To best support student learning and reduce violations of academic integrity, be sure to clearly communicate your policies regarding the use of generative AI in your syllabus, in assignment instructions, and verbally in class. When generative AI is permitted, clarify expectations for documentation and attribution, as well as what aspects of the work should be produced by the students themselves. Discuss with students how generative AI output can be incorrect or problematic and that they are responsible for verifying the output and references if AI use is allowed for an assignment.

A set of course policy icons has been developed by the GenAI Advisory Council to help Cornell instructors communicate with students about appropriate AI use for different courses and assignments. They are downloadable and can be incorporated into your syllabus or assignment instructions, to help provide clear guidance to students about course expectations.

Elizabeth Karns, in her role as a Provost Fellow and in consultation with University Counsel, has developed the following recommendations for instructors on communicating expectations and addressing possible academic integrity violations.

• Prevention
• Violation Awareness
• Evidence of Violation
• Crediting Generative AI in an Assignment
• Detecting AI Generated Content
• References

Prevention

Set Clear Expectations in the Syllabus 

To reduce the ambiguity surrounding appropriate uses of generative AI, faculty should clearly define for students both your expectations and their responsibilities around using generative AI tools.

Here are a few recommended steps you can take. Making these explicit can help clarify expectations for students.

• Require that students verify the accuracy of all citations and references they include in their work.
• Request that students provide verification of references or methods, with a student's response determining whether a formal academic integrity notification is warranted.
• Clearly limit allowable methods, tools, or references, and specify that deviations from these restrictions could constitute evidence of potential integrity violations.
• Inform and remind students that they should expect to verbally explain the work they submitted. 

Canvas Course Module on Academic Values & Integrity

Karns has also developed a Canvas Course module to help students recognize academic integrity concerns that are specific to your course. This resource is designed to encourage students to reflect on their personal values and learning goals within the context of your course.

back to top

Violation Awareness 

Inquire About Unusual Submissions

Instructors should use their initial impressions of potential violations involving generative AI as a starting point for deeper inquiry, supported by objective and verifiable evidence, before proceeding with academic integrity hearings. A conversation with the student in question may garner new information that clarifies the problem.

Instructors can follow up, in person or by email, on any submissions that include unexpected methods, references, or approaches that appear to surpass course expectations. Students can be asked to explain how they arrived at such content. If the explanation raises concerns, the instructor can document the concern and proceed with further investigation.

back to top

Evidence of Violation

Gather Objective Evidence

Faculty should follow up their initial concerns about a generative AI-related violation with tangible evidence, such as:

• Submitted work includes citations or references to content that does not exist or cannot be verified.
• Inclusion of advanced references, methods, or approaches that exceed the scope of the course or seem implausible without external assistance.
• The student is unable to verbally explain, expand upon, or defend their submitted work when queried.
• The work is inconsistent with previously submitted work or preparatory steps submitted by the student, such as outlines, drafts, or prior discussions during office hours.

Evidence of an academic integrity violation needs to meet a clear and convincing standard. This is a medium level of proof – it’s above the “more likely than not” and below the “beyond a reasonable doubt.” The instructor will need to show that the allegation is far more likely to be true than false. It does not require first-hand observation of an act. The use of multiple forms of evidence helps to reach this standard of evidence.

back to top

Crediting Generative AI in an Assignment 

When using material generated from an LLM in course materials or in assignments that students submit, transparency is key, and these instances should be properly referenced. The APA style guide, MLA style guide, and Chicago Manual of Style include recommendations and examples for citing LLM-generated materials that you can share with students.

• For example, consider:
  • Including an in-text citation with reference to the LLM model, i.e. include text in quotes and reference the author, i.e. (OpenAI, 2023).
  • Asking students to include an AI-generated source in the methods section of their research paper.
  • Having students provide the text or prompt they used for the LLM to generate a response and include what LLM model, date, and version they used.

Similarly, if instructors choose to use LLMs to help in the preparation of course materials, such work should be acknowledged and attributed.

back to top

Detecting AI-Generated Content

Recent advancements in algorithmic systems designed to evaluate whether written work was composed by humans, AI, or both, have motivated new campus evaluations of AI detectors, such as Pangram v.4. We acknowledge the desire to seek technology-based solutions to identify inappropriate use of AI tools in student work. 

Unfortunately, after exploring the research and conducting our own testing, we find it unlikely that current detection technologies will provide a workable solution to the problem of author identification. Detection tools claim to identify work as AI-generated, but they cannot provide evidence for that claim. Research reveals continued margins of error — including continued false positives. This raises a substantial risk that students may be wrongly accused of an improper use of AI, with certain populations carrying a higher burden of false positives than others (Hadra, 2026).

There is a lack of empirical evidence or studies of use from within the context of higher ed. Studies that show the safe, fair, and effective integration of AI detectors in learning communities are limited. Additionally, recent lab studies show a lack of substantial, varied, and verified data sets. Finally, “humanizers,” or software designed to edit AI text to avoid detection by mimicking human writing (ie. through ‘burstiness’ or ‘perplexity’) - are readily available to students, decreasing the likelihood that AI work would be flagged, or that detectors would act as deterrents for AI use.

We currently do not recommend using automatic detection algorithms for academic integrity violations that flag the improper student use of AI.

Note that AI detection scores are not necessary as evidence of academic misconduct. For more information on what kinds of data might support a claim of academic integrity violation stemming from a students' use of AI, explore our resources under evidence of violation.

For more information on promoting academic integrity in your classroom, view our strategies.

back to top

References

Hadra, M., Cambridge, K. & Mesbah, M. (2026). Evaluating the accuracy and reliability of AI content detectors in academic contexts. International Journal for Educational Integrity 22(4). https://doi.org/10.1007/s40979-026-00213-1

Jabarian, Brian and Imas, Alex, Artificial Writing and Automated Detection (August 26, 2025). Available at SSRN: https://ssrn.com/abstract=5407424 or http://dx.doi.org/10.2139/ssrn.5407424

Van Vlasselaer, M., Van Droogenbroeck, F. & Spruyt, B. (2026). Who wrote this? Evaluating the reliability of AI detection tools in higher education. International Journal for Educational Integrity 22(16). https://doi.org/10.1007/s40979-026-00226-w

back to top