Why I'm using AI detection after all, alongside many other strategies

I'm incorporating Turnitin alongside process tracking, writing process assignments, social annotation, lots of student choice, peer review and tutoring, video assignments, and more.

I argued against use of AI detection in college classrooms for two years, but my perspective has shifted. I ran into the limits of my current approaches last semester, when a first-year writing student persisted in submitting work that was clearly not his own, presenting document history that showed him typing the work (maybe he. typed it and maybe he used an autotyper). He only admitted to the AI use and apologized for wasting my time when he realized that I was not going to give him credit and that if he initiated an appeals process, the college would run his writing through detection software. I liked this student, had met with him previously and encouraged him to build confidence in his own voice and helped him find a research topic that interested him. I didn't think he would be well served by an AI on an autopaper, so I was glad that AI detection existed.

I'm also influenced by recent research that suggests detection is likely not biased against English language learners after all, that educators are not as good as we think we are at distinguishing AI from student writing on our own, and that some detection systems are pretty accurate when it comes to naive copy/paste AI use. Christopher Ostro's slide deck lit review of recent research on detection has been invaluable. He discusses this research in an engaging episode of Bonni Stachowiak's wonderful podcast Teaching in Higher Ed. Dr. Tricia Bertram Gallant's leadership on academic integrity and AI has influenced me over the last two years as well. She has shown levelheaded willingness to consider a possible role for detection even as she promotes other approaches as more important and effective.

As I teach composition online asychronously this semester, I'm incorporating Turnitin alongside process tracking, writing process assignments, social annotation, lots of student choice, peer review and tutoring, video assignments, and clear messages about the purpose of each activity and the value of the writing process. I love that Phillip Dawson has described this kind of laying of strategies as a "swiss cheese" approach and others have used the mosaic metaphor (I'm having trouble tracking down who). I've described my combination of approaches in slides for a recent presentation on Academic Integrity and AI.

What about the risk that AI detection will lead to false accusations? It's real, and I let students know I'm aware that detection yields some false positives. I will never trust AI detection as firm evidence, and I am not punishing students. If a student discusses an essay with me, shows process history, and denies AI use, I will give them the benefit of the doubt even if the detector says "AI." If they aren't able to discuss their writing, I ask students to rewrite.

Christopher Ostro put it in a way that resonates for me: "I think AI detection has a place, but its place is limited." In most human endeavors, some accountability structures are important even when we design for intrinsic motivation. And we don't have perfect options here. The options are fewer in online asynchronous classes that allow many working-class students and parents to access college.

I know so many colleagues I respect are highly critical of AI detection, seeing it as signaling antagonism toward students. Christopher Ostro clarifies that his purpose is not to punish students but to provide some accountability that, in the end, encourages learning and shows that we care. He says, "I am not a cop teacher. I am not someone who likes catching cheaters. I’m not someone who wants that to be a big part of my job. Honestly, it’s the least fun part of teaching, but it’s also it is still a part of the job."

I'm a member of the MLA/CCCC Joint Task Force on Writing and AI, a group that has put out strong cautions about it in our working paper on Generative AI and Policy Development. There, we argue that "Tools for detection and authorship verification in GAI use should be used with caution and discernment or not at all." We write, "For those who decide to use AI detectors, please consider the following questions: What steps have you taken to substantiate a positive detection? What other kinds of engagement with the student’s writing affirms your decision to assign a failing grade outside the AI detector’s claim that the text was AI generated?" We also emphasize that "any technological approaches to academic integrity should respect legal, privacy, nondiscrimination, and data rights of students."

I have tried to use detection and process tracking in ways that I hope address those concerns. I invite students to comment frankly on syllabus policies. If students don't want to share process history or if they object to AI detection, I invite them to meet with me to chat about the essay instead. My approach is a work in progress in a changing landscape. Next up: an anonymous survey to see what more I can find out about what students think.