Canada's AI Strategy Faces Its Toughest Test: The University Classroom

Canada's national AI strategy calls for broader AI literacy, public trust, and responsible adoption in post-secondary education. But a new case study suggests the strategy is landing unevenly in the places that matter most.

Johanathan Woodworth, an assistant professor at Mount Saint Vincent University, co-authored a mixed-methods study of 53 faculty members examining how AI policy direction is experienced on the ground. What they found is a gap between policy aspiration and classroom reality.

Faculty described AI policies as unclear, inconsistent, and top-down. Without shared expectations, instructors improvised, often judging student work through suspicion rather than evidence. As one faculty member put it: "I feel like a detective, not a teacher."

Key takeaways:

• Faculty are not rejecting AI. Many see real benefits including faster feedback and new forms of learning support, but report confusion, stress, and institutional abandonment when it comes to implementation.
• Hidden workload is growing. Faculty are redesigning assignments, explaining AI rules, and adjudicating possible misconduct without additional time, support, or training.
• Teacher education is the critical relay. Teacher candidates who leave universities without strong AI frameworks will carry uncertainty into K-12 classrooms, weakening Canada's AI literacy goals at scale.
• The study proposes a CARE framework: Critical AI literacy, Accountable governance, Relational-affective pedagogy, and Ethical orientation, with equity and Indigenous perspectives woven throughout.

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