Policy presence
University of Pennsylvania has 4 source-backed public claims for policy presence; deterministic analysis status: unclear.
Philadelphia, United States
University of Pennsylvania has 19 source-backed AI policy claims from 6 official source attributions. Review state: agent reviewed; 19 reviewed claims. Last checked May 6, 2026.
v1 public contract
University of Pennsylvania has 19 source-backed AI policy claims from 6 official source attributions, including 19 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: University of Pennsylvania is listed as QS 2026 rank 15.
As of this public record, University AI Policy Tracker lists University of Pennsylvania as an agent-reviewed AI policy record last checked on May 6, 2026 and last changed on May 6, 2026. The record contains 19 source-backed claims, including 19 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/university-of-pennsylvania.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.
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.
Deterministic source-backed dimensions derived from this record's public claims.
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.
University of Pennsylvania has 4 source-backed public claims for policy presence; deterministic analysis status: unclear.
University of Pennsylvania has 3 source-backed public claims for ai disclosure; deterministic analysis status: recommended.
University of Pennsylvania has 5 source-backed public claims for coursework; deterministic analysis status: restricted.
University of Pennsylvania has 1 source-backed public claim for exams; deterministic analysis status: blocked.
University of Pennsylvania has 5 source-backed public claims for privacy and data entry; deterministic analysis status: restricted.
University of Pennsylvania has 3 source-backed public claims for academic integrity; deterministic analysis status: restricted.
University of Pennsylvania has 5 source-backed public claims for approved tools; deterministic analysis status: restricted.
University of Pennsylvania has 2 source-backed public claims for named ai services; deterministic analysis status: blocked.
University of Pennsylvania has 5 source-backed public claims for teaching guidance; deterministic analysis status: recommended.
University of Pennsylvania has 4 source-backed public claims for research guidance; deterministic analysis status: restricted.
No source-backed public claim about AI security review or procurement is present in this profile.
The current public tracker record does not contain claim evidence about security review, procurement, vendor approval, risk assessment, authentication, SSO, or enterprise licensing.
Coverage score measures breadth of public, source-backed coverage only. It is not a policy quality, strictness, legal adequacy, safety, or compliance score.
19 reviewed evidence-backed public claim
Other
Oryginalny dowod
Evidence 1Be transparent about the use of AI. Disclose when a work product was created wholly or partially using an AI tool and, if appropriate, how AI was used to create the work product.
Other
Oryginalny dowod
Evidence 1Penn offers access to a number of AI tools. The guidelines for protecting student privacy while using these tools are informed by the data risk classification and the privacy agreements of the tool being used.
Other
Oryginalny dowod
Evidence 1The user of AI should endeavor to validate the accuracy of created content with trusted first party sources and monitor the reliability of that content. Users are accountable for their use of content created by AI and should be wary of misinformation or "hallucinations" by AI tools (e.g., citations to publications or source materials that do not exist or references that otherwise distort the truth).
Other
Oryginalny dowod
Evidence 1Users of AI should avoid sharing personal or sensitive data with the tool and should not input moderate or high-risk Penn data as defined by the Penn Data Risk Classification, or intellectual property, without: Careful consideration and understanding of the tool's use of Penn data and the service provider's stated rights to the data, including, but not limited to whether the service provider offers the option to opt-out of using customer's data to train the AI; A contract in place to protect Penn data; and Review by Penn's Privacy Office and consultation with the Office of Information Security as coordinated by Procurement when moderate or high-risk data is involved.
Other
Oryginalny dowod
Evidence 1It is not permissible under the Health Information Portability & Accountability Act (HIPAA) or Penn Medicine policy to share patient or research participant information in connection with open or public AI tools and services, such as ChatGPT. This is because, as currently configured, such open or public tools and services can use and share any data without regard to HIPAA restrictions and other protections. Therefore, individual patient data and patient data sets (even if deidentified) may not be exposed to open or public AI tools or services, absent institutional approval.
Other
Oryginalny dowod
Evidence 1Researchers should adhere to federal or international requirements on obtaining informed consent, and Institutional Review Board approvals should be obtained prior to exposing research participant data to AI tools. Caution should be adopted when research involves the examination of high-risk data, including Personally Identifiable Information (PII) and research participant health information (both identifiable and non-identifiable) exposed to AI.
Other
Oryginalny dowod
Evidence 1Do not enter any information that could identify a student. This includes names, ID numbers, or email addresses, as well as detailed descriptions of student work or engagement in class that could be identifiable to others.
Other
Oryginalny dowod
Evidence 1Do not enter student work (e.g., papers, projects) without the student's permission, even if it is anonymized. This work is part of the student's confidential academic record.
Other
Oryginalny dowod
Evidence 1Do not require students to enter their own work into an unlicensed AI tool or use it in assignments. Unlicensed tools may be used optionally by students at the instructor's discretion but consider using a Penn-licensed tool for mandatory components of coursework to protect student data.
Other
Oryginalny dowod
Evidence 1Individual instructors determine their own policies related to acceptable student use of generative AI in coursework.
Other
Oryginalny dowod
Evidence 1Members of the Penn community should adhere to established principles of respect for intellectual property, particularly copyrights when considering the creation of new data sets for training AI models. Avoid uploading confidential and/or proprietary information to AI platforms prior to seeking patent or copyright protection, as doing so could jeopardize IP rights.
Other
Oryginalny dowod
Evidence 1While automating tasks using AI may improve operational efficiency for University Business processes, oversight and review of the use of AI and verification of its outputs for these University business processes should be in place to ensure reliability, consistency, and accuracy.
Other
Oryginalny dowod
Evidence 1As expectations may vary between classes and instructors, it is important for instructors to provide students with clear guidelines similar to the guidelines on collaboration, on the use of AI within coursework, and when and how the use of AI within a course should be cited. Disclose to students when course materials have been created with the use of AI and when AI detection software will be used in the course.
Other
Oryginalny dowod
Evidence 1In the absence of other guidance, treat the use of AI as you would treat assistance from another person. For example, this means if it is unacceptable to have another person substantially complete a task like writing an essay, it is also unacceptable to have AI to complete the task.
Other
Oryginalny dowod
Evidence 1Consult with your department leadership and your discipline's publishing standards to determine how the use of AI should be accounted for with regard to authorship in publications.
Other
Oryginalny dowod
Evidence 1Using somebody else's work without crediting the source – including generative AI — is plagiarism. Guided by the policies of faculty, instructional teams, and staff, AI-generated work should be cited like any other reference material, including how and where students used AI-generated information.
Other
Oryginalny dowod
Evidence 1Don't use AI for personal reflection or opinion-based tasks. We are interested in hearing your opinions, your stories and your thoughts. Don't rely on AI for group assignments. Using AI to complete your portion of a group project instead of collaborating with your peers is considered academic dishonesty. Don't use AI to cheat on exams or tests.
Other
Oryginalny dowod
Evidence 1Don't directly copy answers from generative AI tools and submit as your own. Don't use AI to paraphrase or rewrite plagiarized content. Don't post AI-generated discussion posts within the course community forum.
Other
Oryginalny dowod
Evidence 1At the discretion of faculty, instructional teams and staff, Wharton Academy students may use generative AI tools.
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.
6 source attribution
academy.wharton.upenn.edu
catalog.upenn.edu
cetli.upenn.edu
almanac.upenn.edu
cetli.upenn.edu
isc.upenn.edu
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