Believe Your Assignment is AI-Immune? Let's Put it to the Test

Welcome to AutomatED: the newsletter on how to teach better with tech.

Each week, I share what I have learned — and am learning — about AI and tech in the university classroom. What works, what doesn't, and why.

Let’s have some fun and put our money where our mouth is.

This week, I throw down the gauntlet to my fellow professors to submit their best AI-immune writing assignments. We will rise to the occasion and try to complete each of them in less than an hour, using the latest generative AI tools.

I have argued that the AI plagiarism problem is deep, so let’s see if I am right.

The Background

After my recent pieces on the depth of the AI plagiarism problem and on how I think we should conceptualize solutions to it, I received some feedback online from a fellow professor. They told me that their solution is to create assignments where students work on successive/iterative drafts, improving each one on the basis of novel instructor feedback.

Iterative drafts seem like a nice solution, at least for those fields where the core assignments are written work like papers. After all, working one-on-one with students in a tutorial setting to build relationships and give them personalized feedback is a proven way to spark strong growth.

The problem, though, is that if the student writes the first draft at home — or, more generally, unsupervised on their computer — then they could use AI tools to plagiarize it. And they could use AI tools to plagiarize the later drafts, too.

When I asserted to my internet interlocutor that they would have to make the drafting process AI-immune, they responded as follows (note: I won’t link their response because the point here isn’t to put them on blast):

In my view, this is a perfect example of a professor not grasping the depth of the AI plagiarism problem.

The student just needs to tell the AI tool that their first draft — which they provide to the AI tool, whether the tool created the draft or not — was met with response X from the professor.

In other words, they can give the AI tool all of the information an honest student would have, were they to be working on their second draft. The AI tool can take their description of X, along with their first draft, and create a new draft based on the first that is sensitive to X.

Not much work is required of the student, and they certainly do not need to learn how to input the suggested changes or about the relevant concepts. After all, the AI tools have been trained on countless resources concerning these very concepts and how to create text responsive to them.

This exchange indicates to me that the professor simply has not engaged with recent iterations of generative AI tools with any seriousness.

Given that they did not assert that they have tried this sort of assignment on AI tools, I take it they would admit as much, at least with respect to this sort of assignment. Their confidence came from their general understanding of how AI tools’ capabilities interfaced with their assignment’s parameters.

Solving the AI plagiarism problem from the armchair runs contrary to a crucial takeaway of my original piece on solutions: for every assignment, professors need to either decide it can/should be completed in an AI-free environment; make it AI-immune by experimenting a lot with how different AI tools respond to its instructions; or pair it with an AI-immune assignment.

Here is my original diagram of the assignment design process: