The Opposite of Cheating: Teaching for Integrity in the Age of AI (Volume 4)

by Tricia Bertram Gallant, David Rettinger

In these days of an ever-expanding internet, generative AI, and term paper mills, students may find it too easy and tempting to cheat, and teachers may think they can’t keep up. What’s needed, and what Tricia Bertram Gallant and David A. Rettinger offer in this timely book, is a new approach—one that works with the realities of the twenty-first century, not just to protect academic integrity but also to maximize opportunities for students to learn.

The Opposite of Cheating presents a positive, forward-looking, research-backed vision for what classroom integrity can look like in the GenAI era, both in cyberspace and on campus. Accordingly, the book outlines workable measures teachers can use to better understand why students cheat and to prevent cheating while aiming to enhance learning and integrity.

Bertram Gallant and Rettinger provide practical suggestions to help faculty revise the conversation around integrity, refocus classes and students on learning, reconsider the structure and goals of assessment, and generally reframe our response to cheating. At the core of this strategy is a call for teachers, academic staff, institutional leaders, and administrators to rethink how we “show up” for students, and to reinforce and fully support quality teaching, learning, and assessment. With its evidentiary basis and its useful tips for instructors across disciplines, levels of experience, and modes of instruction, this book offers a much-needed chance to pause, rethink our purpose, and refocus on what matters—creating classes that center human interactions that foster the personal and professional growth of our students.

Genres: Education, Nonfiction, Teaching, Artificial Intelligence, Academic, Technology

276 pages, Hardcover

First published January 1, 2024

Reviews

[Jessica · Rating 4 out of 5]

You can tell it was started before GenAI and had to pivot. Some really great sections on teaching integrity and identity, which is more what I was hoping for—tactical advice (scaffolding, banks) is pretty commonplace.

[insect · Rating 3 out of 5]

I was disappointed that, in a book full of recommendations to ‘collaborate’ with LLMs, the chapter on ‘infusing ethics into teaching and learning’ only paid lip service to the ethical ills of AI, with one solitary sentence mentioning the theft and human exploitation involved in the creation of Generative AI.

This aside, the book has a tidy diagnosis of why students cheat (low self-efficacy, unclear course expectations, moral disengagement, focus on performance rather than learning, etc.), and some sensible (not always obvious) strategies for putting learning at the centre of classes instead of grades.

[Julie Tedjeske Crane · Rating 5 out of 5]

This was written, in part, before Chat-GPT burst onto the scene and it shows. At times, it feels like the authors tacked some prose about generative AI onto an already written draft. In spite of this, they successfully present their central thesis: Establishing a culture of academic integrity requires both explaining its importance and creating standards to preserve it. The authors acknowledge the tension between allowing students to make mistakes in a learning environment while enforcing standards of conduct--a balance that I find challenging in practice. Because the stakes are so high, any suggestion of impropriety is likely to be met with a strenuous defense, especially from law students where there could be implications related to character and fitness for the bar. Sometimes I suspect copying (or, more likely, unsanctioned collaborative work) but would have a very hard time proving it.

Another thing I like about the book is it provides advice on assessment integrity. This is an important topic when it comes to generative AI. While the suggestions in the book are necessarily broad, implementation of these principles needs to be done in course-specific ways. This is a topic I have been thinking about a lot. I want my students to ultimately adopt an attitude of co-intelligence, meaning working with AI collaboratively, but to do so they must learn some skills independent of AI use. That means introductory and advanced courses will likely take different approaches to AI use. It also means that assessments in introductory courses will need to be intentionally designed to minimize cheating by using AI. For me, this means creating the types of non-authentic assessments that I might otherwise avoid.

By situating AI-related integrity concerns in a more comprehensive framework, the book offers useful insights into how to tackle these emerging issues in education. Recommended.

[Elizabeth Cherry · Rating 3 out of 5]

This book echoes a lot of already existing best practices, but pieced together with a different focus--how to reduce students' desire and opportunities to cheat. I picked this up because students' use of AI has gotten out of control, including using AI to write personal reflections and metacognition (ironically, both listed among the best practices to avoid such cheating). To be clear, not every student is using AI, and I found it to be generally the same students who would have simply plagiarized in pre-AI days.

I did not appreciate the authors' suggestion to have students use AI to do research on the environmental problems associated with AI. Instead, having students learn to do real research on this real problem is probably better.

There were some interesting tips about flexible deadlines and oral examinations, which I will be doing more research into. But, the bottom line is that the authors really emphasize revising classes to a "mastery orientation" (also called "competency-based education"), which is extremely labor-intensive: give students the choice of their assessments and their grades, give more frequent, lower-stakes assignments, offer them multiple attempts to revise assignments, meet with all students individually...I mean, in a world in which I did not have any other obligations, including eating and sleeping, and I had 30 hours in the day, this could work? Is this aimed at faculty who work at non-teaching-intensive institutions?

I'll likely be incorporating some of the recommended items, such as oral exams, but I will never be incorporating AI into my classes until billionaires no longer exist and AI technology has become carbon-neutral.

Geez, I did not mean for this review to sound so snarky. I am burned out. There are some gems in it, and it was a useful refocusing of best practices towards a discussion of academic honesty. But it also had really high expectations for faculty and as such I think is not going to be easily implemented by faculty at teaching-intensive institutions.

[Daria Fitzgerald · Rating 3 out of 5]

The book is a solid resource for all teachers in higher education. I was happy with many of the suggestions that the authors provided regarding overall classroom integrity, trust, and transparency - particularly because I already utilize these tools in my classes. However, I was looking for very specific information on tackling/preventing AI. The authors, and rightly so, spent much of the book focusing on why AI is used to begin with. After reading the book, I’ve come to the conclusion that there has to a be different approach for each discipline, and clearly High Ed needs to take a new approach to teaching. I do intend to take a few suggestions from the authors and implement changes in my syllabus (due dates for example). Overall, I am happy I read the book and discussed it with colleagues , but still do not feel I have clear-cut answers. Maybe that is asking too much of the authors (especially since a new version of ChatGPT - GPT-5- was just this week).

[Ellen]

Interesting read but will be dispiriting to anyone looking for a quick fix (because there isn’t one). The subtitle suggests a heavy focus on AI which isn’t evident in the text; it’s much more focused on general best practices to prevent cheating or deal with it once it’s happened. Occasionally something is tacked on that relates to AI. All becomes clearer in the conclusion, where the authors write about drafting the book before chatGPT was released, then revising while chatGPT was around and rapidly being developed and adopted. I do think their ideas are useful in the context of genAI, but anyone looking for specific guidance around handling genAI in their classes will probably be disappointed.

[Jennifer · Rating 4 out of 5]

Overall great book with useful ideas and discussions about better teaching methods to discourage cheating as well as how to deal with it when it happens. They do discuss AI, but it’s clear the book had been mostly written before AI had become ubiquitous. This book could really benefit from more specifics about how to handle AI misconduct, especially in online courses. Still, a good and useful read.

[Elizabeth · Rating 5 out of 5]

This was a smart and practical way of thinking about ways of mitigating cheating in contemporary times. In addition to being full of strategies, what I especially appreciated was the approach to thinking about cheating that acknowledged that it is something that nearly everyone is inclined to do given certain conditions.

[Tesia Marshik · Rating 4 out of 5]

nothing new for me, as an educational psychologist who regularly presents on this topic already... but a great overview/ resource overall!! All educators would benefit from thinking about cheating and academic integrity in a more holistic, contextual way and priactively designing their course policies, assignments, and interactions with students with these principles in mind.

[Amanda Perry · Rating 4 out of 5]

A great read heading into my first semester teaching. Definitely going to incorporate some of their strategies!

[Mimi · Rating 4 out of 5]

A really great read which practical ideas on how education needs to pivot in the AI world. I can see myself returning to this as a reference.

[Mel Rizz · Rating 3 out of 5]

Reminded me of a lot of things we told faculty during the pandemic. some good tips but nothing new or earth shattering.

[Erin · Rating 4 out of 5]

I thought this book was great! I highly recommend if you work in education.

[Michelle Marvin · Rating 4 out of 5]

As a writing professor at a mid-sized university, I was part of two interdisciplinary summer reading groups that chose this book for discussion. Both groups were intensely focused on questions around cheating-with-AI. This book inspired excellent discussions. There isn't much that I found "new" in terms of material or pedagogical practice, but some of the graduate students in the group did. More broadly, the points the authors raise about why students cheat and how instructors can develop student-focused learning environments and learning objectives were at the heart of our discussions and takeaways. I'd recommend this book for other like-minded book groups.

[Robin · Rating 5 out of 5]

Gamechanger