Policy presence
City University of New York has 4 source-backed public claims for policy presence; deterministic analysis status: unclear.
New York City, United States
City University of New York
City University of New York has 5 source-backed AI policy claims from 2 official source attributions. Review state: agent reviewed; 5 reviewed claims. Last checked May 17, 2026.
City University of New York AI policy short answer
v1 public contract
City University of New York 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 17, 2026. Discovery context: City University of New York is listed as QS 2026 rank =613.
Citation-ready summary
As of this public record, University AI Policy Tracker lists City University of New York as an agent-reviewed AI policy record last checked on May 17, 2026 and last changed on May 17, 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/city-university-of-new-york.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 AI tool treatment claims.
- Evidence includes Academic integrity claims.
- Evidence includes Privacy claims.
- Evidence includes Teaching claims.
- No specific AI service name is highlighted by the current 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.
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
City University of New York has 1 source-backed public claim for ai disclosure; deterministic analysis status: recommended.
Coursework
City University of New York has 3 source-backed public claims for coursework; deterministic analysis status: required.
Exams
City University of New York has 4 source-backed public claims for exams; deterministic analysis status: required.
Privacy and data entry
City University of New York has 1 source-backed public claim for privacy and data entry; deterministic analysis status: restricted.
Academic integrity
City University of New York has 3 source-backed public claims for academic integrity; deterministic analysis status: required.
Approved tools
City University of New York has 1 source-backed public claim for approved tools; deterministic analysis status: required.
Named AI services
City University of New York has 2 source-backed public claims for named ai services; deterministic analysis status: restricted.
Teaching guidance
City University of New York has 2 source-backed public claims for teaching guidance; deterministic analysis status: recommended.
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.
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.
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
Ai Tool Treatment
CUNY's Academic Integrity Policy says use of generative AI tools must align with the usage policy for specific assignments as defined in the syllabus or communicated by the instructor.
Review: Agent reviewed
Normalized value: Generative AI use is assignment- and instructor-policy dependent.
Original evidence
Evidence 1Any use of generative AI tools must be in line with the usage policy for specific assignments as defined in the course of the syllabus and/or communicated by the course instructor.
Academic Integrity
CUNY's Academic Integrity Policy treats unauthorized use or attempted use of artificial intelligence systems during an academic exercise as cheating.
Review: Agent reviewed
Normalized value: Unauthorized AI-system use during an academic exercise is included in cheating.
Original evidence
Evidence 1Cheating is the unauthorized use or attempted use of material, information, notes, study aids, devices, artificial intelligence (AI) systems, or communication during an academic exercise.
Academic Integrity
CUNY's Academic Integrity Policy includes unauthorized use of AI-generated content, including paraphrased AI-generated content without citing AI as the source, as an example of plagiarism.
Review: Agent reviewed
Normalized value: Unauthorized or uncited AI-generated content may be plagiarism under the CUNY policy examples.
Original evidence
Evidence 1Unauthorized use of AI-generated content; or use of AI-generated content, whether in whole or in part, even when paraphrased, without citing the AI as the source.
Privacy
CUNY's AI Academic Hub lists Privacy & Data Protection, including FERPA compliance, among its guiding AI principles and advises avoiding sensitive or identifiable data when working with AI tools.
Review: Agent reviewed
Normalized value: AI use should protect data and avoid sensitive or identifiable data.
Original evidence
Evidence 1Privacy & Data Protection (FERPA Compliance) - AI must protect student and faculty data. ... When working with AI tools, avoid sharing sensitive or identifiable data.
Teaching
CUNY's AI Academic Hub advises faculty to guide AI use with clarity by establishing responsible AI engagement in coursework.
Review: Agent reviewed
Normalized value: Faculty are advised to establish clear responsible-AI engagement in coursework.
Original evidence
Evidence 1For Faculty: AI-Enhanced Teaching & Research ... Guide AI Use with Clarity - Establish responsible AI engagement in coursework.
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
AI Academic Hub - The City University of New York
cuny.edu
Article I - CUNY Policy
policy.cuny.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.