Denver, United States

University of Denver

University of Denver has 5 source-backed AI policy claims from 2 official source attributions. Review state: agent reviewed; 5 reviewed claims. Last checked May 23, 2026.

University of Denver AI policy short answer

v1 public contract

University of Denver has 5 source-backed AI policy claims from 2 official source attributions, including 5 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 23, 2026. Discovery context: University of Denver is listed as QS 2026 rank 1001-1200.

Citation-ready summary

As of this public record, University AI Policy Tracker lists University of Denver as an agent-reviewed AI policy record last checked on May 23, 2026 and last changed on May 23, 2026. The record contains 5 source-backed claims, including 5 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-denver.json. The entity-level confidence is 93%. 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.

Claim coverage5 reviewedSource languageenPublic JSON/api/public/v1/universities/university-of-denver.json

Policy signals in this record

  • Evidence includes Privacy claims.
  • Evidence includes Academic integrity claims.
  • Evidence includes Teaching 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.
  • Privacy, sensitive-data, or security language appears in the public claim text.
Policy statusReviewed evidence-backed recordReview: Agent reviewedEvidence-backed claims5Reviewed5Candidate0Official sources2

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.

Coverage score90/100Coverage labelbroad public coverageReview: Machine candidateAnalysis confidence78%

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.

Approved tools

No source-backed public claim identifying approved or licensed AI tools is present in this profile.

The current public tracker record does not contain claim evidence that identifies institutionally approved, licensed, procured, or enterprise AI tools.

Not MentionedMachine candidateConfidence0%Evidence0Sources0

Named AI services

University of Denver has 1 source-backed public claim for named ai services; deterministic analysis status: recommended.

RecommendedMachine candidateConfidence79%Evidence1Sources1

Research guidance

No source-backed public claim about research AI use is present in this profile.

The current public tracker record does not contain claim evidence about research use, publication ethics, research data, grants, or human-subjects compliance.

Not MentionedMachine candidateConfidence0%Evidence0Sources0

Security and procurement

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.

Not MentionedMachine candidateConfidence0%Evidence0Sources0

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

5 reviewed evidence-backed public claim

Privacy

The University of Denver University Writing Program tells writers not to input private, proprietary, or protected data into a third-party website without understanding the relevant EULAs, privacy policies, and data-use terms.

Review: Agent reviewedConfidence93%

Normalized value: Writing-program genAI guidance includes a privacy and data-use warning for third-party websites.

Oryginalny dowod

Evidence 1
Review the End-User-License-Agreements (EULAs), privacy policies, and other use documentation so that you understand your rights, the rights of the developer, and the use of data you provide to a company. Never input private, propriety, or protected data to a third-party website without understanding these policies.

Academic Integrity

The University of Denver University Writing Program says students should review and abide by instructor policies for assignments before using generative AI technology.

Review: Agent reviewedConfidence92%

Normalized value: Student-facing writing guidance ties genAI use to instructor assignment policies.

Oryginalny dowod

Evidence 1
Before using any such technology, review the policies and constraints of the rhetorical situation. As a student, this means abiding by instructor policies for assignments communicated in the syllabus, assignment, or verbally.

Teaching

For writing assignments, the University of Denver University Writing Program advises faculty to outline clear policies about when and how generative AI can or cannot be used and to include instructions for acknowledging assistance.

Review: Agent reviewedConfidence92%

Normalized value: Faculty-facing writing guidance recommends assignment-specific genAI policies and acknowledgement instructions.

Oryginalny dowod

Evidence 1
Acknowledge that we are aware that genAI exists and outline clear policies about when and how it can or cannot be used in an assignment or task. Blanket syllabus statements forbidding or giving carte blanche are not as helpful as outlining the ways genAI might be used for specific tasks or giving specific directions not to use genAI for certain parts of the writing process.

Teaching

The University of Denver Office of Teaching and Learning advises faculty to state clear expectations for student work and to consider syllabus statements explaining whether and how AI tools can be used.

Review: Agent reviewedConfidence91%

Normalized value: Faculty-facing AI classroom guidance recommends clear course-level AI expectations and syllabus language.

Oryginalny dowod

Evidence 1
Be very clear about your expectations regarding students' work. Consider syllabus statements indicating whether and how AI tools can be used.

Academic Integrity

The University of Denver Office of Teaching and Learning advises faculty not to rely on AI-checking software to confirm suspected academic dishonesty.

Review: Agent reviewedConfidence90%

Normalized value: Faculty-facing academic-integrity guidance cautions against relying on AI detectors.

Oryginalny dowod

Evidence 1
Don't rely on AI checking software to confirm your suspicions. Although Turnitin and other companies have programs that check for AI-generated writing, many of these programs are, at best, in their earliest stages, or at worse, unreliable.

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

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.

Last checkedMay 23, 2026Last changedMay 23, 2026Open change log

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.

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