With more students experimenting with AI tools and integrating AI into their workflows, OCAD U faculty members are incorporating these tools into studio and classroom settings.
Rather than treating AI solely as a challenge to academic integrity, many faculty members are approaching it as a creative and analytical medium.
This shift is driving new pedagogical approaches that emphasize process over product, originality over automation, and critical engagement over passive use. Students are being guided to understand not only how to use AI tools, but when, why, and to what extent they should be used within artistic and design practices.
Assistant Professor Parantap Bhatt in the Faculty of Design has been incorporating generative AI 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 my teaching, AI is positioned not simply as a tool for output, but as a cognitive collaborator within the design process,” says Bhatt.
Students in his courses engage with AI across three key areas:
According to Bhatt, the approach noted above forms part of a broader framework he describes as “augmented design cognition,” where human intuition and machine intelligence co-evolve.
This work also extends beyond the classroom. For example, he presented related student work at NYU (Tandon School of Engineering) as part of the Learning to Teach: (Re)Designing Creative Tech Pedagogy for the GenAI Era initiative. Additionally, in a recent World Design Organization (WDO) publication, developed with a Strategic Foresight and Innovation graduate student, he explored how generative AI can support circular intelligence and identify gaps in sustainable food systems in Toronto.
Bhatt introduces AI through a structured framework that has evolved over the past few years through pedagogical discourses of teachings at conferences and symposiums and a standard framework looks like the following:
“These principles are also informed by broader conversations around ethics, bias and the role of AI in creative practice and pedagogy,” he says.
The response from students has been very positive.
“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 from students “using AI for answers” to “using AI to ask better questions.”
“This transition significantly strengthens their design thinking and has also been reflected in discussions at broader academic forums and summits,” he says.
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.
“Rather than replacing learning objectives, AI has deepened them—particularly in critical thinking, iteration and communication,” he says.
Bhatt says students gain familiarity with tools currently shaping the profession, but more importantly, they are developing the ability to work effectively with intelligent systems. This will support them as they enter the workforce.
“Navigating prompts, curation and evaluation helps students iterate rapidly while maintaining design intent, and more importantly they learn how to remain critical, ethical, and human-centered designers in an AI-driven context. This positions them not just as users of AI, but as designers who can meaningfully direct and critique it,” he says.