Let me start with a question: What happens when a technology designed to streamline human labor becomes the very thing that complicates the human experience of teaching? That’s the paradox unfolding in Canadian university classrooms, where artificial intelligence is no longer just a tool—it’s a disruptor, a challenge, and sometimes, a burden. The federal government’s AI strategy promises a future of responsible adoption, but the real test isn’t in boardrooms or tech labs. It’s in the quiet chaos of lecture halls, where professors grapple with questions no one anticipated when they chose their careers.
Personally, I think the current debate around AI in education misses a critical truth: this isn’t just about cheating or efficiency. It’s about the fundamental relationship between teachers and students, and how technology is reshaping that bond. A recent study I co-authored with Emily Ballantyne at Mount Saint Vincent University revealed a striking reality—faculty members feel like detectives, not educators, as they try to discern whether student work was generated by AI. One professor described it as 'a game of whack-a-mole,' where every assignment becomes a potential minefield of suspicion. What makes this particularly fascinating is how deeply it exposes the cracks in our educational systems, which were already strained by inequities in workload, support, and cultural responsiveness.
The problem isn’t just the tools themselves. It’s the lack of coherent guidance. Universities are all over the map when it comes to AI policies. Some ban it outright, others require disclosures, and many leave it to individual instructors to improvise. This inconsistency creates a bizarre double standard: students might face different rules depending on the professor, while teachers are left to interpret vague institutional mandates. If you take a step back and think about it, this mirrors the broader societal struggle with AI—how do we create rules that are both flexible enough to adapt to innovation and strict enough to protect integrity? The answer isn’t in top-down mandates. It’s in fostering a culture of shared responsibility, where trust isn’t a liability but a foundation.
What many people don’t realize is that the stakes go beyond academic integrity. AI is altering the very conditions of learning. When students use tools like ChatGPT to generate essays, they’re not just bypassing plagiarism checks—they’re missing out on the messy, iterative process of critical thinking. One faculty member in our study put it bluntly: 'It undermines the development of relational skills.' This raises a deeper question: If AI is here to stay, how do we ensure it enhances, rather than erases, the human elements of education? The answer lies in what I call 'relational pedagogy'—a framework that prioritizes trust, equity, and the emotional labor of teaching.
Here’s where the CARE Framework comes in, a model that redefines AI policy not as a technical problem but as a human one. Critical AI literacy means equipping educators to design assignments that reveal student thinking, not just correct answers. Accountable governance requires universities to stop treating AI as a side issue and instead embed it into their core values. Relational-affective pedagogy demands we acknowledge the emotional toll of policing AI use, which disproportionately falls on marginalized instructors. And ethical orientation? That’s the glue holding it all together—ensuring decisions about AI align with Indigenous principles of relational accountability and the broader goal of education as a humanizing force.
A detail I find especially interesting is how this crisis in higher education is shaping the next generation of teachers. Teacher education programs are the unsung heroes of this story. Future educators need to be trained not just to use AI, but to critically evaluate it. They must ask: Who benefits from this tool? Who might be harmed? What learning goals are we truly serving? Without this kind of training, we risk creating a generation of teachers who are technically proficient but ethically unprepared. This isn’t just about curriculum—it’s about cultivating a mindset that sees AI as a mirror, not a shortcut.
So what’s the takeaway? Canada’s AI strategy is a noble vision, but it’s incomplete without a cultural shift. Universities can’t just issue policy documents; they need to model the kind of trust and collaboration they expect from their faculty. They must recognize that adapting to AI isn’t just about adding new tools—it’s about reimagining the entire ecosystem of teaching and learning. If we fail to do this, we’ll end up with a system that’s technically advanced but emotionally hollow. The real test of Canada’s AI strategy won’t be measured in funding or research papers. It’ll be in the eyes of students who walk into a classroom and see a teacher who feels like a partner, not a policeman.
