Today’s graduates are entering the workforce where familiarity with AI systems is not optional – it is expected, says OCAD University Professor Alexander Manu.
“AI tools have created a new condition for design practice. These systems are not simply accelerating workflows; they are reshaping the very grammar of ideation,” says Manu.
He came to this realization after conducting research for his book, Transcending Imagination: Artificial Intelligence and the Future of Creativity, and after working extensively with thousands of generated images in Midjourney.
His decision to develop the specific course, Generative AI Futures, as well as incorporate Generative AI (GAI) in other courses is rooted in a longer historical awareness of the impact of technological advances.
“Since the early 1980s, I have witnessed successive technological inflections. The introduction of computers, followed by computer-aided design and manufacturing, marked a point of no return for the discipline. Later, advanced visualization software such as Alias further expanded the designer’s capacity to render and simulate. Each of these moments required adaptation, not as a matter of preference, but as a matter of relevance,” explains Manu.
“To resist such shifts is to risk preparing students for a future that no longer exists. Today’s graduates enter a landscape where familiarity with these systems is not optional. It is expected. Encouraging their use is therefore not an imposition but a responsibility,” he says.
Associate Professor Nicholas Puckett says while people are using AI tools in the workforce, and students should be prepared to understand and use them, there are concerns about AI’s impact on creative jobs going forward. He says that finding novel ways to use AI holds incredible value, both creatively and professionally.
“It’s important to recognize that these tools are not going away, so it’s important to find meaningful ways to use them. The tools are really powerful, but what they don't have are good ideas and our students have plenty of those,” says Puckett.
In the fall term in the workshop, Atelier 1, he taught students how to use coding tools like VSCode and Copilot to build creative web apps for phones. The goal was to help students see their phones as tools for making things, not just consuming content, and to combine coding with other creative areas like illustration and animation. As a result, students learned to write code, using real industry methods beyond simple AI prompts.
Puckett says that it’s important to understand that there isn’t one way to use AI tools.
“It becomes clear very quickly that these tools don't create projects. They might create some code, but you have to figure out how and how much you want to integrate them. It takes students a bit of time to learn about using these tools to the point where they feel the agency to experiment with them and not just feel like a passenger,” he says.
Given the impact of AI on the design field and within design education and research, Assistant Professor Dr. Lori Riva says it was an important and relevant topic to incorporate in her course, Contemporary Design Theories and Practices, for second-year students from all disciplines. Her goal was to integrate critical AI literacy into the classroom experience.
“Students are given opportunities to think critically about how we implement these tools responsibly and being aware of the impacts of its usage. Another approach I use is to focus on observation and the slower processes of analysis and research. I maintain that adopting these approaches instills more critical attitudes as to why and how to take up these tools,” she says.
Since students are confronted with the possibility of using AI tools in their writing, research and design practices, Dr. Riva strongly believes educators have a responsibility to adapt teaching approaches to help students develop the critical skills they need to navigate AI. Her goal is to provide a framework for students early in their education so they can be prepared to use these tools responsibly.
GENERATIVE AI FUTURES COURSE FOCUSES ON INFORMED ENGAGEMENT
In Manu’s course, Generative AI Futures, the emphasis has been on informed engagement instead of uncritical adoption. Students are introduced to the operational logic of neural networks, including both their capabilities and their limitations. Particular attention is given to bias, not as an abstract concern, but as a structural condition embedded in training data and model architecture.
“Once students recognize that these systems are trained on partial and uneven representations of the world, a shift occurs. They begin to see themselves not as passive recipients of output, but as active shapers of intent. This repositioning is essential,” says Manu.
He says a recurring theme among students is the challenge of prompting. At first, there is a tendency to treat the system as a tool that returns answers. Over time, this has evolved into a more nuanced understanding where the student becomes a narrator of possibility.
“The learning curve is present, particularly in mastering prompting, but it is perceived as an engaging problem rather than a barrier. The system invites exploration, and in doing so, it sustains curiosity,” he says.
STUDENTS DEVELOP CRITICAL AI LITERACY SKILLS
Dr. Riva incorporates GAI tools in in-class writing activities and research projects. As a result, students observe and analyze design objects and environments that engage with the concepts of the artificial and the natural. Students then use an AI tool to transform one of these objects into something else.
“Through this process, students are learning to engage in a critical thinking process as they use the tool. They are focused less on output and more on how they choose to engage with the tool. They are also learning skills such as writing attribution statements and reflecting on their process,” she says.
“Based on what they write in their reflections, students find they are thinking more purposefully about how and when to use the tools. They are also becoming more conscious of the relationship between their thinking process and the quality of the work that they generate using GAI,” says Dr. Riva.
Although students have opportunities to consider different ways of understanding and using AI tools, not all of them want to use AI and some are against it.
“I offer options for students to engage in the critical discussion without having to use AI tools. Students want transparency and agency regardless of where they stand on the use of AI in their own practices. I use this as an opportunity to talk about what "process work" means in the age of AI. I am open with my students about why I'm using tools like Copilot in the class. I also talk about how I value human effort and what that looks like in my course.”
DESIGN STUDENTS USING AI TO TEST IDEAS, IDENTIFY GAPS
Assistant Professor Parantap Bhatt has been incorporating GAI in third-year residential and hospitality studios in the Environmental Design program as well as in fourth-year interaction/experience design studios, which bring together students from Architecture, Interior Design, Advertising, Graphic Design, Industrial Design and Digital Futures.
In his teaching, AI is positioned not simply as a tool for output, but as a cognitive collaborator within the design process. This approach forms part of a broader framework he describes as “augmented design cognition,” where human intuition and machine intelligence co-evolve.
Bhatt’s assessment on how students use AI focuses on process, authorship and critical engagement. Students are required to document how AI is used throughout their workflow; reflect on what they accept/reject/transform; and demonstrate how their thinking evolves beyond AI outputs.
“Students initially engage AI for speed and generation, but over time develop a more critical understanding of its limitations, including bias and superficial outputs,” he says.
A key shift he has observed is that students have moved from “using AI for answers” to “using AI to ask better questions.” This shift strengthens students’ ability to critically evaluate and refine their design decisions.
“Students are gaining familiarity with AI tools that are currently shaping the profession, and more importantly, they are developing the ability to work effectively with intelligent systems. This prepares students not only to use AI tools, but to critically direct and evaluate them within professional practice,” says Bhatt.
