AI and Academic Integrity

Intentional Design and Communication of Policies

Academic integrity has always required faculty and students to navigate questions about appropriate and ethical academic practices. While student use of generative and agentic AI does not automatically constitute an academic integrity concern, unauthorized use of AI is considered a form of academic misconduct at Kennesaw State University. Whether and how a student may use AI depends on the expectations and requirements set within a course, and these expectations are usually driven by the course learning objectives, disciplinary norms, course levels, and faculty beliefs and values about teaching and learning. As a result, academic integrity in the age of AI requires a series of intentional decisions, transparency, and regular communication with students.

This webpage provides a curated list of resources organized by key decision points faculty may face as they design courses, assignments, and AI policies.

Key Decision Points and Resources

Promoting Course Learning Objectives
In what situations might student use of AI undermine the learning objectives of my course and in what situations might student use of AI support the learning objectives of my course, if at all? (e.g., What skills and knowledge do my students need to develop and practice on their own, without the use of AI? What aspects of student learning could be supported or enhanced through the use of AI?)
Communicating the Course AI Policy
How and when will I effectively communicate my course-level AI policy to my students?
- Syllabus Statements on AI Use (KSU Digital Learning Innovations)
- AI Teaching Strategies: Developing Your Course Policy (The Ohio State University Teaching and Learning Resource Center)
- Classroom Conversations about AI: A Faculty Guide (Penn State)
- AI Assessment Scale
- USG Student Guide to Generative AI Literacy (Topic: Accountability)
Motivating Students for Learning
How can I motivate my students to learn with integrity?
- Course and Assessment Design and AI (Center for the Advancement of Teaching, Learning, and Online Education, University at Albany)
- The Opposite of Cheating: Teaching for Integrity in the Age of AI
- Rethinking Assessments for Academic Integrity using Generative AI (KSU Digital Learning Innovations)
- TILT: Assignment Design for Transparency in Learning and Teaching Souvenir (KSU Center for Excellence in Teaching and Learning)
Addressing Academic Misconduct
How should I best address instances of academic misconduct relating to AI use?
- KSU Office of Student Conduct and Academic Integrity Resolution Process
- “Discussing AI with Your Students” (Cornell University)
- Respond to Suspicions of Student Cheating with AI (University at Albany)
- How Do I Address Suspected Student Misuse of GenAI Tools? (University of Massachusetts at Amherst)

Sources

Bali, M. (2024). Cake-making analogy for setting generative AI guidelines/ethics. In Buyserie, B., Thurston, T. N., Rivera-Dundas, A., Söken, A., Ofakihevahanoa Fotu, B., Litts, B. K., ... & Daniels, T. (2024). Teaching and generative AI: Pedagogical possibilities and productive tensions. Utah State University.
Cornell University, Center for Teaching Innovation. (n.d.). Discussing AI with your students.
Kennesaw State University, Academic Affairs. (n.d.). Syllabus statements on AI use. Digital Learning Innovations, Faculty Development.
Kennesaw State University, Department of Student Conduct and Academic Integrity. (2024, October). Academic integrity resolution process [PDF].
Gallant, T. B., & Rettinger, D. A. (2025). The opposite of cheating: Teaching for integrity in the age of AI (Vol. 4). University of Oklahoma Press.
Lovett, M. C., Bridges, M. W., DiPietro, M., Ambrose, S. A., & Norman, M. K. (2023). How learning works: Eight research-based principles for smart teaching. John Wiley & Sons.
Perkins, M., Roe, J., Furze, L., & MacVaugh, J. (2024). The artificial intelligence assessment scale (AIAS). Journal of University Teaching & Learning Practice, 21(5).
Kennesaw State University, Digital Learning Innovations. (n.d.). Rethinking assessments for academic integrity using generative AI [Online learning module].
The Ohio State University Teaching and Learning Resource Center. (n.d.). AI teaching strategies: Developing your course policy.
The Pennsylvania State University. (2023). Classroom conversations about AI: A faculty guide [PDF].
University at Albany. (n.d.). Course and assessment design and AI. Center for the Advancement of Teaching, Learning, and Online Education.
University at Albany. (n.d.). Respond to suspicions of student cheating with AI. Center for the Advancement of Teaching, Learning, and Online Education.
University of Massachusetts Amherst, Center for Teaching & Learning. (n.d.). How do I address suspected student misuse of GenAI tools?
USG Student Guide to GenAI Literacy (University System of Georgia Office of Teaching and Learning Excellence; request/download access)
Vee, A., Watkins, M., & Bruff, D. (2026). The Norton guide to AI-aware teaching. WW Norton & Company.