The Wrong Battle: Why Your Institution’s AI Policy Is Probably Solving the Wrong Problem
Every week, faculty members across higher education are spending hours doing the same thing: trying to figure out whether a student actually wrote a paper. They’re running submissions through AI detectors. They’re Googling suspicious phrases. They’re comparing sentence-level complexity across a student’s body of work. And they’re losing.
Not because they aren’t smart or dedicated. They’re losing because they’re fighting the wrong battle.
The conversation on most campuses has become consumed with detection: How do we catch students using AI when they shouldn’t? The impulse to protect academic integrity is legitimate, but the detection-first approach has a fatal flaw. AI detectors regularly flag legitimate student writing as AI-generated, including work by students who used only grammar tools, while missing AI-generated content that has been lightly edited. The bias problem compounds the accuracy problem: Stanford researchers found that detectors misclassified over 61% of essays written by non-native English speakers as AI-generated. A 2023 study in the International Journal for Educational Integrity that tested 14 detection tools concluded they are neither accurate nor reliable. As Bowen and Watson have argued, the question institutions must honestly confront is how many false accusations they are willing to accept as collateral damage. The tools students are using are evolving faster than any institution can keep pace with, and the arms race is unwinnable. In the meantime, institutions are spending enormous energy on policing rather than teaching.
There’s a deeper problem with this framing, though, and it’s one that gets far less attention. Focusing on detection treats the symptom, not the disease. The real challenge isn’t that students are using AI. It’s that AI use has fundamentally undermined the validity of many assessment tools that higher education has relied on for decades. A five-paragraph essay, an end-of-semester research paper, a take-home case study: These were always proxies for learning, never the learning itself. AI hasn’t changed that. It has just made the gap between the proxy and the thing it’s supposed to measure impossible to ignore.
That realization is the beginning of a genuine institutional response.
The Paradigm Shift Administrators Must Lead
Institutions that are navigating this well aren’t asking, “How do we catch students using AI?” They’re asking a different question entirely: “How do we know if our students are actually learning?”
That shift in question changes everything downstream: policy, assessment design, faculty development, and institutional culture. And it requires leadership. Faculty can’t make this pivot in isolation. The framing has to come from the top, because what’s really being asked of faculty is a significant professional and intellectual reorientation.
At Grand Canyon University, our approach rests on three interconnected pillars: a clear institutional position, curricular modernization, and what we call learning integrity, a framework that empowers faculty to verify learning rather than detect misconduct.