Policy presence
Cornell University has 5 source-backed public claims for policy presence; deterministic analysis status: unclear.
Ithaca, United States
Cornell University
Cornell University has 26 source-backed AI policy claims from 6 official source attributions. Review state: agent reviewed; 26 reviewed claims. Last checked May 6, 2026.
Cornell University AI policy short answer
v1 public contract
Cornell University has 26 source-backed AI policy claims from 6 official source attributions, including 26 reviewed claims. The record review state is agent reviewed; original-language evidence snippets, source URLs, confidence, and public JSON are preserved for citation. Last checked May 6, 2026. Discovery context: Cornell University is listed as QS 2026 rank 16.
Citation-ready summary
As of this public record, University AI Policy Tracker lists Cornell University as an agent-reviewed AI policy record last checked on May 6, 2026 and last changed on May 6, 2026. The record contains 26 source-backed claims, including 26 reviewed claims, from 6 official source attributions. Original-language evidence snippets and source URLs remain canonical, with public JSON available at https://eduaipolicy.org/api/public/v1/universities/cornell-university.json. The entity-level confidence is 95%. This tracker is not legal advice, not academic integrity advice, and not an official university statement unless the linked source is the university's own official page.
Policy signals in this record
- Evidence includes Other policy claims.
- Named AI services detected in public claims: ChatGPT.
- Disclosure, acknowledgment, citation, or attribution language appears in the public claim text.
- Teaching, assessment, coursework, or syllabus-related language appears in the public claim text.
- Privacy, sensitive-data, or security language appears in the public claim text.
Review: Agent reviewed
This reference record summarizes visible public data only. Official sources and original-language evidence remain canonical; confidence is separate from review state.
This page is not legal advice, not academic integrity advice, and not an official university statement unless a linked source is the university's own official page.
Policy profile
Deterministic source-backed dimensions derived from this record's public claims.
Review: Machine candidate
Policy profile rows are machine-candidate derived metadata. They are not final policy conclusions; inspect the linked claim evidence before reuse.
Analysis page-quality metadata is available at /api/public/v1/analysis/page-quality.json.
AI disclosure
Cornell University has 5 source-backed public claims for ai disclosure; deterministic analysis status: required.
Coursework
Cornell University has 5 source-backed public claims for coursework; deterministic analysis status: restricted.
Exams
No source-backed public claim about exam AI use is present in this profile.
The current public tracker record does not contain claim evidence about exams, tests, quizzes, or examination conditions.
Privacy and data entry
Cornell University has 5 source-backed public claims for privacy and data entry; deterministic analysis status: restricted.
Academic integrity
Cornell University has 5 source-backed public claims for academic integrity; deterministic analysis status: restricted.
Approved tools
Cornell University has 1 source-backed public claim for approved tools; deterministic analysis status: recommended.
Named AI services
Cornell University has 1 source-backed public claim for named ai services; deterministic analysis status: required.
Teaching guidance
Cornell University has 5 source-backed public claims for teaching guidance; deterministic analysis status: recommended.
Research guidance
Cornell University has 2 source-backed public claims for research guidance; deterministic analysis status: recommended.
Security and procurement
Cornell University has 1 source-backed public claim for security and procurement; deterministic analysis status: recommended.
Coverage score measures breadth of public, source-backed coverage only. It is not a policy quality, strictness, legal adequacy, safety, or compliance score.
Evidence-backed claims
26 reviewed evidence-backed public claim
Other
Cornell's committee report states that any information educators are obligated to keep private under FERPA or HIPAA should not be shared with generative AI tools or uploaded to third-party AI vendors.
Review: Agent reviewed
原始证据
Evidence 1GAI 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.
Other
Cornell's committee report states that original research or content owned by Cornell University, students, or employees should not be uploaded to AI tools, as it can become part of the AI tool's training data.
Review: Agent reviewed
原始证据
Evidence 1GAI 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.
Other
Cornell's committee report does not recommend the use of generative AI for summative evaluation or grading of student work, stating that evaluation and grading is among the most important tasks entrusted to faculty.
Review: Agent reviewed
原始证据
Evidence 1While 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.
Other
Cornell's committee report recommends three policy approaches for generative AI use: prohibit GAI where it interferes with foundational learning, allow with attribution where it supports higher-level thinking, and encourage use where it enables exploration and creative thinking.
Review: Agent reviewed
原始证据
Evidence 1We 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 creative ideas, or...
Other
Cornell's committee report discourages the use of automatic detection algorithms for academic integrity violations using generative AI, stating they cannot decisively provide evidence and could lead to unfairly identifying violations, including bias against non-native speakers.
Review: Agent reviewed
原始证据
Evidence 1We 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.
Other
Cornell's IT guidelines state that users are accountable for their work regardless of the tools used to produce it, and when using generative AI tools must always verify information for errors and biases and exercise caution to avoid copyright infringement.
Review: Agent reviewed
原始证据
Evidence 1You 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.
Other
Cornell's IT guidelines prohibit entering any confidential, proprietary, federally or state regulated, or otherwise sensitive or restricted Cornell information into public generative AI tools, as such information becomes public and may be stored and used by anyone.
Review: Agent reviewed
原始证据
Evidence 1If 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.
Other
Cornell has established seven core principles for generative AI in education: integrity of the faculty-student relation, commitment to experimentation and evidence, centrality of faculty judgment, responsiveness to student needs, recognition of both AI goods and harms, respect for institutional and disciplinary heterogeneity, and renewal of Cornell's core mission and values.
Review: Agent reviewed
原始证据
Evidence 1Cornell'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 'goods' and 'harms'. Respect for institutional and disciplinary heterogeneity. The extension and renewal of Cornell's core mission and values.
Other
Cornell recommends that faculty clearly communicate their generative AI policies in their syllabus, in assignment instructions, and verbally in class to support student learning and reduce academic integrity violations.
Review: Agent reviewed
原始证据
Evidence 1To 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.
Other
Cornell does not recommend using automatic AI detection algorithms for academic integrity violations, citing their unreliability and inability to provide definitive evidence, and the risk of wrongly accusing students.
Review: Agent reviewed
原始证据
Evidence 1We 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.
Other
Cornell's committee report recommends that the Code of Academic Integrity be updated with clear and explicit language on the use of generative AI, indicating that individual faculty have authority to determine when AI use is prohibited, attributed, or encouraged.
Review: Agent reviewed
原始证据
Evidence 1The 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.
Other
Cornell's IT guidelines endorse a flexible framework in which faculty and instructors can choose to prohibit, allow with attribution, or encourage generative AI use in education.
Review: Agent reviewed
原始证据
Evidence 1Cornell encourages a flexible framework in which faculty and instructors can choose to prohibit, to allow with attribution, or to encourage generative AI use.
Other
Cornell established the GenAI Education Working Group in spring 2024 with all-college representation including faculty, staff, and students, as the central body for developing new ideas, policies, and practices around generative AI in the classroom.
Review: Agent reviewed
原始证据
Evidence 1Established 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.
Other
Cornell developed seven standardized AI course policy icons (ANY-AI, AT, UA, PP, AS, ER, AI-FREE) to help instructors clearly communicate AI use expectations in syllabi and assignments, which can be combined for nuanced policies.
Review: Agent reviewed
原始证据
Evidence 1These 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.
Other
When generative AI use is permitted in a course, Cornell advises instructors to clarify expectations for documentation and attribution, including citing the AI tool creator (e.g., OpenAI for ChatGPT) when directly quoting AI-generated text in both in-text citations and reference lists.
Review: Agent reviewed
原始证据
Evidence 1When 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. ... 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.
Other
Cornell's Center for Teaching Innovation recommends that faculty discuss course policies and expectations around the use of generative AI tools with their students and clearly communicate when and in what ways use of such tools is permitted or not.
Review: Agent reviewed
原始证据
Evidence 1We 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.
Other
Cornell developed a set of course policy icons through the GenAI Advisory Council to help instructors clearly and consistently communicate AI use expectations to students in syllabi and assignment instructions.
Review: Agent reviewed
原始证据
Evidence 1A 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.
Other
Cornell holds students responsible for verifying the accuracy of AI-generated output and references when AI use is allowed for an assignment.
Review: Agent reviewed
原始证据
Evidence 1Discuss 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.
Other
Cornell describes measures faculty may use to evaluate potential AI-related academic integrity concerns, including requiring students to verify citations and references, requesting verification of references or methods, and informing students that they should expect to verbally explain submitted work.
Review: Agent reviewed
原始证据
Evidence 1Require 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. ... Inform and remind students that they should expect to verbally explain the work they submitted.
Other
Cornell's IT guidelines state that use of generative AI in academic research is governed by the Cornell University Task Force Report 'Generative AI in Academic Research: Perspectives and Cultural Norms' (December 2023).
Review: Agent reviewed
原始证据
Evidence 1The 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.
Other
Cornell's IT guidelines state that use of generative AI for administrative purposes must comply with the Cornell Generative AI in Administration Task Force Report (January 2024).
Review: Agent reviewed
原始证据
Evidence 1The use of generative AI for administration purposes must comply with the guidelines of the Cornell Generative AI in Administration Task Force Report (January 2024).
Other
Cornell's 'AI+AI' initiative aims to strengthen and update academic integrity procedures to better reflect the presence of generative AI, including better models for attributing student use of GenAI and development of evidentiary standards for adjudicating GenAI-related violations.
Review: Agent reviewed
原始证据
Evidence 1'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.
Other
Cornell considers generative AI literacy essential for both students and faculty, defining it as the ability to understand, evaluate, and critically engage with generative AI technologies.
Review: Agent reviewed
原始证据
Evidence 1The 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.
Other
Cornell provides sample syllabus language for an AI-FREE policy that prohibits all generative AI tools, including tools that help reorganize and edit written work, to ensure development of foundational concepts and skills.
Review: Agent reviewed
原始证据
Evidence 1To 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.
Other
Cornell provides sample syllabus language for an AS-UA policy where AI use is generally discouraged but permitted for select assignments with proper attribution, requiring students to cite the AI tool creator.
Review: Agent reviewed
原始证据
Evidence 1Mastering 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. ... If used in any capacity for an assignment, generative AI requires proper attribution for any and all generated work.
Other
Cornell's AI course policy icons include a 'PP' (Privacy Protecting) icon indicating that generative AI use is permitted but no copyrighted or proprietary class materials should be uploaded unless otherwise specified.
Review: Agent reviewed
原始证据
Evidence 1PP (Privacy Protecting—No Proprietary Materials): GenAI use permitted, but no uploading of copyrighted or proprietary class materials unless otherwise specified.
Candidate claims
0 machine or needs-review claim
Candidate claims are not final policy conclusions. They preserve source URL, source snapshot hash, evidence, confidence, and review state so the record can be audited before review.
Official sources
6 source attribution
AI & Academic Integrity | Center for Teaching Innovation
teaching.cornell.edu
AI Course Policy Icons | Center for Teaching Innovation
teaching.cornell.edu
CU Committee Report: Generative Artificial Intelligence for Education and Pedagogy
teaching.cornell.edu
GenAI in Teaching and Learning – Academic Innovation
academicinnovation.cornell.edu
Generative Artificial Intelligence | Center for Teaching Innovation
teaching.cornell.edu
Guidelines for Artificial Intelligence | IT@Cornell
it.cornell.edu
Change log
Source-check timeline and diff-style claim/evidence preview.
View the public change record for this university, including source snapshot hashes, claim review states, and a diff-style preview of current source-backed evidence.
Corrections and missing evidence
Corrections create review tasks and do not directly change this public record.
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