Everyone agrees it is happening. Below is a handy formula to limit unauthorized AI use in the classroom, for use by faculty, administrators, and legislators alike. Some of the key variables are controlled by the institution, some by the faculty. Everyone needs to work together.

I use the term “unauthorized,” so “cheating” is whatever the faculty member declares to be unauthorized use, understanding that in the AI era, students are fluent in AI and will seek the most efficient way to fulfill course assignments. (Most of the misconduct data is pre-LLM and is not directly applicable to today’s technology but I am assuming the same patterns hold.1 )

The hundreds of conversations I have had in the past year with faculty members across the country make clear that there is almost no agreement about what AI misconduct means. Some faculty members hold that any LLM use at all is cheating; others say “go ahead and use Claude or ChatGPT, but if I can prompt your paper into existence in under 10 minutes, you fail.”2

My proposed rule is flexible by focusing on AI use that is “unauthorized” by the faculty member. The institutional role is ensuring that faculty can enforce their own line.

P = 1 − (S × L × M × C × A × κ)

P is the probability that a student will use AI in a way the instructor did not authorize. P runs from 0 to 1. Zero means it won’t happen. One means it will.

The six variables are the conditions of teaching. Each runs from 0 to 1. Multiply them. Subtract from 1. That’s P.

• S — class size

• L — teaching load

• M — modality

• C — one-to-one contact

• A — assessment design

• κ — institutional culture

A 1 means the condition is fully present. A 0.1 means it’s gone.

S — class size. 1 if the class is fifteen or fewer. 0.75 if sixteen to twenty-five. 0.5 if twenty-six to fifty. 0.25 if fifty-one to a hundred. 0.1 if more than a hundred. The class-size literature on cheating is thirty years old and surprisingly consistent.3

L — teaching load. 1 if an instructor teaches one or two courses per term. 0.75 if three. 0.5 if four. 0.25 if five. 0.1 if six or more. The load decides how many students an instructor can know. A 5:5 load with mid-sized sections is 750 papers.4

M — modality. 1 if the class meets in person. 0.75 if hybrid with required attendance. 0.5 if online synchronous. 0.25 if asynchronous with proctoring. 0.1 if asynchronous without proctoring. The systematic review evidence on online exam cheating shows the rate jumping from roughly 30 percent to 55 percent during the pandemic, which is a measurement of structural pre-concession.

C — one-to-one contact. 1 if the instructor has three or more scheduled meetings with each student over the course of the term. 0.75 if two. 0.5 if one. 0.1 if zero. A faculty member who never sits across from a student has no baseline for saying “this is not your work.” The contextual-factors finding replicated since the 1990s, that faculty engagement predicts integrity better than any honor code or detection tool, is the same finding stated in the negative.5

A — assessment design. 1 if the instructor uses an oral defense or in-class component. 0.75 if staged drafts with feedback. 0.5 if take-home with required process artifacts. 0.25 if take-home final only. 0.1 if a single uploaded file graded by rubric. A single upload is the assignment most easily faked and the assignment most institutions are now using.

κ — institutional culture. 1 if the institution adjudicates reports within the term and backs faculty judgment. 0.5 if there’s a written policy that gets enforced sometimes. 0.1 if reports are routinely declined or reversed. κ is the institutional term and it multiplies everything else, because a faculty member who tries to hold the line on authorization in conditions of 5:5 loads and 200-person sections gets nowhere if the institution will not back the report.

1. High probability: The conditions that produce the greatest likelihood of unauthorized AI use are large asynchronous courses.

• 200 students. S = 0.1. A 5:5 load. L = 0.25. Async, unproctored. M = 0.1. No individual meetings. C = 0.1. Single upload. A = 0.1. An institution that won’t enforce. κ = 0.1.

• 0.1 × 0.25 × 0.1 × 0.1 × 0.1 × 0.1 = 0.0000025.

• P = 1 − 0.0000025 ≈ 1.

The probability of unauthorized AI use is 100%. It will happen. Writing AI prohibitions into the syllabus under these conditions is theater.

2. Low probability: The conditions that produce the least likelihood of unauthorized AI are small, in-person labs and seminars.

• 15 students. S = 1. A 2:2 load. L = 1. In person. M = 1. Three meetings per student. C = 1. Oral defense. A = 1. An institution that adjudicates and backs you. κ = 1.

• 1 × 1 × 1 × 1 × 1 × 1 = 1.

• P = 1 − 1 = 0.

The probability of unauthorized AI use is zero or close to it. The conditions will prevent it.

Middle scenario 1): These are the conditions that most faculty actually teach in.

• 30 students. S = 0.5. A 3:3 load. L = 0.75. Hybrid. M = 0.75. One meeting per student. C = 0.5. Drafts and revisions. A = 0.75. Written policy, slow enforcement. κ = 0.5.

• 0.5 × 0.75 × 0.75 × 0.5 × 0.75 × 0.5 = 0.053.

• P = 1 − 0.053 = 0.947.

The probability of unauthorized AI use is almost certain.

Middle scenario 2) Also common conditions for many faculty .

• 35 students. S = 0.5. A 4:4 load. L = 0.5. In person. M = 1. Zero scheduled meetings. C = 0.1. Take-home final only. A = 0.25. Written policy, slow enforcement. κ = 0.5.

• 0.5 × 0.5 × 1 × 0.1 × 0.25 × 0.5 = 0.0031.

• P = 1 − 0.0031 = 0.997.

Even with the class meeting in person, the absence of one-to-one contact and the take-home final drag the product down to almost nothing. M = 1 cannot save the product when C = 0.1. Faculty members who teach in person but never meet their students one-to-one have the same problem as the faculty member teaching async.

Most classrooms are these middle scenarios, which means that there is a 100% likelihood that there is unauthorized AI use occurring in nearly every college classroom.

Most American universities now run on adjuncts and lecturers carrying 4:4 and 5:5 loads, with sections of 30 to 200, increasingly asynchronous, with no time built in for one-to-one contact, with assessment by uploaded artifact because there is no time to grade anything else, at institutions whose enforcement processes take longer than a semester.6 The rule says those conditions produce P ≈ 1.

I decided to create a rule because while everyone agrees that unauthorized AI use is happening,7 I’ve seen no good work toward dealing with it structurally.

According to my rule, the variables compound rather than compete. Some might object that the multiplication is too punishing. A dedicated faculty member with a huge class and a heavy load might still know her students through their writing. Maybe. But the faculty member is also drowning in work, a condition that is in institutional control.

The faculty member who reads this and thinks “I can score 1 on assessment by switching to oral defenses” is right but also defeated by L and S. Oral defenses for 200 students at a 5:5 load are not happening. The rule is multiplicative because the conditions multiply in the faculty member’s work life.

According to my rule, an institution running 5:5 loads, 200-person lectures, asynchronous delivery, no scheduled contact, single-artifact assessment, and a slow enforcement process has created the conditions for unauthorized AI use that a faculty member cannot prevent. State funding that requires institutions to create these conditions should understand that the state is creating the conditions for unauthorized AI use.

The formula will need revision. The variables will need revision. But the multiplication will hold, because the conditions hold, and the conditions are what the formula is for. Note that I have included state legislatures and higher education boards as an audience for this post. They are also responsible for the conditions of unauthorized AI use in the classroom.