Change log

Cornell University

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Review: Agent reviewedLast checkedMay 6, 2026Last changedMay 6, 2026Claims26Sources6

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Cornell University currently has 26 source-backed claim records and 6 official source attributions. Latest tracked changed date: May 6, 2026.

This tracker 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.

11 # Cornell University AI policy record
2+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.
3+Evidence (en, deeacd6dce5b): 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.
4+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.
5+Evidence (en, deeacd6dce5b): 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.
6+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.
7+Evidence (en, deeacd6dce5b): 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.
8+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.
9+Evidence (en, deeacd6dce5b): 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 creative ideas, or...
10+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.
11+Evidence (en, deeacd6dce5b): 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.
12+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.
13+Evidence (en, b1a7c670aa0b): 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.
14+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.
15+Evidence (en, b1a7c670aa0b): 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.
16+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.
17+Evidence (en, 9e356eb6cdc5): 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 'goods' and 'harms'. Respect for institutional and disciplinary heterogeneity. The extension and renewal of Cornell's core mission and values.
18+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.
19+Evidence (en, dc5bf374079a): 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.
20+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.
21+Evidence (en, dc5bf374079a): 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.

Claim changes

26 claim records

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.