The barrage of daily news about Gen AI and student cheating is overwhelming. Although taking measures to prevent cheating can make it harder for students to cheat, new tools are emerging at record speed. There is a call for faculty to reinvent their courses to either embrace students’ use of AI or create assessments that are “AI-proof.” No matter your approach to AI, academic integrity still plays an integral role in teaching and learning. Instructors should include ethics and academic integrity as a learning goal for students to enhance the learning environment and support the ethical development of students. In this article, I rely on empirical research and best practices in teaching to offer three recommendations regarding academic integrity:
Teach students about ethics and what it means to have integrity: Well before the launch of Gen AI, students were consistently found to lack a broad understanding of what constitutes cheating (e.g., Carpenter et al., 2010). Further, students and faculty often do not share a similar definition of “cheating” (Watson et al., 2014). Syllabus statements regarding cheating or academic integrity do not work by themselves (Staats & Hupp, 2012). A direct and open discussion about cheating at the beginning of a course is essential given the myriad tools now available to students. Think about common mistakes students make in your class and address those specifically with examples. Review the resources available to students (including people, AI tools, tutors, or none), and how they should use and disclose their use of those resources for each assignment. For example, can students work together on homework? If so, what does “work together” look like? Extend the discussion of academic integrity to ethics in psychological science by exploring topics such as research misconduct, informed consent, human and animal welfare, research collaboration and authorship, and ethical writing and statistical practices (see Corts & Tatum, 2019). Have students practice ethical decision-making and be sure to include examples regarding academic work, such as plagiarism and cheating.
Connect assignments and assessments to learning goals, focusing on student mastery: A significant concern with AI is that students will offload critical learning tasks or fail to master necessary skills needed for upper-level courses. When students perceive class goals to be focused on mastery and begin to internalize those goals, they are less likely to engage in academic dishonesty (Krou et al., 2021). Communicate how each assignment or assessment aligns with a learning goal of the course. For example, if you are teaching a writing intensive course in which one learning goal is to practice editing a paper, explain to students why they should not use AI for this assignment. If students are allowed to use AI, try incorporating a tool such as the AI Assessment Scale (AIAS; Furze, 2024), a colored-coded tiered scale of use that represents how much a student can rely on AI for any individual assignment. For example, if students are allowed to use AI for planning, brainstorming, and outlining (“AI planning” in the AIAS scale), be sure to explain how they should develop and refine those ideas on their own. APA provides publishing policies that can assist you in teaching students about AI disclosure (American Psychological Association, n.d.).
Review your institution’s academic integrity policy with students and follow it: In one study, 31% of faculty did not understand the process for reporting students who cheat (Blau et al., 2017). In interviews with students who were reported for cheating, Beasley (2014) found that many students were not familiar with the policies or the consequences of academic misconduct and believed that if the instructor had discussed the policy and reminded them, it would have deterred them from cheating. Reviewing the academic integrity policy and the consequences for violating it, and enforcing the policy reduces student cheating (Boehm et al., 2009). A recent article on the hidden curriculum of student conduct policies in higher education suggests that these policies may differentially impact first generation college students (Mowreader, 2025). By reviewing the policy, you are ensuring that all students have the information they need to be successful. Be sure to discuss, explain, and remind students about integrity throughout your course.
In their new book The Opposite of Cheating, Bertram Gallant and Rettinger (2025) assert that academic integrity is fundamentally a teaching and learning issue that can be partially solved with good pedagogy and clear communication between instructors and students. I highly recommend this book as it provides practical teaching and assessment strategies for instructors. And, for some quick tips to update your assignments with regard to AI, see Darby (2025).
Holly Tatum is the Mary Sabel Girard ‘34 chair in psychology at Randolph College where she also served as the director for the Center for Student Research. She teaches courses in introduction to psychology, research methods, experimental psychology, testing and measurements, personality, health psychology, and gender. Tatum conducts basic and applied research on student cheating, academic integrity, and honor codes. She recently completed a grant-funded experiment investigating risk-taking and cheating among high school students using a virtual reality paradigm. She also writes about and presents work on the scholarship of teaching and learning and high-impact practices such as capstone courses in psychology. In 2019, she co-wrote Ethics in Psychological Research: A Practical Guide for the Student Scientist. Tatum earned her PhD in experimental psychology from the University of Tennessee.
