Less Cheating, More Learning

March 26, 2026

 For a few weeks before Spring Break, I had the pleasure of co-facilitating a reading group, together with Juhong Christie Liu, Nathaniel Taeho Yu (both JMU Libraries), and Chelsey Bahlmann Bollinger (College of Education), about the book The Opposite of Cheating. Teaching for Integrity in the Age of AI (Patricia Bertram Gallant and David Rettinger, 2025) — a timely choice, considering faculty concerns about student cheating, especially with generative AI tools. 

What I appreciated about this book (and its precursor, James Lang's Cheating Lessons) was that the authors focused less on ways to detect and punish cheating and more on ways to motivate students not to cheat. Based on research into the causes of cheating, the authors propose ways to create class environments, design assignments, and interact with students that make it less likely that students want to cheat. The underlying assumption here is that students are not hardened anti-social cheaters but overall well-meaning human beings whose behavior responds to incentives, persuasion, and learning. 

Here are some of the suggested approaches: 

Shaping our class environment requires a semester-long effort to create a climate that is conducive to ethical behavior. This involves: 

• Focusing on learning instead of performance, for example by emphasizing intrinsic over extrinsic motivation and de-emphasizing grades as student motivators. 

• Making ethics an integral part of what students learn. We may want to discuss with students: What does academic integrity mean (in our class, our discipline, our profession, and in life), and what purposes does it serve? What are the official ethical standards of our discipline or profession, and what purposes do they serve? How do academic ethics serve the students' best interests in academics and their future professional lives? Bertram Gallant and Rettinger suggest co-creating ethical standards with students (p. 51; JMU's Ethical Reasoning in Action might serve as a starting point for the discussion.) 

• Building connections (through community or rapport) with students. If there is trust between us and our students, they are less likely to cheat, especially if there are commonly understood ethical norms. 

• Building connections between students. Students are less likely to cheat if they understand that this is unfair to students who they are connected to (Lang 2013, p. 50). 

Assignments can be designed in ways that minimize cheating. This may include 

• Clarifying the purposes of assignments, including how they help students achieve their learning goals, and how cheating undermines this process. 

• Explaining what academic honesty looks like in the context of a particular assignment. If we want students to be honest, we have to teach them how this looks like — and what cheating is. (For example, this may include explaining how students may use generative AI in their assignments.) 

• Making sure students feel confident that the assignment has value for them and that they can expect to be successful — core elements of motivation. This may involve encouraging students to seek help from us if necessary, allowing for revisions and corrections, and explaining the purpose of the assignment clearly. 

• Being flexible with deadlines, to the extent possible. Students who run up to a deadline for a major assignment are more likely to cheat (especially in the presence of tools like ChatGPT). Giving a way out of the deadline can help them avoid that decision (Bertram Gallant and Rettinger 2025, p. 66). 

• Avoiding assignments that overwhelm students, for example assignments that count for a large portion of the course grade. Students are more likely to cheat on an assignment if they believe that they cannot succeed in it and that they will fail the course, or receive a dramatically lower grade, because of it (Bertram Gallant and Rettinger 2025, p. 74). Breaking up (and scaffolding) assignments into smaller components is often a helpful approach (p. 83), as well as boosting student confidence in their abilities (see the third point in this listicle). 

• More generally, creating assignments that follow universal design principles to the extent possible, and showing that we are willing to work with students who may need accommodations, is important, not only to prevent cheating. We do not want students to believe that they cannot succeed in an assignment because of disability. 

• Including a metacognitive portion in the assignment. This is possible in exams but also in other types of assignments, for example by asking students to describe how they went about completing the assignment, what went well and what turned out to be a dead end, how they used AI, what they would do differently next time, and similar. 

Lang as well as Bertram Gallant and Rettinger provide more strategies, and more detail; read the two books. We may not be able to use all of their strategies all the time: For example, at some point, deadlines have to be kept so that we can submit grades (though let's not forget about the possibility of incomplete grades). Sometimes, students have to experience strictly proctored exams to be prepared for licensing examinations. And some assignments are simply large assignments that have to count for a large portion of the grade. But we will be able to use many of these strategies some of the time. 

To be sure, it is not possible to "cheat-proof" our classes or assignments, but we can make cheating less likely in ways that also promote learning among students. Cheating has been around since ... the dawn of time (I show myself out) ... and we won't be able to end it. But at least we can teach well and make it more likely than not that most of our students act ethically and learn successfully.