The Incorrect Fight: Why Your Organization’s AI Policy Is Probably Fixing the Incorrect Problem
- By B. Jean Mandernach, Ph.D.
- 05/28/26
Each week, faculty members throughout higher education are spending hours doing the same thing: attempting to find out whether a student really wrote a paper. They’re running submissions through AI detectors. They’re Googling suspicious phrases. They’re comparing sentence-level complexity throughout a student’s body of work. And they’re losing.
Not because they aren’t clever or dedicated. They’re losing because they’re fighting the wrong battle.
The conversation on the majority of schools has ended up being consumed with detection: How do we catch trainees utilizing AI when they should not? The impulse to protect academic integrity is legitimate, but the detection-first technique has a deadly defect. AI detectors routinely flag genuine trainee composing as AI-generated, including work by trainees who used only grammar tools, while missing out on AI-generated content that has been lightly modified. The bias problem compounds the precision problem: Stanford scientists found that detectors misclassified over 61% of essays written by non-native English speakers as AI-generated. A 2023 research study in the International Journal for Educational Stability that checked 14 detection tools concluded they are neither accurate nor reputable. As Bowen and Watson have argued, the question institutions should truthfully confront is the number of incorrect allegations they want to accept as collateral damage. The tools trainees are utilizing are evolving faster than any organization can keep pace with, and the arms race is unwinnable. In the meantime, institutions are spending enormous energy on policing rather than mentor.
There’s a much deeper problem with this framing, though, and it’s one that gets far less attention. Focusing on detection deals with the sign, not the disease. The real obstacle isn’t that trainees are using AI. It’s that AI usage has actually essentially undermined the validity of lots of evaluation tools that higher education has counted on for years. A five-paragraph essay, an end-of-semester research paper, a take-home case research study: These were always proxies for knowing, never the discovering itself. AI hasn’t altered that. It has actually simply made the gap in between the proxy and the thing it’s supposed to determine impossible to ignore.
That realization is the start of a real institutional reaction.
The Paradigm Shift Administrators Should Lead
Institutions that are browsing this well aren’t asking, “How do we capture students utilizing AI?” They’re asking a various question totally: “How do we understand if our trainees are actually discovering?”
That shift in concern changes everything downstream: policy, evaluation design, faculty advancement, and institutional culture. And it requires management. Faculty can’t make this pivot in seclusion. The framing needs to come from the top, since what’s truly being asked of faculty is a significant expert and intellectual reorientation.
At Grand Canyon University, our approach rests on three interconnected pillars: a clear institutional position, curricular modernization, and what we call discovering integrity, a framework that empowers professors to confirm knowing rather than discover misbehavior.