Policy presence
University of Vermont has 5 source-backed public claims for policy presence; deterministic analysis status: unclear.
Burlington, United States
University of Vermont
University of Vermont has 10 source-backed AI policy claims from 7 official source attributions. Review state: agent reviewed; 10 reviewed claims. Last checked May 26, 2026.
University of Vermont AI policy short answer
v1 public contract
University of Vermont has 10 source-backed AI policy claims from 7 official source attributions, including 10 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 26, 2026. Discovery context: University of Vermont is listed as QS 2026 rank 1001-1200.
Citation-ready summary
As of this public record, University AI Policy Tracker lists University of Vermont as an agent-reviewed AI policy record last checked on May 26, 2026 and last changed on May 26, 2026. The record contains 10 source-backed claims, including 10 reviewed claims, from 7 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-vermont.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 Academic integrity claims.
- Evidence includes Privacy claims.
- Evidence includes Security review claims.
- Evidence includes Teaching claims.
- Evidence includes Research claims.
- Evidence includes AI tool treatment claims.
- Evidence includes Source status claims.
- Named AI services detected in public claims: ChatGPT, Claude, Gemini.
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
University of Vermont has 1 source-backed public claim for ai disclosure; deterministic analysis status: recommended.
Coursework
University of Vermont has 5 source-backed public claims for coursework; deterministic analysis status: restricted.
Exams
University of Vermont has 5 source-backed public claims for exams; deterministic analysis status: restricted.
Privacy and data entry
University of Vermont has 2 source-backed public claims for privacy and data entry; deterministic analysis status: restricted.
Academic integrity
University of Vermont has 3 source-backed public claims for academic integrity; deterministic analysis status: restricted.
Approved tools
University of Vermont has 3 source-backed public claims for approved tools; deterministic analysis status: restricted.
Named AI services
University of Vermont has 4 source-backed public claims for named ai services; deterministic analysis status: restricted.
Teaching guidance
University of Vermont has 5 source-backed public claims for teaching guidance; deterministic analysis status: recommended.
Research guidance
University of Vermont has 2 source-backed public claims for research guidance; deterministic analysis status: restricted.
Security and procurement
University of Vermont has 1 source-backed public claim for security and procurement; deterministic analysis status: restricted.
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
10 reviewed evidence-backed public claim
Academic Integrity
UVM's Code of Academic Integrity states that work generated by artificial intelligence is not considered created by the student and is not permitted unless the instructor expressly states otherwise.
Review: Agent reviewed
Normalized value: AI-generated work not permitted unless expressly stated by instructor.
原始证据
Evidence 1Students may not claim as their own work any portion of academic work that was not created by the student. Work generated by artificial intelligence is not considered to be created by the student and is not permitted unless expressly stated by the instructor.
Academic Integrity
UVM's Code of Academic Integrity says course expectations may vary by instructor and students must understand expectations for each assignment and course.
Review: Agent reviewed
Normalized value: Course expectations may vary by instructor.
原始证据
Evidence 1Please note: Course expectations may vary from instructor to instructor. All students have an obligation to ensure a clear understanding of the expectations associated with each particular assignment and each particular course in which the student is enrolled.
Privacy
UVM's approved AI tools page says approved AI applications are approved by data risk level, and a tool approved for one risk level is not approved for higher-risk data.
Review: Agent reviewed
Normalized value: Approved AI tools are limited by UVM data risk level.
原始证据
Evidence 1Individual tools are approved for use with data at various risk levels – Low, Moderate, High, or Restricted – as defined in the UVM Data Classification Matrix When a service is listed as approved for a risk level, that means it is not approved for higher risk levels. For example, if a service is approved for “Low to High Risk”, that means it is not approved for Restricted data such as Protected Health Information or Controlled Unclassified Information.
Security Review
UVM's approved AI tools page lists general AI tools such as ChatGPT, Claude, and Gemini as public-information-only unless another use case goes through Contract Review.
Review: Agent reviewed
Normalized value: General AI tools are public information only unless Contract Review covers the use case.
原始证据
Evidence 1All other general AI tools (ChatGPT, Claude, Gemini, etc.) Public information only Any use cases other than Public Information require Contract Review Free or purchased NOTE: Consider using already purchased tools such as Microsoft Copilot before purchasing other tools
Teaching
UVM's faculty AI guidance says there is not a mandated one-size-fits-all classroom AI approach and tells faculty to clearly communicate how their AI stance shapes course policies and assignments.
Review: Agent reviewed
Normalized value: No mandated one-size classroom AI approach; faculty should communicate course AI stance.
原始证据
Evidence 1Because of this, there isn’t a mandated, one-size-fits-all approach to AI in the classroom and, accordingly, classroom practices with AI vary. This flexibility allows you to start with your stance on AI and tailor your course appropriately—but it also means designing policies and assignments for students who are left grappling with different expectations from class to class. Each of us has an obligation to clearly communicate to students about how and why our stance on AI and other technology has shaped the course, its content, its assignment and/or its policies.
Research
UVM Graduate College AI guidance tells graduate researchers not to share confidential, proprietary, or IP-sensitive data or information with an open-source AI engine.
Review: Agent reviewed
Normalized value: Graduate AI guidance warns against sharing confidential, proprietary, or IP-sensitive information with open AI engines.
原始证据
Evidence 1Don’t share any data or information that are confidential, proprietary, or have IP implications to an open-source AI engine. Confidentiality and security of data voluntarily input to a Large Language Model (LLM) depends on the policies and practices of platform.
Ai Tool Treatment
UVM's student AI guidance tells students that the key course-context question is what the instructor has said about AI use, and that students should check syllabi and assignment instructions.
Review: Agent reviewed
Normalized value: Students should follow instructor, syllabus, and assignment AI expectations.
原始证据
Evidence 1For students, the most important question to ask in a course context is “what has my instructor said about the ways AI tools can be used in this class or assignment?” Expectations and opportunities will differ from class to class, as each set of assignments is carefully designed toward particular learning goals. Students should talk with their instructors, pay attention to policies on the syllabus, and note how assignment instructions reference technology use.
Research
UVM Graduate College AI guidance says students should seek guidance on AI platform use in class assignments, theses, dissertations, and other academic or professional writing.
Review: Agent reviewed
Normalized value: Graduate students should clarify AI use expectations for assignments, theses, dissertations, and professional writing.
原始证据
Evidence 1Students should seek guidance for the use of AI platforms in class assignments, theses, dissertations, and other genres of writing that are part of their academic and professional portfolios. Clear communication as to when and how AI can be used in academic writing will limit the risk of inadvertent misuse of AI tools.
Academic Integrity
UVM Libraries' student guide tells students to have a plan for giving credit when using generative AI and notes that APA, MLA, and Chicago have guidelines for citing generative AI.
Review: Agent reviewed
Normalized value: Students should plan attribution for generative AI use.
原始证据
Evidence 1Have a plan for giving credit. APA Style, MLA Style, and Chicago Style all have guidelines for citing generative AI. Your instructor may also ask for an appendix that includes the prompts that you provided to ChatGPT or the full transcript of your interaction.
Source Status
UVM Libraries' student guide states that UVM does not currently have a policy on ChatGPT, while instructors may have assignment-specific policies on how ChatGPT may or may not be used.
Review: Agent reviewed
Normalized value: UVM Libraries states no current UVM ChatGPT policy; instructor policies may apply.
原始证据
Evidence 1The University of Vermont does not currently have a policy on the use of ChatGPT. However, instructors may have their own policies on how ChatGPT may or may not be used in classroom assignments. If your instructor doesn't have a written policy or hasn't stated whether generative AI can be used for assignments, ask.
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
7 source attribution
AI Policy at UVM - Student Guide to AI - UVM Libraries
libraries.uvm.edu
Code of Academic Integrity | UVM Policies | The University of Vermont
uvm.edu
Effective and Responsible Use of AI: Guidance for Performing Graduate Research and Writing Dissertations, Theses, and Manuscripts for Publications
uvm.edu
Guidelines for Faculty | Artificial Intelligence | The University of Vermont
uvm.edu
Guidelines for Students | Artificial Intelligence | The University of Vermont
uvm.edu
Supported and Approved AI Tools | Artificial Intelligence | The University of Vermont
uvm.edu
Teaching and Writing in the Age of Artificial Intelligence | Writing in the Disciplines | The University of Vermont
uvm.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.
If an official source is missing, stale, moved, blocked, or incorrectly summarized, submit a source URL, policy change report, or institution correction for review. Corrections must preserve source URLs, source language, original evidence, review state, and audit history.