Policy presence
University of Florida has 5 source-backed public claims for policy presence; deterministic analysis status: unclear.
Gainesville, United States
University of Florida
University of Florida has 6 source-backed AI policy claims from 2 official source attributions. Review state: agent reviewed; 6 reviewed claims. Last checked May 24, 2026.
University of Florida AI policy short answer
v1 public contract
University of Florida has 6 source-backed AI policy claims from 2 official source attributions, including 6 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 24, 2026. Discovery context: University of Florida is listed as QS 2026 rank =212.
Citation-ready summary
As of this public record, University AI Policy Tracker lists University of Florida as an agent-reviewed AI policy record last checked on May 24, 2026 and last changed on May 24, 2026. The record contains 6 source-backed claims, including 6 reviewed claims, from 2 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-florida.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 Privacy claims.
- Evidence includes Research claims.
- Evidence includes Teaching claims.
- Evidence includes Academic integrity claims.
- Evidence includes Security review claims.
- No specific AI service name is highlighted by the current public claim text.
- 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.
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 Florida has 1 source-backed public claim for ai disclosure; deterministic analysis status: required.
Coursework
University of Florida has 4 source-backed public claims for coursework; deterministic analysis status: restricted.
Exams
University of Florida has 3 source-backed public claims for exams; deterministic analysis status: required.
Privacy and data entry
University of Florida has 1 source-backed public claim for privacy and data entry; deterministic analysis status: restricted.
Academic integrity
University of Florida has 2 source-backed public claims for academic integrity; deterministic analysis status: required.
Approved tools
University of Florida has 3 source-backed public claims for approved tools; deterministic analysis status: restricted.
Named AI services
University of Florida has 1 source-backed public claim for named ai services; deterministic analysis status: restricted.
Teaching guidance
University of Florida has 1 source-backed public claim for teaching guidance; deterministic analysis status: recommended.
Research guidance
University of Florida has 1 source-backed public claim for research guidance; deterministic analysis status: recommended.
Security and procurement
University of Florida 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
6 reviewed evidence-backed public claim
Privacy
UF AI guidance says users should use UF-approved or UF-provided tools for sensitive or restricted data and says restricted or sensitive data is not permitted with cloud models tagged External in NaviGator Chat.
Review: Agent reviewed
Normalized value: Use UF-approved or UF-provided tools for sensitive/restricted data; restricted or sensitive data is not permitted with External-tagged cloud models in NaviGator Chat.
原始证据
Evidence 1only use UF-approved or UF-provided tools to protect sensitive or restricted data
本地化显示 only
The page says to only use UF-approved or UF-provided tools for sensitive or restricted data.
Research
UF AI research guidance says researchers must obtain UF Institutional Review Board approval before conducting human-subject research that involves AI.
Review: Agent reviewed
Normalized value: UF IRB approval required before human-subject research involving AI.
原始证据
Evidence 1Researchers must obtain UF's Institutional Review Board (IRB) approval before conducting human subject research that involves AI.
本地化显示 only
UF says researchers must obtain UF IRB approval before human-subject research involving AI.
Teaching
UF guidance presents AI-Permitted, Some AI, and No AI as general course-level approaches for instructors to communicate generative AI expectations in syllabi or assignment instructions.
Review: Agent reviewed
Normalized value: General course AI-use approaches: AI-Permitted, Some AI, No AI.
原始证据
Evidence 1The following considerations are not formal statements or policies, but general approaches that may help address AI in your course.
本地化显示 only
The source frames AI-Permitted, Some AI, and No AI as general approaches, not formal policy statements.
Academic Integrity
University of Florida AI guidance says users should not assume AI detection tools are reliable proof of misconduct.
Review: Agent reviewed
原始证据
Evidence 1Don’t: assume AI detection tools are reliable proof of misconduct.
本地化显示 only
The UF guidance lists as a “Don’t” that users should not assume AI detection tools are reliable proof of misconduct.
Academic Integrity
University of Florida AI guidance says AI use should be cited or disclosed when required, and students are responsible for citing generative AI assistance in submissions according to academic-integrity guidelines.
Review: Agent reviewed
原始证据
Evidence 1Do: cite or disclose AI use when required.
本地化显示 only
The UF guidance says users should cite or disclose AI use when required; the student section separately states that coursework uses of generative AI should be cited in submissions according to academic-integrity guidelines.
Security Review
University of Florida AI guidance says users should not use unapproved AI applications that have not gone through UF risk assessment.
Review: Agent reviewed
原始证据
Evidence 1Don’t: use unapproved AI applications that have not gone through UF risk assessment.
本地化显示 only
The UF guidance lists as a “Don’t” that users should not use unapproved AI applications that have not gone through UF risk assessment.
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
2 source attribution
Guidelines for Use of AI at UF
ai.ufl.edu
Guidelines for Use of AI at UF
ai.ufl.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.