David Laszczkowski
Co-Founder, EdPilot
When calculators became cheap and common, universities didn't ban arithmetic — they redesigned what they were trying to teach. The same logic applies now, and the institutions that understand that will be better positioned.
There's a version of the current AI debate that treats it as unprecedented — a crisis unlike anything higher education has faced. That framing is both overstated and unhelpful, because universities have navigated similar transitions before and the pattern is recognizable.
When calculators became cheap and common, there were educators who argued for strict prohibition. The concern was legitimate: if students can outsource computation, will they develop quantitative intuition? Some institutions banned calculators in certain contexts. Others redesigned what they were trying to teach — shifting emphasis from arithmetic execution to problem setup, interpretation, and analytical judgment.
The second group produced graduates who were better equipped for a world in which calculators exist.
The same logic applies now.
AI changes what's easy to outsource. Writing a passable five-paragraph essay, producing a generic literature summary, generating a plausible first draft of an analysis — these things are genuinely easier with current AI tools than they were two years ago.
What AI doesn't change is the underlying goal of education. Universities exist to develop people who can think rigorously, evaluate evidence, construct arguments, apply knowledge in novel situations, and exercise judgment under uncertainty. AI doesn't do any of those things for you. It can produce text that looks like those things, which is a real integrity risk, but it can't actually develop those capacities in you.
The response to calculators wasn't to abandon quantitative education — it was to ask what quantitative skills actually matter when computation is cheap. The response to AI shouldn't be to abandon writing and analytical education — it should be to ask what analytical skills actually matter when first-draft generation is cheap.
The universities that will navigate this well aren't the ones that dig in most defensively. They're the ones that ask the honest question: given that students have access to these tools, what does rigorous education look like?
That question leads to different places depending on the discipline. In some fields it means more emphasis on synthesis and less on summary. In others it means more emphasis on the reasoning behind conclusions and less on the conclusions themselves. In others it means using AI as a teaching tool — something students interact with in structured ways that build understanding — rather than pretending it doesn't exist.
None of this requires abandoning academic standards. It requires thinking clearly about what those standards are actually for.
The most immediate implication for most institutions isn't curriculum redesign — that's a longer project. The immediate implication is governance.
Right now, students at most universities are using AI extensively with no institutional visibility, no faculty oversight, and no connection to their actual course materials. The AI is operating in a completely unstructured way, and whatever impact it's having on learning is invisible.
The first step is building the infrastructure to change that: giving faculty actual control over the AI environment their students interact with, and giving institutions visibility into what's actually happening. That's the foundation everything else builds on.