Policy presence
University of New England Australia has 5 source-backed public claims for policy presence; deterministic analysis status: unclear.
Armidale, Australia
University of New England Australia
University of New England Australia has 6 source-backed AI policy claims from 4 official source attributions. Review state: agent reviewed; 6 reviewed claims. Last checked May 24, 2026.
University of New England Australia AI policy short answer
v1 public contract
University of New England Australia has 6 source-backed AI policy claims from 4 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 New England Australia is listed as QS 2026 rank 1001-1200.
Citation-ready summary
As of this public record, University AI Policy Tracker lists University of New England Australia 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 4 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-new-england-australia.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 Research claims.
- Evidence includes Academic integrity claims.
- Evidence includes Privacy claims.
- Evidence includes AI tool treatment claims.
- Evidence includes Source status 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 New England Australia has 2 source-backed public claims for ai disclosure; deterministic analysis status: required.
Coursework
University of New England Australia has 2 source-backed public claims for coursework; deterministic analysis status: restricted.
Exams
University of New England Australia has 3 source-backed public claims for exams; deterministic analysis status: restricted.
Privacy and data entry
University of New England Australia has 2 source-backed public claims for privacy and data entry; deterministic analysis status: restricted.
Academic integrity
University of New England Australia has 3 source-backed public claims for academic integrity; deterministic analysis status: restricted.
Approved tools
University of New England Australia has 1 source-backed public claim for approved tools; deterministic analysis status: conditionally_allowed.
Named AI services
University of New England Australia has 2 source-backed public claims for named ai services; deterministic analysis status: restricted.
Teaching guidance
No source-backed public claim about teaching guidance is present in this profile.
The current public tracker record does not contain claim evidence about instructor, classroom, assessment-design, or syllabus guidance.
Research guidance
University of New England Australia has 4 source-backed public claims for research guidance; deterministic analysis status: restricted.
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
6 reviewed evidence-backed public claim
Research
UNE's HDR generative AI guidance says higher degree research candidates are required to consider ethical, critical, and creative uses of generative AI and to ensure proper citation of content generated by AI tools and sources they use.
Review: Agent reviewed
Normalized value: HDR candidates must acknowledge and cite generative AI use
Oryginalny dowod
Evidence 1In the planning of a research project, HDR candidates are required to consider the ethical, critical, and creative uses of generative AI and to ensure proper citation of all content generated by AI tools and sources that you use.
Academic Integrity
UNE's HDR generative AI guidance warns that failure to reference generative AI content may be considered academic or research misconduct, and lists risks including unacknowledged AI output, inaccurate AI-generated references, substantive AI-generated thesis or publication content, peer review or ethics-clearance use, and disclosure of sensitive or confidential material to generative AI.
Review: Agent reviewed
Normalized value: HDR GenAI non-acknowledgement and specified uses may raise misconduct risks
Oryginalny dowod
Evidence 1Failure to reference content from generative AI may be considered academic misconduct or research misconduct... avoid... Using output from generative AI tools without acknowledgment... Including substantive content from a generative AI tool into your HDR thesis or publication... Breaching data privacy, security, copyright, intellectual property, or confidentiality...
Academic Integrity
UNE's Student Academic Integrity Policy includes artificial intelligence tools in its example of work substantially written by someone else, within the policy's breach framework for undergraduate and postgraduate award and non-award coursework students; the policy states it does not apply to higher degree by research courses.
Review: Agent reviewed
Normalized value: AI tools included in coursework academic integrity breach examples; HDR excluded from this policy
Oryginalny dowod
Evidence 1This Policy applies to all students enrolled in undergraduate and postgraduate award and non-award courses offered by UNE... The Policy does not apply to higher degree by research courses. Breaches... include... work substantially written by someone else... artificial intelligence tool, or other tool.
Privacy
UNE's HDR generative AI guidance cautions that inputting research data into AI can risk loss of copyright or intellectual property control and can have serious implications if sensitive or highly sensitive data is inadvertently released.
Review: Agent reviewed
Normalized value: HDR research data input to AI is flagged as privacy/IP risk
Oryginalny dowod
Evidence 1Inputting any research data into AI, is at the risk of losing copyright/intellectual property rights... This could have significant ramifications... if you inadvertently release sensitive or highly sensitive data.
Ai Tool Treatment
A UNE School of Education public myLearn information page tells students that computer programs, artificial intelligence tools, or other tools should not be used to write or produce any part of an assessment response unless explicitly referenced and in line with unit coordinator advice for the assessment task.
Review: Agent reviewed
Normalized value: School of Education page: AI assessment use requires explicit referencing and unit coordinator advice
Oryginalny dowod
Evidence 1tools including computer programs, artificial intelligence tools or other tools should NOT be used to write, or produce, any part of your assessment response unless it has been explicitly referenced and in line with advice by the unit coordinator in your assessment task.
Source Status
UNE's public Learning Online academic integrity myLearn section states that a Generative AI and Academic Integrity at UNE module exists and lists module topics including general principles for use of generative AI, Turnitin detection and AI, and referencing generative AI.
Review: Agent reviewed
Normalized value: Public myLearn source confirms GenAI academic integrity module topics, but full module content is separate
Oryginalny dowod
Evidence 1Discover Generative AI and Academic Integrity at UNE and learn UNE's stance and how you can ethically use Generative AI... Generative AI Modules: What is Generative AI? General Principles for Use of Generative AI. TurnItIn Detection and AI. Referencing Generative AI. Example Case Study.
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
4 source attribution
Academic Integrity | Learning Online at UNE
mylearn.une.edu.au
Artificial Intelligence in HDR
une.edu.au
School of Education Information Site: UNE Academic Integrity Policy
mylearn.une.edu.au
Student Academic Integrity Policy
policies.une.edu.au
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.