07/20/2026

Summer Series Pre-webinar Blog 5: Going forward with integrity, staying human

by Tricia Bertram Gallant and David Rettinger

1) How have the ways your work in academic integrity engages with generative artificial intelligence (GenAI) changed, or not changed, over the past few years?

Tricia: I guess it really hasn’t changed that much? I mean, whether students are outsourcing to a friend, a parent, a contract cheating provider or a GenAI tool, they are missing out on learning, they’re misrepresenting their knowledge and skills, and they’re denying the opportunity to build their own (brain) muscle and to exercise it. So, fundamentally it always just comes back to integrity - are you doing what you promised to do or are you pretending? So, working with students is about trying to raise their self-efficacy (their belief that they can do the work), increase their motivation for learning (even on tasks they don’t like), enhance their ability to make better ethical decisions under stress and pressure, and to improve their ability to communicate when they need help. On the other hand, my work with faculty has changed a lot to focus less on tweaking assignments or being more explicit about their integrity policies, to really rethinking their entire courses and assessments for the age of AI. And, I’ve leaned more into the notion of “close observation”; I used to say “we want to graduate people who chose to do the right thing when no one was watching.” And I still believe that’s a noble goal. However, I also believe that the temptations and opportunities to outsource are too hard to resist for many - especially those under 25 - and so we do need to structure opportunities to closely observe students demonstrating what they know and what they can do so that they don’t spend all of their energies on resisting the temptation and faculty don’t spend all of their energies on detecting whether they student succeeded.

David: I started out slowly, hoping that small classes and close connections with students would be enough to maintain academic integrity in the face of the temptations of AI. It didn’t take long to realize that this plan wasn’t going to work. I started a three-pronged approach to change. First was in my own practice. I take the approach that I work with AI so colleagues don’t have to. I’ve started using it for a variety of tasks from writing assessments to creating graphics for LinkedIn. I’m learning the strengths and pitfalls of it so I can speak with students and other faculty from knowledge, rather than hearsay. Second, I’m evaluating my goals for learning in each class. Because AI can separate product from process, as Tricia points out, the need for close observation requires me to focus assessment and learning on what’s important to know, not what’s nice to know. For example, I want my students to be good writers, but that can’t be an objective in every course, so I have to decide when it’s ok to stop assessing student writing, per se. Lastly, I’m trying to build the use of AI into some student projects judiciously. My goal is to help them develop critical thinking skills about their interactions with AI so that they can be effective users or abstainers.

2) How have you seen the discourse about GenAI and academic integrity evolve, or not, over the past few years?

Tricia: I’m still a little perplexed over the lack of evolution around the GenAI and academic integrity discourse. For example, people are still focused on old narratives about detectors (NONE of them work!), which includes blaming them for “false accusations” faced by students. When, in reality, there are effective and ineffective detectors, they might be used as one piece of a puzzle that we’re trying to solve, and poor or non-existent policies/procedures (not the detectors) are to blame if students aren’t receiving due process. Another example is the stark polarization of the language like “students aren’t cheating, they’re using the tools for learning” or “it’s not students fault if they are cheating, they’re just responding to the incentives; it’s the system’s fault.” When, in reality, it’s both/and. Students are using the tools for learning and sometimes they’re also using them to outsource. And, students are responding to incentives and they also have moral agency which they could use to resist those incentives. We are very much right now operating in a grey area with a lot of uncertainty and I think it would be helpful if, instead of exchanging in ChatGPT-like juxtapositions, we embraced the uncertainty and acknowledged the tensions so that we can accurately identify the challenges and then craft the appropriate solutions.

David: I agree with Tricia. The discourse is very binary because change is happening so fast. Simplification is good, but oversimplification is problematic, even more so when we don’t realize that we have oversimplified. A good example might be “stoplight” approaches to assessment design in which each assessment is slotted into a category with endpoints at full use of AI, including development and complete AI prohibition. These approaches were welcome and critical in the early stages of the discourse and still provide an excellent starting point for instructors and course designers just beginning their AI revision process. More recently the limitations of these approaches are emerging, including the fact that without a means of verifying compliance, a well-crafted policy is of limited use. Creating categories can also be an oversimplification, since AI use can and should change within courses, and even within a particular activity. What started as a useful simplification became oversimplification, so we must now add complexity gradually, so that change is possible for everyone.

3) What kind of intervention or approach to student use or misuse of GenAI have you used that worked well? Have you tried anything that did not work well?

Tricia: I have really found that educating students on the durable human skills that they can develop while in college does help them: 1) critique their use of GenAI tools and 2) see the value in assignments that they’re not otherwise intrinsically motivated to engage in deliberately and ethically. So, we do this exercise with students where we give them a wheel of durable human skills and then ask them to identify what skills they’re practicing when doing things like taking exams and writing essays. When we do this with them, they kind of get that lightbulb moment: they understand that there’s some reasoning behind what they might otherwise see as busy work. It’s not a magical solution, but it’s taught me that we must unhide the hidden curriculum and be more intentional, strategic and honest with students about the importance of building foundational muscles now so that they can intellectually, ethically and responsibly use GenAI tools later.

David: Tricia has mentioned process assessments and visibility, which are my main tools for approaching academic integrity generally and AI in particular. A switch to process assessments means creating a window into students’ work process and evaluating that process, rather than the final outcome on a project, paper, or exam. Sometimes this is simple, like evaluating writing based on the revision process or showing work on exams. Other times it may involve moving parts of a project to the classroom so I can have literal visibility into their process. These practical changes are paired with rhetoric that helps students to understand why the processes are important. Word problems aren’t enough to make an assessment authentic, rather students must understand why they are being asked to perform a task, and what's in it for them.

4) What has most challenged or frustrated you?

Tricia: the AI companies and their rapid pace of constant product developments, as well as their lack of care for educational integrity even while they push to ingratiate themselves into educational environments and course management system platforms. I feel like faculty, students, and staff are not exercising their human agency sufficiently to counteract those really strong and powerful forces. Yes, GenAI is out there and we can’t stop it per se, but we can choose to shape how it ends up in society (through citizen engagement with our elected officials), choose how to integrate it (or not) into the curriculum, and choose how we use it (or not) in our own work. We are not at the mercy of 6 companies - unless we choose to be.

David: Honestly, it’s the snake oil salespeople. With the advent of AI, suddenly everyone is an expert on academic integrity and misconduct, and they can solve it with this one simple trick! Spend five minutes on social media (or better yet, don’t) and you will come across someone you’ve never heard of telling you about your area of expertise. It’s exhausting.

What doesn’t frustrate me is my students’ response to all of this. Of course it’s disheartening when a student I like decides to make a bad decision, but that’s not new. I see every day that students are, as a group, responding in thoughtful ways to a very difficult change. It makes it easier to work on their behalf to remember that they are much more lost than we are.

5) What has given you hope?

Tricia: So, you’ve caught me. I call myself a hopeful pessimist, but I have to admit that the pessimistic side of me is much quicker to come to mind than my hopeful side. My hope, though, comes from laws being passed in some countries to reign in the control that the AI (and social media) firms have over humanity. My hope comes from the courts that find that maybe, just maybe, the AI company - not just the human using the tool - is responsible for spreading mis and disinformation. My hope comes from the humans creating documentaries like The AI Doc: Or How I Became an Apocaloptimist, the humans writing books like The AI Con, the humans recording podcasts like Your Undivided Attention, and the humans running Young Futures which aims to help families ensure that kids can thrive in a digital world. All of these humans are helping to counter the dominant narrative that AI is inevitable, that AI is only beneficial, that AI is the future. They create balance in the force, a balance that is very much needed right now.

David: For me, it’s been the usefulness of AI itself. Our jobs require us to think about the ways that it undermines education, ways that are real and really important, but it also allows us to imagine ways that we can provide resources for students that are currently out of reach. I’m imagining bespoke tutoring when an instructor isn’t available, infinitely patient tutors, and adaptive learning tools. AI can make quick work of customizing course materials for learners with different needs, giving me hope for an AI-aided move toward Universal Design for Learning. I, for one, welcome our robot overlords.

Tricia Bertram Gallant, PhD, is the Director of Integrity & Testing at UC San Diego and co-author of The Opposite of Cheating: Teaching for Integrity in the Age of AI.

David Rettinger, PhD, has been a leader in academic integrity, a scholar of cheating, and a professor of psychology for more than 20 years, most recently at the University of Tulsa.

The authors' views are their own.

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EDITOR’S NOTE:

This is the fifth of our blogs is to accompany the ICAI Summer Series of webinars. Tricia and David will be delivering the fifth and final webinar on ‘Going forward with integrity, staying human’ on July 23 at 12pm EST.

Here is some recommended pre-reading for the webinar:

Required reading:

Bertram Gallant, T. and Rettinger, D. (2026). TOOC podcast, Bookiversary episode 50.

Center for Humane Technology (2026). Preserving What Makes Us Deeply Human in the Age of AI

https://www.datocms-assets.com/160835/1777309468-cht-deeply-final.pdf

Recommended further reading:

Bertram Gallant, T., Rettinger, D. (2025). The Opposite of Cheating. University of Oklahoma Press (Book, podcast)

Bertram Gallant, Rettinger, Moulton (2026). TOOC podcast, episode 53 and Moulton (2026) Analog Inspiration card deck/site