The Incorrect Battle: Why Your Institution’s AI Policy Is Most Likely Solving the Incorrect Issue
- By B. Jean Mandernach, Ph.D.
- 05/28/26
Each week, professor across college are spending hours doing the same thing: trying to find out whether a student actually wrote a paper. They’re running submissions through AI detectors. They’re Googling suspicious expressions. They’re comparing sentence-level intricacy across a trainee’s body of work. And they’re losing.
Not due to the fact that they aren’t wise or dedicated. They’re losing due to the fact that they’re fighting the wrong fight.
The conversation on many schools has become taken in with detection: How do we capture trainees using AI when they shouldn’t? The impulse to protect academic stability is legitimate, but the detection-first approach has a deadly defect. AI detectors routinely flag genuine trainee composing as AI-generated, consisting of work by students who used just grammar tools, while missing out on AI-generated material that has actually been gently edited. The predisposition problem compounds the precision issue: Stanford scientists discovered that detectors misclassified over 61% of essays composed 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 trustworthy. As Bowen and Watson have argued, the question organizations must truthfully confront is how many false allegations they are willing to accept as civilian casualties. The tools students are using are evolving faster than any organization can equal, and the arms race is unwinnable. In the meantime, institutions are spending huge energy on policing instead of mentor.
There’s a much deeper problem with this framing, though, and it’s one that gets far less attention. Concentrating on detection deals with the sign, not the disease. The genuine challenge isn’t that trainees are using AI. It’s that AI usage has actually basically undermined the validity of lots of evaluation tools that higher education has actually depended on for years. A five-paragraph essay, an end-of-semester term paper, a take-home case research study: These were constantly proxies for learning, never ever the discovering itself. AI hasn’t altered that. It has just made the space in between the proxy and the important things it’s expected to measure difficult to neglect.
That realization is the beginning of a real institutional action.
The Paradigm Shift Administrators Must Lead
Institutions that are browsing this well aren’t asking, “How do we catch students using AI?” They’re asking a various concern completely: “How do we know if our trainees are really finding out?”
That shift in question changes whatever downstream: policy, assessment design, faculty advancement, and institutional culture. And it requires management. Professors can’t make this pivot in seclusion. The framing needs to originate from the top, since what’s really being asked of faculty is a considerable expert and intellectual reorientation.
At Grand Canyon University, our technique rests on three interconnected pillars: a clear institutional position, curricular modernization, and what we call learning integrity, a structure that empowers professors to confirm knowing rather than identify misbehavior.