There’s a quiet revolution happening in Canadian classrooms, one that’s less about chalkboards and textbooks and more about algorithms and uncertainty. As universities grapple with the integration of artificial intelligence, the real test of Canada’s AI strategy isn’t in boardrooms or government reports—it’s in the messy, human-centered world of university classrooms. What makes this particularly fascinating is how the tension between innovation and tradition is reshaping not just what students learn, but how they learn it, and who bears the burden of figuring it out.
Let’s cut through the jargon. Canada’s national AI strategy is all about boosting literacy, building trust, and ensuring responsible adoption. But here’s the catch: when you hand educators a vague mandate to ‘incorporate AI responsibly,’ you’re asking them to solve a puzzle with half the pieces missing. A recent study I co-authored with Emily Ballantyne at Mount Saint Vincent University revealed that faculty members feel like they’re playing a game of Whac-A-Mole with AI policies. They’re expected to decide when AI is appropriate, when it’s not, and how to balance academic integrity with student agency—all while navigating a patchwork of institutional guidelines that range from draconian restrictions to open-ended experimentation.
What many people don’t realize is that this isn’t just about technology. It’s about relationships. The study found that faculty members are increasingly acting as detectives, scrutinizing student work for signs of AI assistance, which undermines the very trust that should be the foundation of education. One professor described feeling like a ‘suspicion enforcer’ rather than a mentor. This isn’t just exhausting—it’s dehumanizing. If you take a step back and think about it, this mirrors a broader trend: as AI becomes more prevalent, the emotional labor of teaching is being quietly redefined, with educators shouldering the weight of a system that’s still figuring itself out.
The policy problem here isn’t just about guidelines; it’s about perspective. International frameworks like the AI Ecological Education Policy developed by Cecilia Chan in Hong Kong offer a blueprint, but they’re built on assumptions that predate tools like ChatGPT. Canada’s unique context—its commitment to Indigenous reconciliation, its diverse post-secondary landscape, and its history of balancing equity with innovation—demands a different approach. What this really suggests is that policies must be rooted in lived experience, not just theoretical models. For instance, when considering Indigenous communities, AI policies can’t ignore the cultural responsibilities tied to education. It’s not just about ‘adding AI’ to curricula; it’s about ensuring that technology doesn’t erase the relational accountability that Indigenous pedagogies emphasize.
The CARE Framework, which I find especially compelling, offers a way forward. It’s not just a checklist; it’s a philosophy. Critical AI literacy means teaching students to question tools, not just use them. Accountable governance requires universities to stop treating AI as a black box and instead create transparent, fair processes. Relational-affective pedagogy? That’s where the rubber meets the road. If students and faculty can’t trust each other, AI will become a wedge, not a bridge. And ethical orientation—this is the heartbeat of the whole thing. Every decision about AI must ask: Who benefits? Who gets left behind? How does this align with the purpose of education beyond grades?
Here’s the kicker: universities aren’t just failing to support faculty—they’re actively creating a culture of suspicion. A detail that I find especially interesting is how the burden of policing AI falls disproportionately on educators, even as institutions claim to prioritize student well-being. This hidden work is now part of classroom life, and it’s unsustainable. Imagine a teacher who’s supposed to inspire critical thinking but spends hours second-guessing whether a student’s essay was written by a human or an algorithm. It’s a paradox that speaks to a deeper question: Can we trust technology without losing trust in each other?
The solution isn’t just more rules. It’s about reimagining what education can be. Universities need to recognize that adapting to AI requires more than just updating syllabi—it demands a cultural shift. Faculty need time, training, and institutional support to redesign assignments that foster AI literacy without sacrificing authenticity. They need to be part of the conversation, not just subjects of top-down mandates. And they need to be compensated for the extra work, because the emotional and intellectual labor of navigating AI isn’t a side gig—it’s a core part of their role.
Teacher education programs, meanwhile, are the unsung heroes of this story. Future educators must be trained not just to use AI, but to critically evaluate it. They need to ask questions like: What does this tool do or miss? Who benefits? Who may be harmed? These aren’t abstract exercises—they’re survival skills in a world where AI is both a tool and a threat. Without this preparation, the promise of Canada’s AI strategy will remain just that: a promise.
In the end, the real test of Canada’s AI strategy isn’t in the policies or the technology—it’s in the classrooms. It’s in whether educators feel supported, whether students feel trusted, and whether the system as a whole remembers that education is about people, not just data. If universities can’t get this right, they risk turning AI into a barrier to learning, not a bridge to it. And that would be a tragedy no algorithm could ever fix.
