Summary: Some colleges tell students that AI use is not allowed. Employers expect new grads to be proficient in AI. Students are caught in the gap and being a "digital native" doesn't fill it. A cautionary lesson from an Air Canada lawsuit tells us: you own what AI produces. Students need clear AI policies, backed by tools to practice its use.
Universities are graduating students into workplaces that expect fluent, confident use of AI. Many of those students spent four years being warned that using AI is a form of cheating. That contradiction is the subject of a recent Harvard Business Publishing essay by management professor Megan Gerhardt (2026), who describes the whiplash of hearing colleagues plan to "shut down" AI in their classrooms the same week her corporate clients asked why new hires were afraid to touch it.
The reflex to restrict is understandable; assessment practices built over decades were upended in a few semesters. But prohibition carries a quieter cost. Students told to avoid AI never learn to use it with judgment — to catch a fabrication, to notice when a confident answer is wrong, to know what to ask next. And the assumption that a "digital native" generation will simply absorb these skills does not hold: fluency with apps is not the same as the discipline to verify what a model produces.
Regular readers may sense a tension with our recent posts, which argued that letting AI do students' thinking short-circuits real learning. This is the same problem from the other side. A student who outsources their reasoning learns nothing; a student taught to fear AI graduates unable to use it responsibly. Neither is a case for banning the tool or for turning it loose — both are arguments for design.
What is at stake is not academic. When Air Canada's customer-service chatbot invented a refund policy, the airline argued before a tribunal that the chatbot was a "separate legal entity" responsible for its own statements. The tribunal rejected that outright and held the company accountable for what its AI told a customer (Moffatt v. Air Canada, 2024). The lesson for students entering professional work is exact: when you use AI, you own what it produces. Knowing when to trust output and when to check it is now a core professional skill — and it is precisely what a hidden, stigmatized relationship with AI cannot build.
"You cannot teach critical judgment about AI tool use when students are forbidden to use them."
This is where design matters — not only the design of a course, but of the tools inside it. When AI lives in a private chat window students are careful not to mention, faculty never see the reasoning they are meant to coach. When AI is built into the learning environment, that reasoning becomes visible and teachable.
ScholarStack is intentionally designed to harness AI for education. Our AI Chat Agent draws students out — asking them to reason, verify, and push back on what the model returns — so that checking AI's work becomes a habit instead of an afterthought. Faculty keep control of how the AI behaves in their course. Instructors can build their preferred scenario-based practice exercises, such as analyzing a case or rehearsing a difficult conversation, turning the use of AI tools into a mechanism to develop professional instincts safely. And because the interaction is structured and observable, it gives an institution a coherent, intentional model of what responsible AI use looks like.
The question facing higher education leadership was never whether to allow AI. Students already have it, and the workplace already expects it. What they cannot do is resolve the contradiction on their own, guessing course by course which instructor to believe. Breaking that tie is the institution's responsibility, and it comes down to policy — a clear, useful position on what responsible AI use looks like, backed by the tools that let students and faculty actually practice it. Institutions that take that on will send graduates into the workforce ready to use AI with judgment. Those that leave it to individual classrooms will keep sending them into the gap: able to produce AI's output, but unprepared to stand behind it.
Stay tuned for future posts about important topics on AI in education. Thanks for reading.
– David Miller & Gladys Mercier