Teaching for Integrity in the Age of AI: From Compliance to Culture
Inspired by Chapter 2 of The Opposite of Cheating: Teaching for Integrity in the Age of AI by Tricia Bertram Gallant and David Rettinger
Academic integrity is not a checklist or compliance form. It is a living culture shaped by what we model, how we design, and the conversations we hold with our students. Gallant and Rettinger remind us that integrity is cultivated through transparency, design, and dialogue, not surveillance or punishment. The real challenge now is how to teach integrity in an age where AI is everywhere.
The U.S. Department of Education’s 2023 report, “Artificial Intelligence and the Future of Teaching and Learning”, encourages educators to treat AI as a design opportunity for advancing human-centered learning, not a threat to academic honesty. Recent data highlight the urgency of this work. According to the Higher Education Policy Institute’s 2025 Student AI Survey, 92% of undergraduates report using generative AI tools, up from 66% the year before. A Guardian report found that AI-related misconduct cases have tripled since 2023. The takeaway is clear: integrity education has to evolve alongside AI literacy.
2025 Snapshot: AI & Academic Integrity
Use and Attitudes
Over 85% of undergraduates use GenAI tools (Inside Higher Ed, HEPI 2025)
61% of students want clear, course-level AI policies
33% of students were concerned about being accused of plagiarism or cheating (Campus Technology)
While 45% believe using AI for editing is “acceptable academic support”
Institutional Responses
A survey found that less than 40% of higher education institutions have formal AI acceptable use policies. (EDUCAUSE 2025)
AI-related misconduct tripled in the UK (The Guardian 2025)
76% of universities are redesigning assessments for AI contexts (UCL Education AI Survey 2025)
Faculty Trends
A significant gap exists between student and faculty adoption: only 61% of faculty report using AI in teaching, and of those, a large majority (88%) do so minimally (ASEE AI Training 2025 led by Drs. Adita Jori and Andrew Patz).
82% of instructors use GenAI for feedback or rubric design (EDUCAUSE 2025 AI Landscape Study)
Detection tools now use watermarking and metadata tracing, but false positives remain a major concern (arXiv 2025)
Model Integrity
Students notice how we work. They learn from the way we check our sources, document decisions, and acknowledge mistakes. Modeling integrity starts with transparency.
As the EDUCAUSE 2025 AI Landscape Study notes, many universities are investing in training that helps faculty engage AI responsibly. Modeling integrity now means showing how to use AI intentionally, not avoid it.
This aligns with findings from Gu and Yan’s 2025 meta-analysis, which showed that students benefit most when teachers scaffold AI use and talk openly about it. When instructors frame AI as a learning partner, not a shortcut, students develop stronger judgment and accountability.
Make Integrity Explicit
Integrity should show up as often in our discussions as it does in our policies. When we talk about it before projects, during collaborations, and after challenges, students begin to see ethics as part of the learning process.
Tricia Bertram Gallant and David Rettinger emphasize that ethical behavior thrives when it’s designed into the experience. Singer-Freeman, Verbeke, and Barre (2025) found that students across all academic levels want clear guidance on what’s acceptable AI use. If we make expectations explicit, we replace anxiety with understanding.
A recent MDPI review on Generative AI and Academic Ethics reinforces this point, noting that while GenAI can enhance engagement and efficiency, it also increases risks to originality and ethical reasoning.
Use Clear, Simple Language (The Social Institute)
Students need to understand A.I. policies to be able to follow them. That means avoiding jargon and overly technical language.
Instead of: “A.I. assistance must align with established academic integrity principles.”
Say: “You may use A.I. for brainstorming ideas, but not for writing entire sections of code or essays.”
Establish consistent Rules Across Departments or Schools
One of the biggest sources of confusion is inconsistent enforcement when it comes to A.I. rules. Departments or schools can develop a universal A.I. guidelines that applies to all instructors, rather than allowing individual educators to set conflicting rules. Over half of students (58%) report that their school or program has a policy, but a substantial number (28%) say it differs, with some courses or professors having a policy and some not (Forbes 2025). Consider creating an instructor handbook outlining departmental or school-wide A.I. best practices to make sure they are consistently communicated to students.
Frame Integrity Positively
Instead of framing integrity around rules, frame it around growth. Students respond better when they see ethical choices as part of their professional development.
A Packback editorial on academic integrity in 2025 argues that punitive detection systems often erode trust and discourage learning. When faculty shift from surveillance to conversation, integrity becomes something students take ownership of, not something they fear.
Clarify Expectations
Ambiguity creates rationalization. In the age of AI, clarity is an act of fairness.
The National Centre for AI’s 2025 student study found that first-year students, in particular, feel confused about when and how AI use is acceptable. Faculty can address this by defining boundaries early and discussing examples. Transparency about tools, citations, and documentation helps students learn discernment.
Research from arXiv’s 2025 watermarking study adds that while detection tools are improving, they still make errors. Open conversations about what these systems can and cannot do build trust and understanding. Institutions like MIT and Duke University (22 minute mark) provide sample policy language for faculty to adapt. These statements define what “appropriate help” means and require students to cite AI contributions when used. Clarity transforms anxiety into accountability.
Normalize Conversations About Ethics
Ethics belongs in everyday learning. Conversations about bias, authorship, and data use should happen alongside technical instruction.
A 2025 study on synthetic media ethics found that students value open discussions about deepfakes and misinformation but often lack the frameworks to evaluate them. Integrating these discussions into our teaching helps students connect ethics to both academic and professional practice.
Use the Syllabus as a Moral Document
The syllabus sets the tone for integrity. Transparent grading policies, clear AI statements, and flexible revision options communicate fairness and care.
Universities are redesigning their syllabi and assessments to support “authentic learning” instead of reactive policing. The University of Melbourne’s Assured Learning model and the UCL Education AI Initiativeare leading examples, focusing on oral exams, reflective portfolios, and transparent assessment design.
Respond to Misconduct Constructively
When integrity violations occur, they can become moments for growth. Reflection, accountability, and dialogue teach more than punishment ever could.
The Packback 2025 Integrity Report encourages “growth-oriented remediation,” noting that many flagged cases stem from confusion, not intention. At Indiana University, we can uphold policy while still approaching each case as a learning opportunity.
Building a Culture of Integrity
Integrity thrives when it’s shared across the institution. Faculty, staff, and students each play a role.
The University of New South Wales’ 2025 partnership with OpenAI illustrates this shift: giving staff controlled access to ChatGPT within a responsible use framework. When universities model integrity through their own practices, students learn that ethics is not a barrier to innovation—it’s the framework that sustains it.
Final Thought
Teaching for integrity in the age of AI is about creating conditions where honesty becomes the natural choice. When we model transparency, design for trust, and engage in open dialogue, we teach more than content—we teach character.
As Amanda McKenzie, Director of Academic Integrity at the University of Waterloo, Canada, shares, “Integrity is not the opposite of cheating. It’s the presence of purpose.” When that purpose runs through our teaching, policies, and partnerships, we do more than protect academic standards. We prepare students to lead with integrity in a world increasingly shaped by AI.
Pedagogical Tips for the Start of the Semester
The first weeks of the semester are a unique window to shape not only what students will learn, but how they will learn. In STEM courses, where concepts can be abstract, skill levels vary wildly, and technologies evolve quickly, intentional, evidence-based practices can help you set students up for long-term success.
Below are a few strategies with examples and tools you can implement immediately.
Design an Inclusive, Transparent Syllabus
Evidence base: Transparent teaching research (Winkelmes et al., 2016) shows that when students understand the purpose, tasks, and criteria for success, they perform better.
Implementation tips:
Purpose statements: For every major assignment, include a short note on why it matters and how it connects to industry or future coursework.
Example: “This database schema project builds skills in relational modeling, which are directly relevant to backend software engineering interviews.”Clear expectations: Break down grading policies, late work policies, and collaboration guidelines into plain language, avoiding overly technical or legalistic phrasing.
Accessibility & flexibility: Link to tutoring labs, office hours, online learning resources, and note-taking tools. Indicate whether assignments can be resubmitted after feedback.
Create a one-page “Quick Reference” sheet covering key policies (late work, collaboration, grading)
Norm-setting: Add a “Community Norms” section that covers respectful code reviews, how to ask questions in class, and expectations for group work. In large classes, it’s vital to set expectations for respectful online discussions, effective use of the Q&A forum (e.g., checking if a question has already been asked), and guidelines for group work if applicable (e.g., conflict resolution strategies).
Establish Psychological Safety Early
Evidence base: Google’s Project Aristotle (2015) and Edmondson’s (1999) work on team learning show that psychological safety, where students feel safe to take intellectual risks, is essential for high performance.
Implementation tips:
Low stakes start: In week one, run short, open-ended coding challenges that allow multiple solutions. Make it clear that mistakes are part of the process.
Start with anonymous polls about programming experience to acknowledge the diversity of backgrounds in the room.
Instructor vulnerability: Share a personal example of a bug or failed project you learned from. This normalizes challenges in programming. In a large lecture, you can briefly mention common misconceptions students often have with a new concept, and how to navigate them.
Model Constructive Feedback: When providing feedback on early assignments (even low-stakes ones), focus on growth and learning. When addressing common errors in a large class, frame it as an opportunity for collective learning rather than pointing out individual mistakes.
Multiple communication channels: Set up a Q&A platform (InScribe) where students can post questions anonymously.
Use Early Analytics for Intervention
Evidence base: Freeman et al. (2014) found that early course engagement strongly predicts later success, allowing for timely support.
Implementation tips:
Student Engagement Roster (SER): https://ser.indiana.edu/faculty/index.html During the first week of class, consider explaining the SER to your students and tell them how you will be using it. If students are registered for your class and miss the first class, report them as non-attending in SER. It will allow outreach that can help clarify their situation. Here’s a sample text you could put into your syllabus:
This semester I will be using IU’s Student Engagement Roster to provide feedback on your performance in this course. Periodically throughout the semester, I will be entering information on factors such as your class attendance, participation, and success with coursework, among other things. This information will provide feedback on how you are doing in the course and offer you suggestions on how you might be able to improve your performance. You will be able to access this information by going to One.IU.edu and searching for the Student Engagement Roster (Faculty) tile.Use Canvas Analytics:
Identify struggling students. “Submissions” allows you to view if students submit assignments on-time, late, or not at all.
See grades at a glance. “Grades” uses a box and whisker plot to show the distribution of grades in the course.
See individual student data. “Student Analytics” shows page view, participations, assignments, and current score for every student in the course.
Track early submissions: Note which students complete the first assignments or attend early labs
Personal outreach: Email or meet with students who are slipping to connect them with tutoring, peer mentors, or study groups.
Positive nudges: Celebrate early wins (e.g., “I noticed you submitted the optional challenge problem. Great initiative!”).
Proactive Outreach (with TA Support): If you identify students who are struggling, send personalized emails offering support and directing them to available resources (e.g., tutoring, office hours with TAs). Consider delegating some of this outreach to TAs in large courses.
Announcements Highlighting Resources: Regularly remind the entire class about available support resources, study strategies, and upcoming deadlines through announcements.
Key Implementation Strategies for Success
Start Small and Build Don’t attempt to implement all strategies simultaneously. Choose 2-3 that align with your teaching style and course structure, then gradually incorporate additional elements.
Leverage Your Teaching Team In large courses, TAs are essential partners. Invest time in training them on consistent feedback practices, student support strategies, and early intervention protocols.
Iterate Based on Data Use student feedback, performance analytics, and your own observations to refine your approach throughout the semester. What works in one context may need adjustment in another.
Maintain Connection at Scale Even in large courses, students need to feel seen and supported. Use technology strategically to maintain personal connection while managing the practical demands of scale.
Conclusion
By implementing these research-backed strategies, faculty can create learning environments where diverse students thrive, engagement remains high, and learning outcomes improve significantly.
The investment in implementing these practices pays dividends not only in student success but also in teaching satisfaction and course sustainability. As you prepare for the new semester, consider which strategies best align with your course goals and student population, then take the first step toward transforming your large enrollment course into a dynamic, supportive learning community.
Remember: even small changes, consistently applied, can create significant improvements in student learning and engagement. Start where you are, use what you have, and do what you can to create the best possible learning experience for your students.
References
Winkelmes, M. A., Bernacki, M., Butler, J., Zochowski, M., Golanics, J., & Weavil, K. H. (2016). A teaching intervention that increases underserved college students’ success. Peer Review, 18(1/2), 31–36. Association of American Colleges and Universities.
Edmondson, A. C. (1999). Psychological safety and learning behavior in work teams. Administrative Science Quarterly, 44(2), 350–383. https://doi.org/10.2307/2666999
Google Inc. (2015). Project Aristotle: Understanding team effectiveness. Retrieved from https://rework.withgoogle.com/intl/en/guides/understanding-team-effectiveness
Freeman, S., Eddy, S. L., McDonough, M., Smith, M. K., Okoroafor, N., Jordt, H., & Wenderoth, M. P. (2014). Active learning increases student performance in science, engineering, and mathematics. Proceedings of the National Academy of Sciences, 111(23), 8410–8415. https://doi.org/10.1073/pnas.1319030111
Evidence Based Research Supporting the Use of PlayPosit in classes
PlayPosit https://app.teaching.iu.edu/tools/playposit is an interactive teaching tool/application used to make interactive videos, also known as bulbs. The application can be integrated into the Canvas using your own videos or by extracting videos from other sources, such as YouTube or TED Talks, and convert them into an interactive, topic- and student-focused mini-lessons by adding questions at appropriate time-points in the video. Educators may also prepare video clips from longer recorded lectures and encourage student engagement by providing context or additional information through text and images on slides, inserting questions to check for understanding, including discussion and reflection questions, giving pre-recorded feedback as they see fit, and even incorporating polling. PlayPosit videos pause at certain intervals chosen by the instructor to give students an opportunity to respond.
PlayPosit settings allow for multiple playback options, including allowing students to rewind, fast forward or retake the activities. The instructor may also incorporate instant feedback after each question; so, students not only see whether they answered correctly but also understand the rationale. This feedback enables instructors to modify instructional activities midstream in light of their effectiveness, impact, and value. Because formative evaluations are designed to guide the teaching process – and are not used as outcome indicators – they are generally individualized evaluations that are under the control of the instructor and target specific instructional topics, issues or concerns.
van der Meij, H., & Bӧckmann, L. (2021). Effects of embedded questions in recorded lectures. Journal of Computing in Higher Education, 33(1), 235–254. https://doi.org/10.1007/s12528-020-09263-x.
Researchers sought to examine the effectiveness of providing “open-ended embedded questions” in recorded video lessons to better prepare students for upcoming in-class topics.
Student surveys, user logs, and knowledge tests showed that learners “engaged significantly more with the embedded questions lecture” and showed “significantly higher” average scores on those topics. Researchers concluded that embedded questions “can increase the effectiveness of online video-recorded
lectures.”
Lewandowski, H.J., Pollard, B., West, C.G. (2020). Using custom interactive video prelab activities in a large introductory lab course. 2019 Physics Education Research Conference Proceedings. https://doi.org/10.1119/perc.2019.pr.Lewandowski.
As part of a broader redesign of a large introductory physics course at the University of Colorado Boulder, researchers developed pre-lab videos with embedded PlayPosit questions to help students better prepare for in-person lab activities.
Researchers found that 90% of students completed the pre-lab video modules, with the “vast majority of students spending a nontrivial amount of time engaging with each question.” 80% of students felt the activities prepared them well for in-person lab tasks.
Sherifi, D., Jia, Y., Hunt, T. J., & Ndanga, M. (2023). Evaluation of a PlayPosit guided group project’s impact on student engagement in an undergraduate course. Discover Education, 2(1), 32. https://doi.org/10.1007/s44217-023-00057-8
Students appreciated that PlayPosit activities were enjoyable and different from other learning resources. Eleven students expressed that PlayPosits were “enjoyable, “short”, “quick”, “not overbearing”, “not drawn out or boring”, “made learning fun”, “a unique way of learning”, and “kept students interested”.
PlayPosit increased the teaching presence of the professor by virtue of having multiple touch points with the material focused on relevant tasks.
PlayPosit contributed to better interaction of the students with the course content, and as per their comments, was helpful and beneficial, as well as interesting and attractive. Furthermore, students were more attentive to the other course videos and recordings.
Karpicke, J.D. (2012). Retrieval-based learning: Active retrieval promotes meaningful learning. Current Directions in Psychological Science, 21(3), 157–163. https://doi.org/10.1177/0963721412443552.
Researchers compared the effects of different study strategies on student learning between three test groups in the same course: one studying concepts by rereading materials with no recall activities, one mostly rereading with some recall activities, and one reading just once with more recall activities.
While surveyed students believed rereading would be the most effective study strategy, those who simply reread the same materials with no recall activities performed poorest on assessments. Those who practiced just one retrieval activity “doubled long-term retention,” and those who practiced multiple retrieval activities showed even larger gains
For more information on how to integrate PlayPosit into your course, please visit https://app.teaching.iu.edu/tools/playposit
Getting Started with Gradescope
Gradescope is an online grading platform that streamlines the grading process for assignments, quizzes, and exams. It offers features such as AI-assisted grading, rubric-based assessment, and detailed analytics, making it a valuable tool for both instructors and students. Some of those features include:
Customized Rubrics: Instructors can create customized rubrics within Gradescope that outline specific grading criteria and expectations for assignments. By tailoring rubrics to align with learning objectives and student skill levels, instructors can provide more personalized feedback that addresses individual strengths and areas for improvement.
Individualized Feedback: Gradescope allows instructors to provide individualized feedback on student submissions. Instructors can leave comments directly on student work, offering personalized guidance, suggestions, and encouragement to support each student's learning journey.
Assignment Variations: Gradescope allows instructors to create multiple variations of certain assignment types, each with its own set of questions or parameters. This feature enables instructors to provide students with personalized assignments based on factors such as skill level, learning style, or individual interests.
Flexible Grading Options: Gradescope offers flexibility in grading options, allowing instructors to choose between manual grading, automated grading (using, or a combination of both. This flexibility enables instructors to adapt grading methods to suit the needs of different assignments, courses, and student populations.
Grade Adjustments: Instructors can easily adjust grades within Gradescope based on individual circumstances or extenuating factors. Whether accommodating accommodations for students with disabilities, considering exceptional circumstances, or recognizing exceptional effort, Gradescope allows instructors to personalize grading decisions while maintaining consistency and fairness.
Learning Analytics: Gradescope's analytics dashboard offers insights into student performance trends, allowing instructors to identify patterns, common misconceptions, and areas for improvement. This data-driven approach enables instructors to personalize grading strategies and instructional interventions to address specific learning needs.
Student Engagement Tracking: Gradescope allows instructors to track student engagement with assignments and assessments, including submission times and completion rates. By monitoring student activity, instructors can identify students who may need additional support or encouragement and tailor grading strategies accordingly.
Additional resources:
Getting Started with Gradescope: https://www.gradescope.com/get_started
Programming Assignments in Gradescope https://help.gradescope.com/article/ujutnle52h-instructor-assignment-programming
Trying Something New In Your Course
Here are a few ideas to consider when trying something new adapted from Tips for Teachers.
Can I improve something I am already doing?
Before looking to make wholesale changes to your teaching, based on your reflections, identify practices you already do and look for ways to improve them. This should take less time and effort and give you a platform of success upon which to make further changes in the future. For example:
Instead of several worked examples that you have to whiz through, choose one or two that you have thought carefully about. Spend time going through them. Consider modelling them in silence first, and then using carefully considered self-explanation prompts/questions to give students a better opportunity to understand the process.
How will I know if the idea works?
How are you going to know if the idea has been a success or not? The more objective the measure, the better. For example:
If you are looking to boost your participation ratio by using tools like (Top Hat or PlayPosit), track the number of times you see responses from all students.
What will I have to stop doing?
This is the question that gets asked the least, and yet is so important. Trying something new may mean you have to no longer do something else. This plays out in two ways: A new idea in the classroom may mean you have less lesson time to do something else. Is that a sacrifice worth making? Planning a new idea may mean you have less planning time to work on something else. Is that a sacrifice worth making?