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The University of Melbourne

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Coursework

Conditional

At the University of Melbourne, each Subject Coordinator sets the boundaries of appropriate GenAI use in their subject; students must check before using AI for assessment-related work and produce the submitted work themselves unless explicitly authorized.

Official source ↗

Exams

Conditional

In University of Melbourne examinations, students must verify whether GenAI is authorised by the Subject Coordinator or exam instructions, and if permitted, use only approved tools and specified permitted uses. Tool access conveys no exam authorization, and unauthorised or undisclosed AI use in exams constitutes academic misconduct under MPF1310.

Official source ↗

Disclosure

Required in the stated scope

University of Melbourne guidance mandates a dual disclosure framework: citing AI outputs in the reference list when quoting or paraphrasing does not replace an acknowledgement declaration; students must provide both a citation and a process declaration describing how tools were used, with unauthorised or undisclosed use constituting misconduct under MPF1310.

Official source ↗

Privacy & sensitive data

Restricted in the stated scope

Students and researchers must not upload personal data, private information, third-party copyright, or patentable IP to external AI platforms. University teaching and assessment IP must remain within SparkAI or sanctioned environments; student LMS interactions and AILA session metadata are retained and reviewable by authorized decision-makers under the LMS privacy collection notice.

Official source ↗

Institutional tools

Conditional

The University of Melbourne provides AILA within Canvas LMS where enabled by coordinators, SparkAI as a secure internal platform for staff (student access prohibited), Turnitin AI writing detection as an investigative diagnostic in similarity reports, and M365 Copilot Chat for student study support. Institutional access conveys neither coursework nor examination permission.

Official source ↗

Research & publication

Restricted in the stated scope

Under section 5.11 of the Responsible Conduct of Research Policy (MPF1318) and active University guidance, researchers must document DAT use, disclose tools in outputs, and obtain privacy/security/ethics approvals. For graduate research theses, AI editing is strictly restricted to minor language and clarity support under Australian Standards for Editing Practice, with supervisor AI review requiring explicit written candidate consent.

Official source ↗
More reviewed record topics

Academic integrity & procedures

Restricted in the stated scope

Under MPF1310, unauthorised or undisclosed technology use in assessable work constitutes academic misconduct. Poor Academic Practice is managed educatively by Subject Coordinators, distinct from formal Faculty Level 1 and Serious Level 2 misconduct. Turnitin AI detection reports serve as an investigative prompt rather than proof, students have the right to provide drafts and explanations in exploratory discussions, and MPF1323 establishes a 20-university-business-day appeal window.

Official source ↗

Teaching guidance

Source guidance

Melbourne teaching guidance advises Subject Coordinators to establish clear subject AI boundaries, model transparency, and provide guidance on translation and editing tool limits. GenAI tools must never substitute for staff evaluative judgement in marking or grading. Subjects are designed with at least 50% secure assessments to assure learning, supported by CSHE pedagogical assessment enhancement strategies.

Official source ↗

Security & procurement

Conditional

The University of Melbourne AI Principles establish an overarching governance framework emphasizing fairness, literacy, privacy, and security; staff testing of assessment materials must remain within SparkAI or sanctioned environments rather than public third-party services.

Official source ↗

Before you submit

Do

  • Check your Subject Coordinator syllabus and specific examination instructions before using generative AI in exams or coursework.
    Site advice
  • Provide both a reference list citation when quoting/paraphrasing AI outputs and an acknowledgement declaration describing process assistance.
    Requirement in this scope
  • Document digital assistance tool (DAT) research use, disclose DAT use to partners, collaborators, and in publications/theses, and obtain all necessary approvals (including privacy, security, or ethics review where applicable) under MPF1318 cl 5.11.
    Requirement in this scope

Don’t

  • Do not upload personal information, student records, confidential research, or third-party copyright materials to unauthorized public AI tools.
    Requirement in this scope
  • Do not assume institutional tool access (AILA, SparkAI, M365 Copilot Chat) conveys permission for course assignments or examinations.
    Requirement in this scope
  • Do not use generative AI tools in examinations or assessable work without Subject Coordinator authorization or disclosure, which constitutes academic misconduct under MPF1310.
    Requirement in this scope

AI use questioned?

The University of Melbourne

Subject-level assessment guidelines; Student Academic Integrity Policy (MPF1310); Student Appeals Policy (MPF1323); Responsible Conduct of Research Policy (MPF1318 cl 5.11); Open-book exams advice; Canvas LMS privacy notice.

Investigation & response

At the University of Melbourne, using GenAI tools on an assessment task that does not permit them, or using them in preparation without acknowledgement, constitutes academic misconduct under cl. 4.13 of the Student Academic Integrity Policy (MPF1310).

Original evidence and scope

At the University of Melbourne, using GenAI tools on an assessment task that does not permit them, or using them in preparation without acknowledgement, constitutes academic misconduct under cl. 4.13 of the Student Academic Integrity Policy (MPF1310).

claim-university-of-melbourne-5

Student academic integrity and disciplinary procedures

It’s important your students realise that if 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).

Section: University policies in relation to Generative AI; extracted text lines 57-57

Official university source ↗

The University of Melbourne academic misconduct framework explicitly categorizes unauthorized or undisclosed use of technology (including GenAI) as a breach type under academic integrity procedures.

Original evidence and scope

The University of Melbourne academic misconduct framework explicitly categorizes unauthorized or undisclosed use of technology (including GenAI) as a breach type under academic integrity procedures.

claim-university-of-melbourne-technology-breach

Student Academic Integrity Policy breach categories

Breach types Plagiarism Unauthorised re-use of previous work (self plagiarism) Breach of examination or assessment rules or directions Falsification or misrepresentation of data Unauthorised or undisclosed use of technology (incl. GenAI)

Section: Managing Suspected Academic Misconduct - Breach Types; extracted text lines 128-133

Official university source ↗

Poor academic practice refers to minor and unintentional departures managed educatively by Subject Coordinators, and is explicitly distinct from academic misconduct.

Original evidence and scope

Poor academic practice refers to minor and unintentional departures managed educatively by Subject Coordinators, and is explicitly distinct from academic misconduct.

claim-university-of-melbourne-misconduct-tiers

Student Academic Integrity Policy procedural classifications

Poor academic practice refers to minor and unintentional departures from accepted scholarly conventions or failures to comply with assessment guidelines. Poor academic practice incidents are characterised by inexperience, lack of student knowledge, or poor academic skills, and does not compromise the purpose and/or integrity of the assessment. Importantly, poor academic practice is not academic misconduct, may be treated as an assessment matter and managed within the marking and assessment guidelines set by the Board of Examiners for the subject.

Section: Poor Academic Practice Definition and Management; extracted text lines 66-67

Official university source ↗

Turnitin AI detection results serve only as a prompt for further investigation; if suspected, students may be invited to explain their work and provide drafts or notes in an exploratory discussion.

Original evidence and scope

Turnitin AI detection results serve only as a prompt for further investigation; if suspected, students may be invited to explain their work and provide drafts or notes in an exploratory discussion.

claim-university-of-melbourne-turnitin-investigation-rights

Turnitin AI investigative procedures and student rights

As with the similarity report generated by Turnitin, the result of the AI writing detector tool is a prompt for further investigation. Should there be a suspicion that part or all of your submitted assessment has been produced using generative AI, you may be asked to explain your essay and argument (how you developed the argument, what sources you used, how you reached the conclusion you did), or to provide drafts or notes of early versions of the assessment.

Section: Advice for Students Regarding Turnitin - Prompt for investigation; extracted text lines 15-16

Official university source ↗

Possible outcomes

Poor academic practice refers to minor and unintentional departures managed educatively by Subject Coordinators, and is explicitly distinct from academic misconduct.

Original evidence and scope

Poor academic practice refers to minor and unintentional departures managed educatively by Subject Coordinators, and is explicitly distinct from academic misconduct.

claim-university-of-melbourne-misconduct-tiers

Student Academic Integrity Policy procedural classifications

Poor academic practice refers to minor and unintentional departures from accepted scholarly conventions or failures to comply with assessment guidelines. Poor academic practice incidents are characterised by inexperience, lack of student knowledge, or poor academic skills, and does not compromise the purpose and/or integrity of the assessment. Importantly, poor academic practice is not academic misconduct, may be treated as an assessment matter and managed within the marking and assessment guidelines set by the Board of Examiners for the subject.

Section: Poor Academic Practice Definition and Management; extracted text lines 66-67

Official university source ↗

The University of Melbourne academic misconduct framework explicitly categorizes unauthorized or undisclosed use of technology (including GenAI) as a breach type under academic integrity procedures.

Original evidence and scope

The University of Melbourne academic misconduct framework explicitly categorizes unauthorized or undisclosed use of technology (including GenAI) as a breach type under academic integrity procedures.

claim-university-of-melbourne-technology-breach

Student Academic Integrity Policy breach categories

Breach types Plagiarism Unauthorised re-use of previous work (self plagiarism) Breach of examination or assessment rules or directions Falsification or misrepresentation of data Unauthorised or undisclosed use of technology (incl. GenAI)

Section: Managing Suspected Academic Misconduct - Breach Types; extracted text lines 128-133

Official university source ↗

Under section 5.11 of the Responsible Conduct of Research Policy (MPF1318) and University guidance, research personnel must document digital assistance tool use, disclose DAT use to collaborators and in outputs (including theses), and obtain all required privacy, security, and ethics approvals.

Original evidence and scope

Under section 5.11 of the Responsible Conduct of Research Policy (MPF1318) and University guidance, research personnel must document digital assistance tool use, disclose DAT use to collaborators and in outputs (including theses), and obtain all required privacy, security, and ethics approvals.

claim-university-of-melbourne-research-integrity-mpf1318

Responsible Conduct of Research Policy (MPF1318) section 5.11 obligations

In line with section 5.11 of the Responsible Conduct of Research Policy, research personnel are responsible for assuring the integrity of their research produced with the assistance of digital assistance tools, including but not limited to artificial intelligence (AI) tools. Research personnel must: Document the use of a digital assistance tool (DAT) in their research process Disclose the use of a DAT to research partners, collaborators and in research outputs, including but not limited to publications and theses

Section: Responsible Conduct of Research Policy cl. 5.11 obligations; extracted text lines 36-38

Official university source ↗
Ensure all necessary approvals are obtained to use the DAT, including but not limited to a Privacy Impact Assessment, Information Security Risk Assessment, or ethical review and approval where applicable.

Section: Approvals for DAT Use; extracted text lines 39-39

Official university source ↗

Review & appeal

Under University of Melbourne student appeals procedures, appeals against academic misconduct decisions must be submitted via the online form within 20 University business days from the original decision date.

Original evidence and scope

Under University of Melbourne student appeals procedures, appeals against academic misconduct decisions must be submitted via the online form within 20 University business days from the original decision date.

claim-university-of-melbourne-appeals-window

Student Appeals Policy (MPF1323)

To submit an appeal, you must complete the online student appeals form within 20 University business days from the date of the original decision.

Section: Submitting an Appeal - 20 Business Days Window; extracted text lines 126-126

Official university source ↗

Poor academic practice refers to minor and unintentional departures managed educatively by Subject Coordinators, and is explicitly distinct from academic misconduct.

Original evidence and scope

Poor academic practice refers to minor and unintentional departures managed educatively by Subject Coordinators, and is explicitly distinct from academic misconduct.

claim-university-of-melbourne-misconduct-tiers

Student Academic Integrity Policy procedural classifications

Poor academic practice refers to minor and unintentional departures from accepted scholarly conventions or failures to comply with assessment guidelines. Poor academic practice incidents are characterised by inexperience, lack of student knowledge, or poor academic skills, and does not compromise the purpose and/or integrity of the assessment. Importantly, poor academic practice is not academic misconduct, may be treated as an assessment matter and managed within the marking and assessment guidelines set by the Board of Examiners for the subject.

Section: Poor Academic Practice Definition and Management; extracted text lines 66-67

Official university source ↗

AI tools & access

  • M365 Copilot Chat (student account)Institutional access / licenceConditions apply

    Log in with Unimelb student account online or via web app.

  • AILAUniversity-hosted serviceConditions apply

    Available in Canvas LMS subject sites when enabled by the Subject Coordinator.

  • SparkAIUniversity-hosted serviceConditions apply

    Staff access SparkAI at spark.unimelb.edu.au with University credentials.

  • Turnitin AI writing detectionInstitutional access / licenceConditions apply

    Staff access similarity reports in Canvas LMS.

Record updatesLast 90 days · Recorded updates: 2

Updates to this site's reviewed record, not confirmation that the university changed its policy.

  1. Page update

    Other records · Page update

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  2. Record addition · Site correction

    Coursework · Exams · 8 more topics · Records involved: 35 (21 corrected / adjusted · 14 added)

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Reviewed claims

35

Official sources

18 official URLs; 18 source attributions and their captured versions retained.

GenAI at Melbourne (Academic Skills - Study Skills)

students.unimelb.edu.au

University official source

The University of Melbourne

Hash and captured version

GenAI at Melbourne (Academic Skills - Study Skills) · 2026-10-18T02:21:17Z

SHA-256 of the retained capture file bytes.

21aa00d7cd52d1d54ccbe90e120c97e007fbada8a044df8d8f431ea69b65cdf1

Hashes identify a captured version; they do not establish current policy or review approval.

Official source ↗

Generative AI in T&L at the University of Melbourne (Melbourne CSHE)

melbourne-cshe.unimelb.edu.au

University official source

The University of Melbourne

Hash and captured version

Generative AI in T&L at the University of Melbourne (Melbourne CSHE) · 2026-10-18T02:21:17Z

SHA-256 of the retained capture file bytes.

5483d7a12c29a086e5573afc1dc6847677337700b3433df7de7065bde7c51951

Hashes identify a captured version; they do not establish current policy or review approval.

Official source ↗

Official Statements from The University of Melbourne on Generative AI

melbourne-cshe.unimelb.edu.au

University official source

The University of Melbourne

Hash and captured version

Official Statements from The University of Melbourne on Generative AI · 2026-10-18T02:21:19Z

SHA-256 of the retained capture file bytes.

d99bff55d35c72e3bade539482d434d21a73ecdd2c812ad967eb48e84bbf624f

Hashes identify a captured version; they do not establish current policy or review approval.

Official source ↗

University of Melbourne Submission: Inquiry into Generative AI in Education (July 2023)

about.unimelb.edu.au

University official source

The University of Melbourne

Hash and captured version

University of Melbourne Submission: Inquiry into Generative AI in Education (July 2023) · 2026-10-18T02:21:19Z

SHA-256 of the retained capture file bytes.

909a6dd0283b713c9ee62d17bd1108ede4b8e110e624d820fc76f1009ec69c27

Hashes identify a captured version; they do not establish current policy or review approval.

Official source ↗

What Is This Guide? Generative AI in Teaching and Learning (MSD BEL+T)

msd.unimelb.edu.au

University official source

The University of Melbourne

Hash and captured version

What Is This Guide? Generative AI in Teaching and Learning (MSD BEL+T) · 2026-10-18T02:21:22Z

SHA-256 of the retained capture file bytes.

c22e73100bc983cabada9e565712d1ab02601743a820b151844f4a8f8ec159a0

Hashes identify a captured version; they do not establish current policy or review approval.

Official source ↗

Academic Integrity at the University of Melbourne (Academic Skills)

students.unimelb.edu.au

University official source

The University of Melbourne

Hash and captured version

Academic Integrity at the University of Melbourne (Academic Skills) · 2026-10-18T02:21:22Z

SHA-256 of the retained capture file bytes.

8e04e2a3eb4bab50429a46cf0d8b8acc9bdb7f5babdf5dcf90ad8285e1f5170a

Hashes identify a captured version; they do not establish current policy or review approval.

Official source ↗

Advice for Students Regarding Turnitin and AI Writing Detection

academicintegrity.unimelb.edu.au

University official source

The University of Melbourne

Hash and captured version

Advice for Students Regarding Turnitin and AI Writing Detection · 2026-10-18T02:21:23Z

SHA-256 of the retained capture file bytes.

1fcff778b6a2fbec4d5934f678ef18012121890f7c0ab15ef8e2139c3b43fce4

Hashes identify a captured version; they do not establish current policy or review approval.

Official source ↗

Student IT: Microsoft 365 Copilot Chat Guidance

studentit.unimelb.edu.au

University official source

The University of Melbourne

Hash and captured version

Student IT: Microsoft 365 Copilot Chat Guidance · 2026-10-18T02:21:24Z

SHA-256 of the retained capture file bytes.

57163adaedc013bb356d4c68600255e8288521f7b877b78ac191f4da9b0f5387

Hashes identify a captured version; they do not establish current policy or review approval.

Official source ↗

Student Academic Integrity Policy: Schedule 1 Academic Misconduct (MPF1310)

about.unimelb.edu.au

University official source

The University of Melbourne

Hash and captured version

Student Academic Integrity Policy: Schedule 1 Academic Misconduct (MPF1310) · 2026-10-18T02:21:24Z

SHA-256 of the retained capture file bytes.

b789bcde7cdcb10405d74e1a2d7aa5aa448f4d01b7ff96df64d6b8a3b4e0075c

Hashes identify a captured version; they do not establish current policy or review approval.

Official source ↗

Student Academic Integrity: What Changed in 2025? (MPF1310 Implementation)

academicintegrity.unimelb.edu.au

University official source

The University of Melbourne

Hash and captured version

Student Academic Integrity: What Changed in 2025? (MPF1310 Implementation) · 2026-10-18T02:21:24Z

SHA-256 of the retained capture file bytes.

c0c1540963ac89d35b53d040641d193e50ce5524fb3dda85f655654a0e237a32

Hashes identify a captured version; they do not establish current policy or review approval.

Official source ↗

Academic Board: Student Appeals Guidance and Deadlines (MPF1323)

about.unimelb.edu.au

University official source

The University of Melbourne

Hash and captured version

Academic Board: Student Appeals Guidance and Deadlines (MPF1323) · 2026-10-18T02:21:24Z

SHA-256 of the retained capture file bytes.

7ead11188d33151bc145399912868ebbe170991d4bdeff60f8adf4d085b34d88

Hashes identify a captured version; they do not establish current policy or review approval.

Official source ↗

Responsible Conduct of Research Policy (MPF1318): Explanatory Memorandum

about.unimelb.edu.au

University official source

The University of Melbourne

Hash and captured version

Responsible Conduct of Research Policy (MPF1318): Explanatory Memorandum · 2026-10-18T02:21:25Z

SHA-256 of the retained capture file bytes.

bb272cca2d094e25b37411dba615d2634f8a1e687e4cb9ea87e7f17c42db0a85

Hashes identify a captured version; they do not establish current policy or review approval.

Official source ↗

The University of Melbourne AI Principles

www.unimelb.edu.au

University official source

The University of Melbourne

Hash and captured version

The University of Melbourne AI Principles · 2026-10-18T02:21:25Z

SHA-256 of the retained capture file bytes.

fe5c4e073af35c5a10f0ce70804e2d37b63afc31334c37e873f1a4c81308f0af

Hashes identify a captured version; they do not establish current policy or review approval.

Official source ↗

Secure Assessment Guidelines (Education Melbourne)

about.unimelb.edu.au

University official source

The University of Melbourne

Hash and captured version

Secure Assessment Guidelines (Education Melbourne) · 2026-10-18T02:21:25Z

SHA-256 of the retained capture file bytes.

aa7a6a2b58f2bd8e294fd41655e4717b33e66c873fc0cab8b85432c6d39144e4

Hashes identify a captured version; they do not establish current policy or review approval.

Official source ↗

Assuring Learning in the Age of GenAI: Guidelines and Principles for Assessment

about.unimelb.edu.au

University official source

The University of Melbourne

Hash and captured version

Assuring Learning in the Age of GenAI: Guidelines and Principles for Assessment · 2026-10-18T02:21:26Z

SHA-256 of the retained capture file bytes.

a7a3346b0d0d4f5eeee041286fe18062eca20db304def4b7952ae9bafff9d083

Hashes identify a captured version; they do not establish current policy or review approval.

Official source ↗

Open-book Exams (Academic Skills - Study Skills)

students.unimelb.edu.au

University official source

The University of Melbourne

Hash and captured version

Open-book Exams (Academic Skills - Study Skills) · 2026-10-18T06:01:36Z

SHA-256 of the retained capture file bytes.

2b50d9d0a964d0c73c56a71b50198c01f6ee60fbd2f6021db3e3b9803f0e94cf

Hashes identify a captured version; they do not establish current policy or review approval.

Official source ↗

Responsible Use of AI in Research (Policy and Guidance)

research.unimelb.edu.au

University official source

The University of Melbourne

Hash and captured version

Responsible Use of AI in Research (Policy and Guidance) · 2026-10-18T06:01:36Z

SHA-256 of the retained capture file bytes.

0d78ffb0dd1692fe7c14147282cc31310ac0e0ff6d5d198c1eaf7c083e027bf4

Hashes identify a captured version; they do not establish current policy or review approval.

Official source ↗

University of Melbourne LMS Privacy Collection Notice

lms.unimelb.edu.au

University official source

The University of Melbourne

Hash and captured version

University of Melbourne LMS Privacy Collection Notice · 2026-10-18T06:01:37Z

SHA-256 of the retained capture file bytes.

685ec5cf985c86f25ed5a2db826c7e91e8f3e816f8993ef7de2629d75375766a

Hashes identify a captured version; they do not establish current policy or review approval.

Official source ↗
Record information, JSON & citation

Review layers: claim review applies to the claims only. Analysis is a separate machine-generated summary awaiting review. Coverage scores do not establish complete policy knowledge.

Claim review state
agent_reviewed
Analysis review state
machine_candidate
Record revision
melbourne-v6-r3-20261018
Dataset release
public-release-20260924-002
Correction published
Student snapshot
Not published
Current reviewed JSON · Historical baseline

Published V6 presentation; JSON and MCP retain the existing public record. Supplementary quotations and scope notes remain pending review. Local candidate corrections, tools and student summaries are not approved for publication. Institution-wide audit remains PARTIAL.

          Current reviewed JSON
          Questions students ask

          Questions students ask

          Can I use AI for my coursework?

          At the University of Melbourne, each Subject Coordinator sets the boundaries of appropriate GenAI use in their subject; students must check before using AI for assessment-related work and produce the submitted work themselves unless explicitly authorized.

          View evidence →
          Do I need to disclose AI use?

          University of Melbourne guidance mandates a dual disclosure framework: citing AI outputs in the reference list when quoting or paraphrasing does not replace an acknowledgement declaration; students must provide both a citation and a process declaration describing how tools were used, with unauthorised or undisclosed use constituting misconduct under MPF1310.

          View evidence →
          Can I upload sensitive data?

          Students and researchers must not upload personal data, private information, third-party copyright, or patentable IP to external AI platforms. University teaching and assessment IP must remain within SparkAI or sanctioned environments; student LMS interactions and AILA session metadata are retained and reviewable by authorized decision-makers under the LMS privacy collection notice.

          View evidence →
          Which tools can I access?

          The University of Melbourne provides AILA within Canvas LMS where enabled by coordinators, SparkAI as a secure internal platform for staff (student access prohibited), Turnitin AI writing detection as an investigative diagnostic in similarity reports, and M365 Copilot Chat for student study support. Institutional access conveys neither coursework nor examination permission.

          View evidence →
          What are the examination rules?

          In University of Melbourne examinations, students must verify whether GenAI is authorised by the Subject Coordinator or exam instructions, and if permitted, use only approved tools and specified permitted uses. Tool access conveys no exam authorization, and unauthorised or undisclosed AI use in exams constitutes academic misconduct under MPF1310.

          View evidence →
          What rules apply to research and publication?

          Under section 5.11 of the Responsible Conduct of Research Policy (MPF1318) and active University guidance, researchers must document DAT use, disclose tools in outputs, and obtain privacy/security/ethics approvals. For graduate research theses, AI editing is strictly restricted to minor language and clarity support under Australian Standards for Editing Practice, with supervisor AI review requiring explicit written candidate consent.

          View evidence →
          What counts as academic misconduct?

          Under MPF1310, unauthorised or undisclosed technology use in assessable work constitutes academic misconduct. Poor Academic Practice is managed educatively by Subject Coordinators, distinct from formal Faculty Level 1 and Serious Level 2 misconduct. Turnitin AI detection reports serve as an investigative prompt rather than proof, students have the right to provide drafts and explanations in exploratory discussions, and MPF1323 establishes a 20-university-business-day appeal window.

          View evidence →
          What guidance applies to teaching?

          Melbourne teaching guidance advises Subject Coordinators to establish clear subject AI boundaries, model transparency, and provide guidance on translation and editing tool limits. GenAI tools must never substitute for staff evaluative judgement in marking or grading. Subjects are designed with at least 50% secure assessments to assure learning, supported by CSHE pedagogical assessment enhancement strategies.

          View evidence →
          What security and procurement requirements apply?

          The University of Melbourne AI Principles establish an overarching governance framework emphasizing fairness, literacy, privacy, and security; staff testing of assessment materials must remain within SparkAI or sanctioned environments rather than public third-party services.

          View evidence →
          When was this record checked?

          Record checked:

          Record version →
          Which official sources support these rules?Official source →
          Where can I read the full evidence?

          35 · Full policy evidence

          Full policy evidence →