Policy presence
The University of Melbourne has 4 source-backed public claims for policy presence; deterministic analysis status: unclear.
Parkville, Australia
The University of Melbourne
The University of Melbourne has 18 source-backed AI policy claims from 5 official source attributions. Review state: agent reviewed; 18 reviewed claims. Last checked May 6, 2026.
The University of Melbourne AI policy short answer
v1 public contract
The University of Melbourne has 18 source-backed AI policy claims from 5 official source attributions, including 18 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 6, 2026. Discovery context: The University of Melbourne is listed as QS 2026 rank 19.
Citation-ready summary
As of this public record, University AI Policy Tracker lists The University of Melbourne as an agent-reviewed AI policy record last checked on May 6, 2026 and last changed on May 6, 2026. The record contains 18 source-backed claims, including 18 reviewed claims, from 5 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-melbourne.json. The entity-level confidence is 98%. 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 Other policy claims.
- Named AI services detected in public claims: ChatGPT, Grammarly.
- 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.
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
The University of Melbourne has 4 source-backed public claims for ai disclosure; deterministic analysis status: required.
Coursework
The University of Melbourne has 4 source-backed public claims for coursework; deterministic analysis status: required.
Exams
The University of Melbourne has 1 source-backed public claim for exams; deterministic analysis status: required.
Privacy and data entry
The University of Melbourne has 4 source-backed public claims for privacy and data entry; deterministic analysis status: restricted.
Academic integrity
The University of Melbourne has 2 source-backed public claims for academic integrity; deterministic analysis status: required.
Approved tools
The University of Melbourne has 1 source-backed public claim for approved tools; deterministic analysis status: required.
Named AI services
The University of Melbourne has 2 source-backed public claims for named ai services; deterministic analysis status: required.
Teaching guidance
The University of Melbourne has 2 source-backed public claims for teaching guidance; deterministic analysis status: recommended.
Research guidance
The University of Melbourne 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
18 reviewed evidence-backed public claim
Other
At the University of Melbourne, using GenAI tools to produce work submitted for assessment without acknowledgement constitutes academic misconduct under cl. 4.13 of the Student Academic Integrity Policy (MPF1310).
Review: Agent reviewed
Evidence originale
Evidence 1If an assessment task does not permit the use of such tools, or if they use such tools in the preparation of an assessment submission without acknowledgement, this constitutes academic misconduct under cl. 4.13 of the Student Academic Integrity Policy (MPF1310).
Other
A high AI score in Turnitin's writing detection report at the University of Melbourne is not proof that academic misconduct has taken place and does not on its own constitute grounds for making an allegation of academic misconduct.
Review: Agent reviewed
Evidence originale
Evidence 1A high AI score in Turnitin's writing detection report is not proof that academic misconduct has taken place (any more than would be the case when using the more familiar similarity report tool to flag potential plagiarism). It does not, on its own, constitute grounds for making an allegation of academic misconduct.
Other
Students must check with their Subject Coordinator before using GenAI for assessment-related work at the University of Melbourne.
Review: Agent reviewed
Evidence originale
Evidence 1Before you use GenAI for assessment-related work you must check to ensure that your Subject Coordinator has authorised its use.
Other
University of Melbourne assessment materials and teaching materials constitute University IP and should never be tested on third-party external GenAI platforms such as ChatGPT; any such testing must be done only within the University's secure SparkAI platform.
Review: Agent reviewed
Evidence originale
Evidence 1Since assessments and other teaching materials constitute University IP, they should never be tested on third-party external platforms such as ChatGPT, since these platforms use all prompts and inputs to further train their models. Any such testing/auditing must be done only within the University's secure GenAI platform (SparkAI) or other University of Melbourne-sanctioned platforms.
Other
University of Melbourne students must appropriately cite any use of GenAI tools in the preparation of assessment submissions.
Review: Agent reviewed
Evidence originale
Evidence 1Any use of GenAI in the preparation of an assessment submission must be appropriately cited.
Other
At the University of Melbourne, generative AI tools can only be used in research outputs where the material generated or substantially altered by these tools is acknowledged according to the University's policy and the Australian Code for the Responsible Conduct of Research.
Review: Agent reviewed
Evidence originale
Evidence 1Generative AI tools can only be used if material that is generated or substantially altered by these tools is acknowledged according to the University's policy and the Australian Code for the Responsible Conduct of Research.
Other
The University of Melbourne advises researchers that they should not share confidential information or information about an innovation in a generative AI prompt, as that may mean the IP is no longer owned by the researcher or by the University.
Review: Agent reviewed
Evidence originale
Evidence 1The University has advised researchers that they should not share confidential information or information about an innovation in a generative AI prompt, as that may mean that the IP is no longer owned by the researcher or by the University.
Other
University of Melbourne students must not upload personal information (full name, date of birth, address, or other confidential/sensitive/private information) to GenAI tools.
Review: Agent reviewed
Evidence originale
Evidence 1Protect your personal information and that of others. Do not upload your full name, date of birth, address or other confidential, sensitive or private information.
Other
University of Melbourne students must not upload copyrighted material (such as lecture slides or subject material) to AI tools without permission, as this may violate the intellectual property rights of creators.
Review: Agent reviewed
Evidence originale
Evidence 1Don't make copyright material available on the web or to an AI tool without permission.
Other
Each University of Melbourne subject coordinator is responsible for setting out the bounds of appropriate GenAI use within their subject, and is strongly encouraged to consider possible use case scenarios, set clear boundaries, and have conversations with students.
Review: Agent reviewed
Evidence originale
Evidence 1It is the responsibility of each subject coordinator to set out the bounds of appropriate GenAI use within their subject. Coordinators are strongly encouraged to consider possible use case scenarios for their assessments, set clear boundaries, and have conversations with their students to enable clarity about what tools are appropriate and for what tasks.
Other
The University of Melbourne states that University IP cannot be uploaded to external sites and recommends using the University's SparkAI platform when staff want AI to review University materials.
Review: Agent reviewed
Evidence originale
Evidence 1University IP cannot be uploaded to external sites, so if you want AI to review some of your materials, we recommend using the SparkAI platform.
Other
At the University of Melbourne, use of GenAI tools for marking or grading should never be a substitute for staff exercising their own evaluative judgement.
Review: Agent reviewed
Evidence originale
Evidence 1Use of GenAI tools should never be a substitute for staff exercising their own evaluative judgement.
Other
The University of Melbourne advises staff to model the transparency requested from students by acknowledging the use of any GenAI tools in materials provided to students.
Review: Agent reviewed
Evidence originale
Evidence 1We should also model the transparency we are requesting from students and acknowledge the use of any GenAI tools in materials we provide to them.
Other
University of Melbourne staff should consider and provide advice on the limits of acceptable use of AI-powered translation and editing tools (e.g. Grammarly, Google Translate) in their subjects, as student use can extend to the point where students are no longer expressing their own ideas.
Review: Agent reviewed
Evidence originale
Evidence 1Student use of these tools may pose risks to academic integrity, and staff should consider and provide advice on the limits of acceptable use in their subject. Use of these tools can easily extend to the point where students are no longer expressing their own ideas or understanding of the subject matter.
Other
The University of Melbourne recommends that universities should have autonomy over their own generative AI policies and that the Australian Government develop optional guidance for the sector on the use of generative AI in teaching, learning, assessment, and research.
Review: Agent reviewed
Evidence originale
Evidence 1The Australian Government develop optional guidance for the sector on the use of generative artificial intelligence (AI) in teaching, learning, assessment, and research. However, Universities should continue to have autonomy over their own generative AI policies to ensure they are appropriate for their communities.
Other
The University of Melbourne has adopted AI Principles designed to guide actions around the adoption and use of AI tools and systems across the institution.
Review: Agent reviewed
Evidence originale
Evidence 1This guidance is also influenced by the University of Melbourne AI Principles, which have been designed to guide actions around the adoption and use of AI tools and systems.
Other
University of Melbourne students must produce the text, code, designs, or images they are being assessed on themselves, not via GenAI, in order to develop the skills and capabilities their degree claims they have.
Review: Agent reviewed
Evidence originale
Evidence 1To develop these skills and capabilities, you must produce the text, code, designs or images you're being assessed on. For example, if a subject learning outcome includes 'discuss and critique', you must generate that discussion and critique – not GenAI – in order to develop those skills.
Other
The University of Melbourne has published a Statement on Graduate Research and digital assistance tools governing the use of AI tools by Graduate Researchers.
Review: Agent reviewed
Evidence originale
Evidence 1Statement on Graduate Research and digital assistance tools — This page features the University's official statement about the use of digital assistance tools by Graduate Researchers.
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
5 source attribution
GenAI at Melbourne
students.unimelb.edu.au
Generative AI in T&L at the University of Melbourne
melbourne-cshe.unimelb.edu.au
Official statements from The University of Melbourne
melbourne-cshe.unimelb.edu.au
UoM Submission: Inquiry into Generative AI in Education
about.unimelb.edu.au
What Is This Guide? - Melbourne School of Design
msd.unimelb.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.