Teaching Thursday: More on the Opposite of Teaching

Over the weekend, I finished Tricia Bertram Gallant and David A. Rettinger, The Opposite of Cheating: Teaching for Integrity in the Age of AI. (2025). It’s a fine little book that belongs to a very specific genre of literature focused on college level instruction. These books tend to offer very sound, if rather hackneyed advice: focus on student learning, be humane, communicate your course goals, design your classes with those goals in mind, and don’t be afraid to innovate or experiment. There must be dozens of books that make these general recommendations (in various flavors, nuances, and contexts) annually. They offer the combined advantage of being “recent” which appeals to early career faculty attentive to the latest jargon, practices, and approaches and approachable for faculty across many disciplines. In general, they are harmless.  

The strongest part of this book is that it serves as a useful reminder that many examples of academic dishonesty stem not from student laziness or a willful disregard for academic standards or fairness. Instead, they stem from ambiguous instructions (e.g. can they work with other students to do the assignment?), questionable pedagogical goals (e.g. classes with a disproportionate emphasis on rote memorization and content reproduction) misunderstanding disciplinary standards (e.g. what needs to be cited and how?), exhaustion and desperation (e.g. poor decision making when faced with the relentless stress of college life), or the failure of faculty to make clear the pedagogical goal of the assignment (e.g. the use of generative AI to write when the goal of the assignment is actually writing). Gallant and Rettinger offer some useful advice on communicating expectations to students and argued that by explaining the pedagogical goals of work and disciplinary (and community) expectations, faculty can create an environment where students have a clearer understanding of what constitutes cheating. This is good advice and is consistent with the message that books concerned with faculty teaching have been offering for the last 20 odd years (if not more). Unsurprisingly, faculty who communicated better with their students and recognized their struggles and temptations, created more effective learning environments which, in turn, minimized the risk of cheating. This is a good reminder, but hardly a revolutionary insight.

If anything, I hoped that the book might speak to the growing awareness that generative AI may well change how we teach. There have been a constant flow of blog posts, newsletter think pieces, Chronicle articles, and workshops on using AI, preventing the use of AI, or even just understanding AI. Hecks, The Digital Press even got into the act with Shawn Graham’s remarkable little book Practical Necromancy for Beginners. Social media is an enormously amusing war zone with otherwise thoughtful faculty making ill considered statements about AI and equally incredulous responses by other commentators. At its absolute best, it devolves into people blocking one another amid various social media insults. In other words, it’s the kind of fun that only social media can bring where stakes are low and emotion is high.

Gallant and Rettinger bring a different perspective to the conversation. They began their book — which I imagine originally focused on cheating and pedagogy in higher education — before the explosion in generative AI. Responsibly, they pivoted and not only folded AI into their text at some key junctures, but also make some recommendation for how to use generative AI to create and refine assignments, to produce rubrics, and to randomize test banks.

The most interesting aspect of their argument is that good pedagogy not only mitigates against cheating by making it less appealing (and more difficult) for students, but also engages with student behavior on an ethical level. It is in this latter area where their work is most interesting, but also the most frustrating. They encourage us to embrace an ethically informed pedagogy and who can reasonably disagree with that. At the same time, they are a bit vague on what these ethics should be. They acknowledge that disciplinary standards, social assumptions and expectations, and even institutional policies can contribute to an ethical environment, but such pluralistic view of ethics offers a pretty soft foundation especially at institutions that don’t have honor councils or honor codes and in classes filled with students from a range of backgrounds, majors, and attitudes. Indeed, the latter environment is exactly where cheating is most likely to occur. 

More curiously still, is that the authors themselves don’t necessarily consider the ethical issues surrounding the use of AI at all. One of the more authentic aspects of the online outrage factory is people’s genuine concern for the use of intellectual property to train AIs. It is possible to quibble that once one has published a work, one cannot necessarily control how it is used (especially once one has assigned copyright to a third party). At the same time, this isn’t to suggest that authors can’t have serious and sincere ethical concerns if they feel intellectual property that they developed is being used in ways that they find questionable. Others observe the environmental cost of the use of AI makes any purported gains in private efficiency come with a planetary price tag. By framing the use of AI as a decision of global consequence, we have the opportunity to decenter our own practices and to decouple our discussions of ethics in the classroom from the teacher-learner dyad.   

It is easy to critique the book’s casual attitude toward AI use by faculty especially in light of its insistence on ethically informed approaches to teaching (that form what the authors’ call “the opposite of cheating”) because it exposes the unsettled present concerning AI. The unsettled character of the discussions surrounding AI reflect (to me at least) the ambiguity present in many contemporary ethical conversations in the classroom, in politics, and in our communities. This does not make having ethical conversations in the classroom less valuable. In fact, it likely make them all the more important, but it also exposes a gap in this book. If talking about ethics is fundamental to teaching, the ethics of AI need to stand more clearly in this book.