to advance their modeling skills, and build a competitive portfolio for use beyond graduation.
In a recent conversation, faculty member Dustin Altschul offered insights on how he has integrated the AI-powered visualization tool Veras into his design studio.
Teaching AI in the studio
As AI advances, it will offer both opportunities and challenges for the architecture profession. The same can be said for architecture schools tasked with equipping students for the future of practice. How do faculties account for the fast pace of innovation in AI when structuring years-long curricula? How can students from architectural backgrounds be introduced to AI through media and methods that feel relevant to their future careers? How can educators ensure that students develop an individual design process while also capitalizing on AI tools that show potential to transform that process?
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At Western Michigan University’s Richmond Institute for Design and Innovation, faculty member Dustin Altschul is using Veras to introduce students to the potential of the technology. In Altschul’s courses, Veras is positioned as a concept-phase tool in addition to helping students develop the modeling and visualization skills needed for graduate portfolios.
“We’re using Veras for a bunch of different things,” Altschul said in a recent conversation. “The first project I started with was in the Design Communication 5 course that's focused on photoreal rendering and animation, and pushing the limits of narrative. AI gives us that early concept to begin to work from.”
AI explorations by student Kayla Swope. Image courtesy of Western Michigan University’s Richmond Institute for Design and Innovation
AI as concept exploration
In Altschul’s studio, Veras is introduced not as a tool for creating final renders but as a fast generator of reference material that can inform later modeling and visualization decisions. In one early project, for example, students were asked to design habitation pods and sci-fi environments, chosen specifically to challenge preconceptions.
For Altschul, Veras expanded the range of forms and atmospheres students explored. “The applications are primarily rooted in look development and concept exploration,” Altschul explains. “The use of Veras basically gave them very complicated geometry they wouldn't have normally arrived at. It gave them very rich materials, very much like the abstract worlds they were designing for.”
Importantly, the AI results were not treated as the end of the assignment. Instead, they became prompts for deeper technical and design work. “Now that we’re working with that, we’re going to learn how to actually turn that into a visualization, or how to create those lighting effects,” Altschul added. “The initial point was a deep AI study. Every student was responsible for creating around a hundred AI images within a week's time using Veras and then noting how they manipulated the geometry, how that would lend to different visual outcomes, and then developing the skills around prompting and the basics of text-to-image AI generation.”
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AI explorations by student Sarah Fuchsgruber. Image courtesy of Western Michigan University’s Richmond Institute for Design and Innovation
Throughout the process of generating AI concepts, the goal of asking for a large quantity of material was not about repetition but range. Altschul encouraged students to be as broad as possible before beginning to narrow down concepts that appealed to them. For Altschul, the exercise encouraged students to discover conceptual edges and risks that may not have otherwise been discovered.
“I saw some really beautiful looking things,” Altschul recalled. “One student wanted to make a commentary about waste. She actually used Veras to generate a landfill that contained a TV on which people are living on inside the landfill. That was surprising and somewhat different.”
The use of AI as a vehicle to broaden the students’ ambitions continued as the studio moved from designing abstract worlds to designing buildings. By producing unexpected, evocative concepts through AI, Altschul noted that students become inspired to improve their digital modelling skills to capture, develop, and deliver on the promising beginnings.
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“I use the AI studies as a way to get them excited about a complicated concept, before they have to work through the digital process of actually modeling it,” Altschul explains. “If they were left to their own devices, they wouldn't have gotten to this level of conceptual complexity. But after they generated this reference material, they felt more confident about pursuing it through the process of ‘Okay, I've got to learn a lot about Rhino, but I'm willing to do it because I got the concept now.’ If they were just sketching by hand, I don't think the geometry would have been as rich in some of the designs.”
2089 by student Natalie Moroney. Image courtesy of Western Michigan University’s Richmond Institute for Design and Innovation
2089 by student Natalie Moroney. Image courtesy of Western Michigan University’s Richmond Institute for Design and Innovation
The student relationship with AI
The students in Altschul’s course were relatively inexperienced with AI. While students had previously used ChatGPT for researching information, Altschul’s course was the first time they engaged with AI-driven text-to-image generation. Meanwhile, while the students had basic skills in SketchUp and Rhino, developing ideas beyond basic geometries in Veras challenged them to grow their digital modelling skills.
Despite being introduced to a new paradigm through AI, the student experience in Altschul’s studio still retains many of the fundamental properties of a traditional design process. Like any other project, ideas began in a broad, abstract form before being distilled down to a defined, developed scheme through a series of tests, reflections, and iterations. Likewise, students were still required to work across scales, zooming in and out on individual aspects of the design, such as interior versus exterior approaches, rather than asking AI to generate a comprehensive design in one single move.
“Some students felt like AI was a very natural extension of how they wanted to work; then there were a few who felt it was water and oil for a few days,” Altschul recalled. “But we got through it.”
While AI effectively introduces another creative actor into the students’ design process, Altschul also saw an opportunity to use the addition of AI as a means of teaching students the familiar architectural skill of judging and evaluating work. Presented with a mountain of potential avenues and design suggestions through AI, students were encouraged to view the material as references rather than instructions, and to use their own design sensibilities to determine the next step.
“I look at it in the frame of precedents like clipping things out in magazines or even Pinterest,” Altschul explained. “Developing that kind of body of reference material has always been a part of the practice of architecture and interior design for centuries upon centuries.”
“I think AI is way more inspirational, but then there’s also a side benefit, as it causes my students to be way more specific with their vocabulary,” Altschul added. “Now they ask, ‘How do I get these prompts?’ And I say, ‘Well, you've got to expand your vocabulary. You've got to read. You've got to use precise design language.’ In doing so, they're able to also present their projects better, because they've had exercise around what needs to go into the prompt, and they're able to talk with an expanded vocabulary of what their concept is about.”
Solapis Pod by student Abby Naugle. Image courtesy of Western Michigan University’s Richmond Institute for Design and Innovation
Building a strong portfolio for future careers
When considering how to introduce AI to students, Altschul’s decision to introduce the technology in the third year of the degree is driven by a need to first allow students to develop digital modelling skills and a personal design judgement. “We don't want all of our students' judgment to be purely based upon these images but more like being a human of the world and understanding culture and society and history,” Altschul notes.
Upon graduation, however, Altschul sees several benefits in having introduced students to responsible AI tools integrated into their design workflow. In addition to strengthening the students’ portfolios through a large, engaging design process, Altschul notes an increasing demand from employers for students to be familiar with AI. Over the next decade, meanwhile, Altschul sees promise in his graduates’ ability to deploy AI in practice.
“A lot of things that happen in the built environment come down to the effort of how long it is going to take us to model it or how long it is going to take to build it,” Altschul explained. “I’m hopeful that AI will begin to introduce efficiencies, whether it's around us all of a sudden, having a robot construct the buildings. Or we can actually move through iteration at a much quicker rate. I'm hopeful that's going to result in just simply better-designed buildings, because there'll be the ability and time to provide that more detailed focus to the things that we truly care about as humans.”
“It is the result of crossing thresholds,” Altschul concluded. “I don't want to say AI is replacing the human imagination, but it's extending the human imagination. As we begin to see more complicated forms, richer material palettes, and interesting light effects realized in a visual, all of a sudden, the architecture feels more tangible, more achievable. If we see a picture like that, it can inspire us to say: Wow, we can design like that!”