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Teaching in the Age of Generative AI: Academic Integrity and GenAI

An understandable initial reaction to genAI is the concern that students will use it to cheat – they will feed it a prompt or question (with some additional parameters or guidance) and submit work they didn’t create.

While this is a legitimate and serious concern, faculty do have influence over students’ motivation, behavior, and learning related to genAI. The best thing faculty can do is talk with students about genAI use and misuse, including the topic of cognitive offloading. For instance, hep students consider an example of how cognitive offloading could help them be successful (e.g., suggesting a structured timeline for studying) and how cognitive offloading could interfere with their learning (e.g., suggesting a thesis for a paper). Students then need help building a sense of self-efficacy toward the tasks of the class through scaffolded instruction; timely, personalized feedback; and opportunities to practice.

Additionally, the social nature of learning encourages open dialogue about academic integrity. For instance, Gray, Edsall, & Parapadakis (2025) suggest that “if students value their self-image as part of an intellectual community that rejects cheating for intellectual and ethical reasons, it could discourage AI-based cheating in the first place” (p. 2077). The authors acknowledge the effort required to help students adopt this stance, especially in the face of AI hype, however, these community norms and values will only be established through intentional conversation. When possible, tie ethical academic behavior to the ethical standards of your discipline, to help students understand the expectations for professional conduct that they are developing now and will serve them in the future careers.

Simply put, current AI Detectors don’t work reliably. For instance, Turnitin released an AI detector in spring 2023, touting a high level of accuracy, which they have since acknowledged generates more false positive reports than they originally claimed.

A meta-review published in 2025 by Deep, Edgington, Ghosh, & Rahaman examined peer-reviewed articles focused on AI detection that were published between 2021-2024. They flag the ongoing issues of AI detectors related to inconsistency, consent, and surveillance.

In the book The Opposite of Cheating: Teaching for Integrity in the Age of AI, Gallant & Rettinger discuss the need for assessment security related to genAI and acknowledge that AI detectors may serve a role, but should not be the sole evidence a faculty member uses if accusing student of using genAI for academic misconduct.

The CTLI’s professional judgment is that there are no current tools that can confidently provide faculty with an accurate and reliable assessment of whether or not a piece of writing was composed using genAI and that the existing tools carry high risks of false positives, which undermines trust between students and faculty.

James Lang wrote the wonderful book Cheating Lessons: Learning from Academic Dishonesty. His research indicates that humans are more likely to cheat under certain conditions and that faculty can encourage academic honesty by setting classroom conditions conducive to integrity. These principles apply to academic integrity concerns related to genAI as well as other types of cheating. And, most importantly, they also increase learning outcomes.

  • Tap into students’ intrinsic motivation for success.
  • Emphasize learning for mastery not performance.
  • Lower the stakes.
  • Set students up for a high sense of self-efficacy.

Beyond encouraging academic integrity, there are some other approaches that may minimize the inappropriate use of genAI:

  • Oral assessments. Asking students to prepare ahead and then engage in an oral assessment either 1:1 with you or as part of a class activity will demonstrate a degree of individual effort. While a student could use a genAI tool in preparation, they would still need to internalize that information in order to demonstrate their learning. This handout of 16 oral assessments may spark an idea you could adopt for your teaching.
  • OneDrive version history. Ask students to complete papers in Word Online through their VSC accounts and require their submission be a link to that file in their VSC OneDrive account that gives you access to the version history. The version history will show you a date/time-stamped list of when changes were made and if you compare a previous version to a newer version, you can see what changes were made. If a paper goes from blank to full in a matter of minutes, presumably content was copied into it. While that does not mean a student copied content from a website or genAI tool, it provides you with information from which to have a conversation about that students’ writing process.
  • Unessays. Unessays can be used in any discipline, are usually assigned at the beginning of the semester (to give time for students to brainstorm and begin work), and allow students freedom of expression to propose a project and then create a meaningful artifact representing their learning. This blog post by Cate Denial outlines her process and provides examples of some unessays students have submitted in her history classes.
  • Paper/pencil assessments. You may find it useful to use classtime to have students complete and submit paper-pencil assessments (worked problems, quizzes, exams) where you can monitor their use of outside resources.
  • Two-stage exams. During a two-stage exam, students take an exam individually, first. They then take the exam a second time, but during this step, they work collaboratively with classmates in small groups of 3-5. Students can then submit a second answer sheet, either individually or collectively, after learning from one another’s ideas and logic. The students’ exam grades are usually a combination of the scores earned during the two stages with the weighting of each portion dependent on the instructor’s goals and philosophical approach (e.g., first stage 40%/second stage 60% or first stage 75%/second stage 25%).

If you would like support to implement any of these ideas, don’t hesitate to schedule a consultation with a CTLI staff member.

Review the Academic Policies section of the current Undergraduate Catalog and Graduate Catalog for the relevant policy and procedure for how to handle incidents of cheating and academic dishonesty.

Explore related pages on the topic of Generative AI.