Cornell University

Using, obtaining, or providing unauthorized assistance on examinations, papers, or any other academic work. Unauthorized assistance includes any aid not explicitly permitted by the instructor, whether provided by notes or reference aids, by other individuals, by artificial intelligence, or by devices such as phones, laptops, or wearable technology.

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Citing fabricated or non-existent sources, including false citations generated by artificial intelligence or other tools. Students are responsible for the accuracy and existence of all sources they cite, regardless of how those sources were generated or obtained.

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During in-class examinations no student may use, give, or receive any assistance or information not given in the examination or by the proctor. No student may take an examination for another student. Between the time a take-home examination is distributed and the time it is submitted by the student for grading, the student may not consult with any persons other than the course professor and teaching assistants regarding the examination. The student is responsible for understanding the conditions under which the examination will be taken.

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Using, obtaining, or providing unauthorized assistance on examinations, papers, or any other academic work. Unauthorized assistance includes any aid not explicitly permitted by the instructor, whether provided by notes or reference aids, by other individuals, by artificial intelligence, or by devices such as phones, laptops, or wearable technology.

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Academic integrity violations shall be evaluated against the “clear and convincing” standard of proof, which refers to a quantum of evidence beyond a mere preponderance but below that characterized as “beyond a reasonable doubt.” Clear and convincing evidence will produce in the mind of the trier of fact a firm belief as to the facts sought to be established. C. Initial Assessment Faculty members who become aware of an apparent violation shall investigate. As part of the investigation, faculty members may discuss the apparent violation with the student, including questions about the student’s process, sources of information, and techniques used in completing the work. Aft

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er this initial assessment faculty members may: Decline to move forward with the case; or Notify the student of a Primary Hearing process (II.D), during which further investigation and assessment of evidence may occur; or Offer the Accepting Responsibility process (II.E) to the student.

جست‌وجوگر شواهد: II.B–C; character chunk 680; extracted text lines 175–179

If, after investigation, possibly including discussion with the student, a faculty member believes that a student has violated the Code of Academic Integrity, the faculty member shall present the student with the charge. The charge shall include notification of a primary hearing to be held as soon as practical after the alleged infraction has come to the attention of the faculty member, but with at least one week’s notice to the student. This notification period may be shortened by the agreement of both parties. The charge shall also include notice of the availability of the Respondents’ Codes Counselor. Composition At the primary hearing the following shall be present: t

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he faculty member concerned, the student in question, and a third-party independent witness. The independent witness shall be a faculty or staff member or a student appointed by the Hearing Board Chairperson or the chairperson of the faculty member’s department. The student may also bring to the hearing an advisor and additional witnesses to testify to their innocence.

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At the primary hearing, the faculty member shall present evidence in support of the charge against the student. The student shall be given the opportunity to respond and, if they wish, to present evidence refuting the charge. The function of the independent witness is to observe the proceedings impartially, and in the event of an appeal from the judgment of the faculty member, be prepared to testify as to the procedures followed.

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After hearing the student, the faculty member may either dismiss the charge or, if there is clear and convincing evidence that the student has violated this Code, find the student guilty. If the student is found guilty, the faculty member may impose any suitable grade punishment including failure in the course. A student wishing to seek review of the decision may bring the case before the Academic Integrity Hearing Board of the faculty member’s college. Faculty members shall report all findings of violation after a Primary Hearing to the central Academic Integrity registry (II.G). The finding shall be shared with the student, and with the Academic Integrity Hearing Boards

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of the student’s college and the faculty member’s college.

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The Board may act in one or more of the following ways Find the student not guilty of the charge. Find the student guilty of the charge and Recommend to the faculty member that they reduce the penalty given. Affirm the faculty member’s decision. Recommend that the faculty member record a failing grade for the course, or for some portion of it. Recommend to the dean of the student’s college that the student be placed on probation (or the college’s equivalent). Recommend to the dean of the student’s college that the student be suspended from the University for a period of time. Recommend to the dean of the student’s college that the words “declared guilty of violation of th

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e Code of Academic Integrity” be recorded on the student’s transcript. The Hearing Board may set a date after which the student may petition the Board to have these words deleted from the transcript. Recommend to the dean of the student’s college that the student be expelled from the University. Recommend to the dean of the student’s college any other suitable action, including counseling, community service, or reprimand.

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When a student fails to uphold the principles of Academic Integrity, there is an opportunity for further education and growth. So as an alternative to the Primary Hearing and Appeals process, the Code offers the following Accepting Responsibility process. The Accepting Responsibility process does not result in a finding of guilt under the Code of Academic Integrity. In that sense, the process is akin to a warning. But the process does provide for consequences in the form of a limited penalty and mandatory completion of a workshop. Participation in the Accepting Responsibility process is voluntary for both the faculty member and the student: if either party chooses to use

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the Primary Hearing and Appeals process, that process shall take precedence. Students who choose the Accepting Responsibility process waive all appeals on the matter to the AIHB and Dean. Procedural questions may be directed to the Dean of Faculty.

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Faculty who choose to offer the Accepting Responsibility process to a student shall present the charge, evidence, and penalty to the Accepting Responsibility record keeper. The faculty member must have determined they believe the evidence meets the clear and convincing threshold. The penalty must be limited to the course work involved in the charge. The record keeper shall convey the charge, evidence, and penalty to the student. Further, the record keeper shall determine whether the student is eligible to participate in the Accepting Responsibility process. Students are ineligible if they have previously accepted responsibility or have a previous finding of guilt for an A

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cademic Integrity violation. If the student is ineligible, the record keeper shall not disclose that fact to the faculty member but instead after three business days shall notify both the student and faculty member that the student will be pursuing the standard Primary Hearing and Appeals process. If the student is eligible, the record keeper shall offer the student the choice of the Accepting Responsibility process or the Primary Hearing and Appeals process. The student shall have three business days to make the choice. A student who chooses to accept responsibility shall have three business days to reconsider and change their choice. A student who fails to make a choice

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shall be deemed to have chosen the Primary Hearing and Appeals process. The record keeper shall communicate the student’s final choice to the faculty member.

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A student who accepts responsibility must complete an educational workshop organized by the Accepting Responsibility record keeper. The workshop must be attended in person and may require additional outside work. If the student fails to complete all requirements of the workshop within two weeks, the record keeper shall notify the faculty member that the student has defaulted on the Accepting Responsibility agreement, and that the faculty member may begin a Primary Hearing and Appeals process. In that process, the faculty member is not bound by the penalty previously stated during the Accepting Responsibility process. A student who defaults will be ineligible for Accepting

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Responsibility in the future.

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The student may seek review of the decision of the primary hearing if: They believe the procedure was improper or unfair. They contest the finding of the faculty member. They believe the penalty was too strict considering the offense.

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The Academic Integrity Hearing Board shall hear all cases that come before it de novo. While the Hearing Board may recommend an increase in any penalty imposed at the primary hearing, it should consider raising the penalty, if it is the student seeking review, only in the exceptional case. The individual seeking review shall notify the chairperson of the Hearing Board of the faculty member’s college within ten working days of the primary hearing. An exception to this deadline may be granted at the discretion of the chairperson of the Hearing Board on a showing of good cause.

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Those present at the hearing shall be: The student, who has the right to be accompanied by an advisor and/or relevant witnesses. The faculty member, who has the right to bring relevant witnesses. The third-party independent witness, if a primary hearing was held. Any other person called by the chairperson. Should the student or faculty member fail to appear before the Hearing Board, the Board shall have full authority to proceed in their absence. The Board members shall hear all available parties to the dispute and examine all the evidence presented. The Board may solicit outside advice at the discretion of the chairperson. The chairperson shall preside over the hearing t

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o ensure that no party threatens, intimidates, or coerces any of the participants. The student shall have the right to present their case and to challenge the charges or the evidence. The student’s advisor may assist the student in the presentation and questioning. A Hearing Board panel shall consist of at least the chairperson, two faculty members and two students. Of those present, a simple majority shall decide the issue. The chairperson shall vote only in the case of a tie vote. The Board shall find the student guilty only if there is clear and convincing evidence indicating that the student has violated this Code.

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Honor Codes The existing school honor codes as in the College of Veterinary Medicine and the Law School are not governed by the foregoing legislation, but current versions of these honor codes must be kept on file with the Office of the Dean of the Faculty. In the case of allegations against a student enrolled in a course subject to a school honor code but registered in another college, all actions beyond the primary hearing revert to the Hearing Board of the student’s college. Graduate School Graduate students are subject to the Code of Academic Integrity. The Graduate School maintains graduate-specific procedures that are governed by the Code of Legislation of the Gradu

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ate Faculty.

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The student may appeal a decision of the Hearing Board. The appeal must be directed to the dean of the student’s college, in writing, and shall be constructed according to one or both of the guidelines established below. The appeal shall normally be submitted within 4 weeks of notification of the Board’s decision, but exceptions to this deadline may be granted by the dean on showing of good cause. If the Board’s decision involves students from more than one college, the deans involved shall consult with each other. a. Appeal of a finding of guilt. A student who has received a finding of guilt from the Board, or whose finding of guilt in a Primary Hearing was upheld by the

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Board, may appeal on one or both of the following grounds: Additional evidence which might have affected the outcome of the hearing became available following the hearing A violation of procedure by the Hearing Board that might have prejudiced the outcome of the hearing. The dean may deny the appeal or send the case back to the Hearing Board for reconsideration. b. Appeal of a penalty. The student may appeal the findings of the Hearing Board regarding penalties. The appeal shall specify the reasons why the student believes the penalty is inappropriate. After consultation with the Hearing Board, the dean may take one of the following actions: If a grade penalty has been e

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xacted (II.D.4.b.i-iii), the dean may recommend to the faculty member that the grade penalty be reduced. If another penalty has been exacted (II.D.4.b.iv-viii), the dean may modify or decline to carry out the recommended penalty. In all but the most unusual circumstances, it is the expectation that the findings and recommendations of the Hearing Board will be upheld by the dean. The dean’s decision cannot be appealed.

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Likely, your instructors have communicated their policies about student use of artificial intelligence (AI). If they have not, before you use a AI tool, make sure you ask your professors whether that is permitted and, if so, find out the parameters of permissible use. Faculty across the university vary in their policies about for what purposes, if any, students may use AI.  Faculty have different policies based on course learning objectives, so do not assume one professor’s expectation is representative of other professors’ policies. Unauthorized use of AI violates the Code of Academic Integrity . When you are permitted to use AI, you are required to follow rules of attri

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bution by citing to the tool you used. If you are uncertain if a tool is using artificial intelligence, ask your instructor.  Failing to credit an AI tool for work generated is no different than failing to acknowledge any other sources upon which you relied.  Remember that you are required to use quotation marks for verbatim generated text and to acknowledge the source for generated text that you paraphrase.  Find out from your professors their preferred form of citation. Additionally, for AI, faculty commonly require students to provide more information than is customary for other citations.  For example, you may be required to include a description of how you used the A

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I tool, how it contributed to your work, and/or how you validated the information that you obtained from AI.

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Academic Integrity: Fall 2026 Updates Changes to the code of Academic Integrity were recently approved by the Faculty Senate, effective August 24, 2026. The main changes are: “Accepting Responsibility” is now available to all in-person classes at the undergraduate and graduate levels on the Ithaca and Cornell Tech campuses; and the “Academic Integrity Registry,” a centralized reporting, recordkeeping, and communication tool for offers of Accepting Responsibility and Primary Hearings.

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Offer of a non-reportable warning in the form of an educational workshop, “ Accepting Responsibility ,” for (first-time) minor violations; to all in-person classes at undergraduate and graduate levels on the Ithaca and Tech campuses (excludes CVM & Law).

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The Accepting Responsibility process does not result in a finding of guilt under the Code of Academic Integrity. In that sense, the process is akin to a warning. But the process does provide for consequences in the form of a limited penalty and mandatory completion of a workshop.

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Central Records A central registry of Academic Integrity records shall be maintained by the university. These records shall include findings of guilt through the Primary Hearing and Appeals process, as well as records of completed Accepting Responsibility processes. Each record shall include information about the course, the charge, a summary of evidence, the decision of the faculty member, penalties, and the outcome of any appeal process. Disclosure The central registry shall disclose records (including Accepting Responsibility and Primary Hearings and Appeals) to Hearing Boards considering other penalties in relation to the same student, and to deans or associate deans

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of colleges in furtherance of legitimate educational interests. Beyond these stated situations, the central registry and the college record keepers shall not disclose violations unless specifically directed by the student or as required by law. If the student is found not in violation by the Hearing Board or Dean then all records of the case, including the report of the primary hearing, shall be expunged from the central registry, and all files or records held by the Colleges.

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The widespread availability of generative AI tools offers new opportunities of creativity and efficiency and, as with any new tool, depends on humans for responsible and ethical deployment in research and society. The Cornell University Task Force Report, Generative AI in Academic Research: Perspectives and Cultural Norms (December 2023), offers perspectives and practical guidelines to the Cornell community on the use of generative AI in the practice and dissemination of academic research.

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This report offers perspectives and practical guidelines to the Cornell community, specifically on the use of Generative Artificial Intelligence (GenAI) in the practice and dissemination of academic research. As emphasized in the charge to a Cornell task force representing input across all campuses, the report aims to establish the initial set of perspectives and cultural norms for Cornell researchers, research team leaders, and research administration staff. It is meant as internal advice rather than a set of binding rules. As GenAI policies and guardrails are rapidly evolving, we stress the importance of staying current with the latest developments, and updating procedu

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res and rules governing the use of GenAI tools in research thoughtfully over time.

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All work for the A and B exams must be the student’s own . Students may not use GenAI platforms such as ChatGPT to draft or write their A exam papers or dissertation. The use of GenAI tools that suggest improvements to students’ language and grammar must be disclosed, including prompts given, suggestions offered, and revisions made to the writing. Students must discuss with their committee their use of GenAI in any of the formative processes (such as brainstorming a lit review), reporting phases (such as producing data tables), and writing phase of their A or B exam. All students should write a reflexive commentary in an appendix to A exam papers and the dissertation on t

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heir use of GenAI, including prompts, a summary of the suggestions, and observations about how this has shaped their work.

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Adobe Firefly More information Adobe Firefly Adobe Firefly (Available with an Adobe license) Firefly allows you to generate images from text, then manipulate and edit them. Coming soon: generative voice and video content.

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Important Security Note: While many other AI tools are publicly available, including both free and paid options, these services do not provide adequate data protection for Cornell information, and are deemed as unapproved. Regardless of whether a public tool is free or paid, do not enter any data classified as moderate-risk or above into publicly-available generative AI platforms. Only use CIT-approved or locally-approved tools for sensitive Cornell data.

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Cornell AI Platform Quick info Availability: Limited Price: Free Risk: Moderate risk and below Cornell AI Platform Cornell’s AI Platform is a secure, private sandbox for accessing and experimenting with AI tools that comprises two complementary modules: AI Gateway and AI Agent Studio. AI Gateway allows you to access frontier models such as Google Gemini, Anthropic Claude, OpenAI GPT, and experiment with other models like Mistral, AWS Nova and Grok, all in a secure Cornell environment. It also enables you to power tools like Claude Code and OpenWebUI, and to seamlessly integrate AI into your custom scripts and application development. AI Agent Studio allows teams to build,

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manage, and govern AI agents and workflows that run on schedules or respond to specific triggers, while providing observability and auditing tools. The AI Platform is currently in a pilot phase, and we are actively seeking collaborators to help shape the future of AI at Cornell.

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Important Security Note: While many other AI tools are publicly available, including both free and paid options, these services do not provide adequate data protection for Cornell information, and are deemed as unapproved. Regardless of whether a public tool is free or paid, do not enter any data classified as moderate-risk or above into publicly-available generative AI platforms. Only use CIT-approved or locally-approved tools for sensitive Cornell data.

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Microsoft Copilot Basic More information Quick info Availability: Faculty, staff, and students Price: Free Risk: Low Risk and below Microsoft Copilot Basic Microsoft Copilot Basic: The university-wide “private” tool provides access to OpenAI GPT models, in addition to OpenAI’s DALL-E vision model. It enables faculty, staff, and students who are 18 years of age or older to experiment with GenAI text, image, and coding tools without storing the person’s login and chat data or using that data to train the large language models. However, it does use Microsoft’s public search engine where privacy is limited. For this reason, only enter low-risk data — information that the univ

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ersity has made available or published for the explicit use of the general public. If you need a tool that enables you to use higher levels of data, see Ideas, Requests, and Oversight of AI at Cornell for possible next steps.

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Important Security Note: While many other AI tools are publicly available, including both free and paid options, these services do not provide adequate data protection for Cornell information, and are deemed as unapproved. Regardless of whether a public tool is free or paid, do not enter any data classified as moderate-risk or above into publicly-available generative AI platforms. Only use CIT-approved or locally-approved tools for sensitive Cornell data.

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Zoom AI Companion More information Zoom AI Companion The Zoom AI Companion gives hosts and participants shareable meeting summaries and next-steps lists, “highlight reels” in recordings, catch-up options for people joining a meeting late, and more. Currently, the university is evaluating security and privacy considerations for these new features.

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Important Security Note: While many other AI tools are publicly available, including both free and paid options, these services do not provide adequate data protection for Cornell information, and are deemed as unapproved. Regardless of whether a public tool is free or paid, do not enter any data classified as moderate-risk or above into publicly-available generative AI platforms. Only use CIT-approved or locally-approved tools for sensitive Cornell data.

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Updated July 27, 2026 | Effective August 24, 2026 Issues related to courses prior to August 24, 2026, will use the pre-August 24, 2026 version Code.

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Honor Codes The existing school honor codes as in the College of Veterinary Medicine and the Law School are not governed by the foregoing legislation, but current versions of these honor codes must be kept on file with the Office of the Dean of the Faculty. In the case of allegations against a student enrolled in a course subject to a school honor code but registered in another college, all actions beyond the primary hearing revert to the Hearing Board of the student’s college. Graduate School Graduate students are subject to the Code of Academic Integrity. The Graduate School maintains graduate-specific procedures that are governed by the Code of Legislation of the Gradu

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ate Faculty.

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GAI tools pose potential privacy risks because data that is shared may be used as training data by the third-party vendor providing the service. Therefore, any information that educators are obligated to keep private, for example, under the Family Educational Rights and Privacy Act (FERPA) or the Health Insurance Portability and Accountability Act (HIPAA), should not be shared with such tools or uploaded to these third party vendors of GAI.

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GAI tools also have implications for intellectual property rights. Original research or content that is owned by Cornell University, our students, or employees should not be uploaded to these tools, since they can become part of the training data used by the GAI tools. These include student assignments, data produced in projects or research groups, data that contains personally identifiable information, data from research partners (for example, companies) that may contain proprietary information, data that could be protected by copyright, etc. If a class expects the use of GAI, it is important to make sure all students have equal access to the technology without cost barr

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iers resulting in differential access. Licensing agreements for the use of GAI tools should be provided or negotiated by the institution, while ensuring that the tools do not limit the university’s educational activities and academic freedom, respect privacy and intellectual property rights, and do not impose cost barriers or constraints. At the time of writing, the US Department of Education has issued a new report [ 3 ] encouraging the development of policies that advance learning outcomes while protecting human decision making and judgment; that focus on data quality of AI models to ensure fairness and unbiased decisions in educational applications; that understand the

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impact on equity and increase the focus on advancing equity for students. We agree with these important considerations and expect Cornell researchers and educators will contribute to these improvements in GAI. back to top

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While GAI may have selective utility in assisting in providing feedback for low-stakes formative assessment (for example in practice problems), we currently do NOT recommend it be used in summative evaluation of student work. Evaluation and grading of students is among the most important tasks entrusted to faculty, and the integrity of the grading process is reliant on the primary role of the faculty member. back to top

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We recommend instructors consider three kinds of policies either for individual assignments or generally in their courses. To prohibit the use of GAI where it interferes with the student developing foundational understanding, skills, and knowledge needed for future courses and careers. To allow with attribution where GAI could be a useful resource, but the instructor needs to be aware of its use by the student and the student must learn to take responsibility for accuracy and correct attribution of GAI-generated content. To encourage and actively integrate GAI into the learning process where students can leverage GAI to focus on higher-level learning objectives, explore c

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reative ideas, or otherwise enhance learning. Roadmap. We structure this report as follows.

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We currently discourage the use of automatic detection algorithms for academic integrity violations using GAI, given their unreliability and current inability to provide definitive evidence of violations.

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You are accountable for your work, regardless of the tools you use to produce it. When using generative AI tools, always verify the information for errors and biases and exercise caution to avoid copyright infringement. Generative AI excels at applying predictions and patterns to create new content, but since it cannot understand what it produces, the results are sometimes misleading, outdated, or false.

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If you are using public generative AI tools, you cannot enter any Cornell information, or another person's information, that is confidential, proprietary, subject to federal or state regulations, or otherwise considered sensitive or restricted. Any information you provide to public generative AI tools is considered public and may be stored and used by anyone else. As noted in the University Privacy Statement , Cornell strives to honor the Privacy Principles : Notice, Choice, Accountability for Onward Transfer, Security, Data Integrity and Purpose Limitation, Access, and Recourse.

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As we adapt to these quickly evolving tools and observe how students are using them, many of us are still formulating our own values around what this means for our classes. Cornell’s response to generative AI in teaching and learning is built around seven core principles. We invite instructors to consider these principles as they make decisions and talk with their students and colleagues about generative AI and learning: The integrity of the faculty-student relation. A commitment to experimentation, evidence and learning from experience. The centrality of faculty judgment and expertise in the classroom. Responsiveness to real student needs and uses. Recognition of both AI

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‘goods’ and ‘harms’. Respect for institutional and disciplinary heterogeneity. The extension and renewal of Cornell’s core mission and values .

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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.

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Unfortunately, it is unlikely that detection technologies will provide a workable solution to this problem. It can be very difficult to accurately detect AI-generated content. Although some instructors have reported that the tone and feel of AI-generated content is different from the student work they normally receive, in some cases it is almost undetectable from the work produced by students. Detection tools claim to identify work as AI generated, but cannot provide evidence for that claim, and tests have revealed significant margins of error. This raises a substantial risk that students may be wrongly accused of improper use of generative AI. For a more in-depth look at

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the reliability of detection technologies, we recommend the article " How Reliable are AI Detectors? Claims Versus Reality. " We currently do not recommend using current automatic detection algorithms for academic integrity violations using generative AI, given their unreliability and current inability to provide definitive evidence of violations. We believe that establishing trusting relationships with students and designing authentic assessments will likely be far more effective than policing students. For more information on promoting academic integrity in your classroom, view our strategies . References Leechuy, Jasmine (2023, August 16). " How Reliable are AI Detect

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ors? Claims Versus Reality ." The Blogsmith.

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The Code of Academic Integrity should be updated with clear and explicit language on the use of GAI, specifically indicating that individual faculty have authority to determine when its use is prohibited, attributed, or encouraged, and that use of GAI on assignments by students is only allowed when expressly permitted by the faculty member .

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Updated July 27, 2026 | Effective August 24, 2026 Issues related to courses prior to August 24, 2026, will use the pre-August 24, 2026 version Code.

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Cornell encourages a flexible framework in which faculty and instructors can choose to prohibit, to allow with attribution, or to encourage generative AI use. In addition to the CU Committee Report: Generative Artificial Intelligence for Education and Pedagogy delivered in July 2023 and resources from the Center for Teaching Innovation , check with your college, department, or instructor for specific guidance.

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Established in spring 2024 with all-college representation including faculty, staff and students, the Cornell GenAI Education Working Group, a part of the university-wide AI Advisory Council, is the central place in which new ideas, policies and practices around GenAI in the classroom are being worked out at Cornell.

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Communicating clear expectations about how you do - or do not - want students to use generative AI tools in your class is important. These icons have been developed to help you clearly and consistently communicate your expectations. They can be used on the syllabus to convey an overall course approach. They can also be used for individual assignments, allowing you to distinguish different policies for different assignments with different learning goals. Icons can be combined to more fully reflect your course policy. For example, you can combine AI-FREE + ER to indicate that AI is not permitted in your course for any assignments (AI-FREE), but it may be used to explore and

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review (ER) new concepts for practice or to promote greater understanding. Another example of using this method is AS+AT+UA to indicate that AI use is permitted for specific assignments (AS), with approved tools (AT) and attribution (UA). You can place the icons prominently on assignments and then provide more detailed explanations as needed. Please note that these icons are meant as general markers, not fully self-sufficient explanations.  They’re intended to accompany a statement of your GenAI course policy, which would provide more detail as to when, how, and whether students are permitted to use GenAI in your course, and a rationale for your policies. Click the blue

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button below to download a zipped file containing the icons (get help unzipping the file on a Mac or a PC ). Download Icons AI Course Policy Definition Color Version ANY-AI (Any Tool, Any Use, Any Time) All AI uses permitted unless otherwise specified. AT (Approved Tools Only) Instructor approved AI tools only. UA (Use with Attribution) All work produced using GenAI should be described and attributed following instructor guidance. PP (Privacy Protecting—No Proprietary Materials) GenAI use permitted, but no uploading of copyrighted or proprietary class materials unless otherwise specified. AS (Assignment-Specific) Policies on GenAI use are assignment-specific. Look to indi

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vidual assignment prompts for guidance. ER (Explore and Review) GenAI may be used to search, explore and review, and practice course concepts.  No other uses, unless otherwise specified. AI-FREE (No AI for Any Use, Unless Otherwise Specified) No AI use for any purpose, unless other specified.

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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.

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As you and your students prepare to investigate the use of generative AI tools, we recommend discussing course policies and expectations around their use, and clearly communicating with your students when and in what ways use of generative AI tools are permitted – or not. We also recommend that you consider the accessibility of generative AI tools as you explore their potential uses, especially those that students may be required to interact with. Finally, it’s important to take into account the ethical considerations of using such tools. These topics are fundamental if considering using AI tools in your assignment design. back to top

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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.

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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.

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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 refe

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rences, 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.

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Cornell is working to offer a set of generative AI tools that will meet the needs of staff doing administrative work, while providing sufficient risk, security, and privacy protections. The use of generative AI for administration purposes must comply with the guidelines of the Cornell Generative AI in Administration Task Force Report (January 2024).

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AI+AI’ : efforts to strengthen and update Cornell’s academic integrity procedures to better reflect the presence of GenAI, including better models for attributing student use of GenAI in assignments or course settings; efforts to streamline, strengthen, and reform the university’s academic integrity system; and the development of evidentiary standards and processes appropriate to the adjudication of GenAI-related violations of academic integrity.

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While ChatGPT and other LLMs can assist learners in various tasks and activities, they cannot replace human creativity, judgment, ethics, or responsibility, all of which are essential for learning. Thus, the need for AI literacy is essential for students and faculty alike. We can think of ethical generative AI literacies as the ability to understand, evaluate, and critically engage with generative AI technologies. Generative AI literacy includes skills such as recognizing when and how generative AI is used in various domains; assessing the reliability and validity of AI-generated outputs; identifying the ethical and social implications of AI applications; and creating and

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communicating with generative AI systems in ways that are appropriate to your course. Just as we adapt to the changing media environment, developing AI literacy will be an ongoing process, but one that is vital to helping you and your students become more informed and responsible users and creators of AI technologies.

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Prohibiting Generative AI Use in Your Course ( AI-FREE ) "To ensure development and mastery of the foundational concepts and skills in this course, the use of generative artificial intelligence (AI) tools is prohibited. This includes tools that help reorganize and edit your written work because the ability to self-assess, reflect on your writing process, and develop your own voice are essential in your growth as a writer. If you are unsure of any policy or any assignment-specific directions – including whether or not a tool is considered generative AI – please consult with me prior to using the technology or completing your assignment. You are responsible for verifying th

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e accuracy of citations and references used in your writing. You will be asked to verbally explain your research and writing process during in-class discussions and in a one-on-one meeting with me. In acknowledgment that AI is impacting our field, please know that appropriate and ethical use of generative AI tools will likely be a part of other courses in your academic program."

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Permitting Generative AI Use with Attribution in Your Course ( AS-UA ) "Mastering the essential, foundational concepts of this course takes effort and practice. Accordingly, the use of generative artificial intelligence (AI) tools is generally discouraged in this course, but will be permitted for select assignments. Whether or not generative AI assistance is permitted for each assignment will be explicitly communicated when that assignment is introduced. If used in any capacity for an assignment, generative AI requires proper attribution for any and all generated work. As AI-generated materials are not retrievable by graders—and there is not a person to whom the work can

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be attributed—students should attribute directly quoted text to the creator of the generative AI tool used (e.g., cite OpenAI when directly quoting ChatGPT). This attribution should be used for both in-text citations and your reference list. Example: When prompted with, “Is it ethical to use generative AI without proper attribution?” ChatGPT indicated, “Using generative AI without proper attribution can be considered ethically problematic, as it raises issues related to intellectual property, transparency, and honesty” (OpenAI, 2023). Reference OpenAI. (2023). ChatGPT (Aug 10 GPT-3.5 version) [Large language model]. https://chat.openai.com For full details on how to prope

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rly cite AI-generated work, please see the APA Style article, How to Cite ChatGPT . Regardless of whether or not the use of generative AI is permitted or prohibited for an assignment, it is critical that you adhere to our communicated course policy (and Cornell’s policy ) on academic integrity. If you are unsure of any policy or any assignment-specific directions-–including whether or not a tool is considered generative AI-–please consult the instructor prior to using the technology or completing your assignment."

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PP (Privacy Protecting—No Proprietary Materials) GenAI use permitted, but no uploading of copyrighted or proprietary class materials unless otherwise specified.

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