Canberra, Australia Last checked

Australian National University (ANU)

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Coursework & assessment

Course-specific permissions

ANU delegates AI assessment permissions to course convenors. Students must check class summaries and assessment outlines for specific AI rules in every course. Unapproved drafting is prohibited.

Official source ↗

Examinations

Assessment outline rules apply

This review did not establish a current central ANU rule specifically permitting or prohibiting generative AI in examinations. Check the instructions for the particular assessment; general misconduct rules do not establish a blanket exam ban.

Official source ↗

Transparency & declaration

Declaration required when used

For ANU Law School submissions where AI tools are used, students must declare the application names, purpose, and extent/frequency; the declaration should be in the first footnote. For other ANU assessments, follow the course-specific instructions.

Official source ↗

Data privacy & student IP

Consent required for uploads

Academic staff are not permitted to upload student data or academic work to GenAI platforms. Users must not use AI to collect, use, store or disclose personal information without express consent of the individual.

Official source ↗

Approved AI services

Commercial data protection

Microsoft Copilot Enterprise (with Commercial Data Protection) is available to all ANU staff and students via SSO. Adobe Firefly is provided for staff and specific creative cohorts.

Official source ↗

Research & publications

ARC & publisher standards

Under Australian Research Council (ARC) policy cataloged by ANU Library, researchers applying for ARC grants must ensure the integrity and accuracy of applications and must disclose any use of generative AI. Under Association for Computing Machinery (ACM) policy cataloged by ANU Library, generative AI tools such as ChatGPT may not be listed as authors of an ACM published work; AI-assisted content creation must be fully disclosed.

Official source ↗
More reviewed record topics

Academic integrity framework

Multi-tier procedural fairness

Under the ANU Academic Integrity Rule 2021 framework, suspected breaches are addressed via informal review or formal inquiry; students are entitled to written notice and a support person (such as ANUSA or Dean of Students); findings are classified into No Breach (no record), Poor Academic Practice (unintentional/careless; penalties include reprimand, re-assessment, or mark reduction up to 25% of assessment marks, or up to 10% for first 48 units of undergraduate coursework; recorded internally, not on transcript), or Academic Misconduct (serious/intentional; formal penalties up to course fail, facility denial, suspension, or exclusion).

Official source ↗

Teaching & assessment design

CLT guidance resources

Teaching staff are encouraged to design assessments that emphasize authentic learning and provide clear syllabus policies regarding generative AI.

Official source ↗

IT security & procurement

Institutional approval required

All technical solutions being used for University business and/or on ANU managed devices must be approved by the University.

Official source ↗

Before you submit

Do

  • Consult class summaries and assessment outlines for course-specific AI requirements before using generative AI.
    Source guidance
  • For ANU Law School submissions using AI, declare tool names, purpose, and extent; the declaration should be in the first footnote. For other courses, follow course instructions.
    Requirement in this scope
  • Log in with your ANU account to access endorsed Copilot Enterprise with commercial data protection.
    Source guidance

Don’t

  • Do not submit AI-generated text or analysis as your own independent work without permission.
    Requirement in this scope
  • Academic staff must not upload student data or academic work to GenAI platforms, and users must not enter personal information into AI without express consent.
    Requirement in this scope
  • Do not assume generative AI is permitted or banned in exams; check the specific assessment instructions.
    Site advice

AI use questioned?

Australian National University (ANU)

Audience and activity scope varies strictly by source: course outlines govern coursework assessment tasks; Law School rules govern law submissions with footnote declarations; teaching guidance restricts academic staff from uploading student data to GenAI platforms; University privacy rules govern personal information handling across all users; Copilot Enterprise terms govern institutional SSO access.

Investigation & response

The ANU procedure for suspected misuse of generative AI is the same as for any misconduct, ensuring a student has the opportunity to respond before a formal decision is made.

Original evidence and scope

The ANU procedure for suspected misuse of generative AI is the same as for any misconduct, ensuring a student has the opportunity to respond before a formal decision is made.

claim-anu-005c
The  ANU procedure  for suspected misuse of generative AI is the same as for any misconduct, which includes giving a student the opportunity to respond.

Section: AI FAQs - procedure for suspected misuse; Extracted text SHA256 9e01dabe33ec988df7e6d95fe52c0c80665d46313747fa72659e7d235ba8d03d; original binding lines 114–114; characters 5619–5771; excerpt offset 0–152

Official university source ↗

Under the ANU Academic Integrity Rule 2021 framework, suspected breaches are addressed via informal review or formal inquiry; students are entitled to written notice and a support person (such as ANUSA or Dean of Students); findings are classified into No Breach (no record), Poor Academic Practice (unintentional/careless; penalties include reprimand, re-assessment, or mark reduction up to 25% of assessment marks, or up to 10% for first 48 units of undergraduate coursework; recorded internally, not on transcript), or Academic Misconduct (serious/intentional; formal penalties up to course fail, facility denial, suspension, or exclusion).

Original evidence and scope

Under the ANU Academic Integrity Rule 2021 framework, suspected breaches are addressed via informal review or formal inquiry; students are entitled to written notice and a support person (such as ANUSA or Dean of Students); findings are classified into No Breach (no record), Poor Academic Practice (unintentional/careless; penalties include reprimand, re-assessment, or mark reduction up to 25% of assessment marks, or up to 10% for first 48 units of undergraduate coursework; recorded internally, not on transcript), or Academic Misconduct (serious/intentional; formal penalties up to course fail, facility denial, suspension, or exclusion).

draft-anu-015
No Breach No Breach means that the course convenor concludes that the conduct has not breached the academic integrity principle. This is the end of the process. There will be no record on the student’s record or official transcript. Poor Academic Practice This is when the course convenor believes that the conduct is a breach of the academic integrity principle, however the conduct is excusable due to mitigating circumstances (e.g. personal, emotional, health-related circumstances, etc.) or the conduct is unintended, careless, inadvertent or uninformed. The breach is also not of a serious nature nor is it a recurrence. The course convenor can take certain actions, e.g. require the student to

Section: Step 4: Notification of outcome; Extracted text SHA256 c821f16619d66cb622a6b5cc289b036d9577d884e00f98834ee1658848d76907; original binding lines 410–414; characters 11103–11879; excerpt offset 0–700

Official university source ↗
re-submit their assessment, or reduce the student’s mark for the assessment.

Section: Step 4: Notification of outcome; Extracted text SHA256 c821f16619d66cb622a6b5cc289b036d9577d884e00f98834ee1658848d76907; original binding lines 410–414; characters 11103–11879; excerpt offset 700–776

Official university source ↗
Step 2: Seek support if required If support and/or assistance is needed at any point in time of the process, please contact ANUSA or the Dean of Students for advice.

Section: Step 2: Seek support; Extracted text SHA256 c821f16619d66cb622a6b5cc289b036d9577d884e00f98834ee1658848d76907; original binding lines 401–403; characters 10241–10407; excerpt offset 0–166

Official university source ↗
reduce the student’s mark for the relevant examination or other assessment no more than 25% of the mark available for the assessment (Rule 30(2) (g)/ Rule 51(2) (g)) no more than 10% of the mark available for the assessment for students within their first 48 units of an undergraduate coursework award (Rule 30(3)/ Rule 51(3))

Table of Outcomes - Poor Academic Practice mark reduction limits; Extracted text SHA256 c821f16619d66cb622a6b5cc289b036d9577d884e00f98834ee1658848d76907; original binding lines 476–478; characters 18851–19177; excerpt offset 0–326

Official university source ↗
(g)     subject to subsection (3 ), reduce the student’s mark for the relevant examination or other assessment by no more than 25% of the ma rk available for the assessment; (h)     tell any relevant professional, government or other organisation or agency about the finding and any action taken in relation to it. [ Note:   For action under paragraph (d), (e) and (f), see section 56 (Undertakings and requirements: finding of poor academic practice). ] (3)     However, under subsection (2)(g) the course convener may reduce the student’s mark by no more than 10% of the mark available for the assessment if the student’s c onduct is in relation to coursework undertaken by the st udent during the

Section 30(2)(g) and Section 30(3) - Course convener mark reduction limits; Extracted text SHA256 41ef5d6b92e42e666b95a9173f64b61863606d72f2381612a86966cb6adb56ed; original binding lines 414–417; characters 36237–37004; excerpt offset 0–700

Official university source ↗
f irst 48 units of a program for an undergraduate coursework award.

Section 30(2)(g) and Section 30(3) - Course convener mark reduction limits; Extracted text SHA256 41ef5d6b92e42e666b95a9173f64b61863606d72f2381612a86966cb6adb56ed; original binding lines 414–417; characters 36237–37004; excerpt offset 700–767

Official university source ↗

Possible outcomes

Under the ANU Academic Integrity Rule 2021 framework, suspected breaches are addressed via informal review or formal inquiry; students are entitled to written notice and a support person (such as ANUSA or Dean of Students); findings are classified into No Breach (no record), Poor Academic Practice (unintentional/careless; penalties include reprimand, re-assessment, or mark reduction up to 25% of assessment marks, or up to 10% for first 48 units of undergraduate coursework; recorded internally, not on transcript), or Academic Misconduct (serious/intentional; formal penalties up to course fail, facility denial, suspension, or exclusion).

Original evidence and scope

Under the ANU Academic Integrity Rule 2021 framework, suspected breaches are addressed via informal review or formal inquiry; students are entitled to written notice and a support person (such as ANUSA or Dean of Students); findings are classified into No Breach (no record), Poor Academic Practice (unintentional/careless; penalties include reprimand, re-assessment, or mark reduction up to 25% of assessment marks, or up to 10% for first 48 units of undergraduate coursework; recorded internally, not on transcript), or Academic Misconduct (serious/intentional; formal penalties up to course fail, facility denial, suspension, or exclusion).

draft-anu-015
No Breach No Breach means that the course convenor concludes that the conduct has not breached the academic integrity principle. This is the end of the process. There will be no record on the student’s record or official transcript. Poor Academic Practice This is when the course convenor believes that the conduct is a breach of the academic integrity principle, however the conduct is excusable due to mitigating circumstances (e.g. personal, emotional, health-related circumstances, etc.) or the conduct is unintended, careless, inadvertent or uninformed. The breach is also not of a serious nature nor is it a recurrence. The course convenor can take certain actions, e.g. require the student to

Section: Step 4: Notification of outcome; Extracted text SHA256 c821f16619d66cb622a6b5cc289b036d9577d884e00f98834ee1658848d76907; original binding lines 410–414; characters 11103–11879; excerpt offset 0–700

Official university source ↗
re-submit their assessment, or reduce the student’s mark for the assessment.

Section: Step 4: Notification of outcome; Extracted text SHA256 c821f16619d66cb622a6b5cc289b036d9577d884e00f98834ee1658848d76907; original binding lines 410–414; characters 11103–11879; excerpt offset 700–776

Official university source ↗
Step 2: Seek support if required If support and/or assistance is needed at any point in time of the process, please contact ANUSA or the Dean of Students for advice.

Section: Step 2: Seek support; Extracted text SHA256 c821f16619d66cb622a6b5cc289b036d9577d884e00f98834ee1658848d76907; original binding lines 401–403; characters 10241–10407; excerpt offset 0–166

Official university source ↗
reduce the student’s mark for the relevant examination or other assessment no more than 25% of the mark available for the assessment (Rule 30(2) (g)/ Rule 51(2) (g)) no more than 10% of the mark available for the assessment for students within their first 48 units of an undergraduate coursework award (Rule 30(3)/ Rule 51(3))

Table of Outcomes - Poor Academic Practice mark reduction limits; Extracted text SHA256 c821f16619d66cb622a6b5cc289b036d9577d884e00f98834ee1658848d76907; original binding lines 476–478; characters 18851–19177; excerpt offset 0–326

Official university source ↗
(g)     subject to subsection (3 ), reduce the student’s mark for the relevant examination or other assessment by no more than 25% of the ma rk available for the assessment; (h)     tell any relevant professional, government or other organisation or agency about the finding and any action taken in relation to it. [ Note:   For action under paragraph (d), (e) and (f), see section 56 (Undertakings and requirements: finding of poor academic practice). ] (3)     However, under subsection (2)(g) the course convener may reduce the student’s mark by no more than 10% of the mark available for the assessment if the student’s c onduct is in relation to coursework undertaken by the st udent during the

Section 30(2)(g) and Section 30(3) - Course convener mark reduction limits; Extracted text SHA256 41ef5d6b92e42e666b95a9173f64b61863606d72f2381612a86966cb6adb56ed; original binding lines 414–417; characters 36237–37004; excerpt offset 0–700

Official university source ↗
f irst 48 units of a program for an undergraduate coursework award.

Section 30(2)(g) and Section 30(3) - Course convener mark reduction limits; Extracted text SHA256 41ef5d6b92e42e666b95a9173f64b61863606d72f2381612a86966cb6adb56ed; original binding lines 414–417; characters 36237–37004; excerpt offset 700–767

Official university source ↗

Review & appeal

The current evidence does not include a verified specific procedure or deadline. Check the applicable university rules.

AI tools & access

  • Microsoft CopilotOfficial recommendationRecorded as allowed in the source scope

    Sign in with ANU email at copilot.microsoft.com or via Microsoft 365 Applications Portal.

  • Adobe FireflyInstitutional access / licenceRestricted in the source scope

    Access via Adobe Creative Cloud portal using ANU single sign-on credentials for allocated users.

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. Held / removed from current conclusions · Record addition · Site correction

    Exams · Research & publication · 7 more topics · Records involved: 34 (29 corrected / adjusted · 4 added · 1 held)

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Reviewed claims

33

Held conclusions and historical evidence (1)

These original records and prior review states are retained for provenance, not as current permissions, duties or precise deadlines.

ANU was developing governance document changes to require students to acknowledge any use of artificial intelligence in their work (as of early 2023).

Current status: held; historical review state: agent_reviewed

claim-anu-010a

Minor changes to a range of relevant governance documents and academic integrity guidance for students are also being considered. Once approved, these changes are likely to require students to acknowledge any use of artificial intelligence in their work.

Official sources

16 official URLs; 16 source attributions and their captured versions retained.

ANU Institutional AI Principles

learningandteaching.anu.edu.au

University official source

learningandteaching.anu.edu.au

Hash and captured version

ANU Institutional AI Principles · 2026-10-20T23:13:00Z

Representation unspecified in this historical record; do not assume an original-file hash.

f024609de98a615b3f99b4980000df01b170b694202271e7bd6e13bc38b7daeb

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

Official source ↗

AI for learning and assessment

learningandteaching.anu.edu.au

University official source

learningandteaching.anu.edu.au

Hash and captured version

AI for learning and assessment · 2026-10-20T23:13:00Z

Representation unspecified in this historical record; do not assume an original-file hash.

57c0904702e932c3f0ae8034fbf2dfdd3e75e74b09d8baf5056c973b3a8eb5c1

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

Official source ↗

Generative AI and Data Governance

www.anu.edu.au

University official source

anu.edu.au

Hash and captured version

Generative AI and Data Governance · 2026-10-20T23:13:00Z

Representation unspecified in this historical record; do not assume an original-file hash.

605ff5a82dcfcd3a14bd3941de843ea6ebd4958ba1f5e0e5a5b7a83a5a2b8ec2

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

Official source ↗

Guide for students: best practice when using Generative AI

www.anu.edu.au

University official source

anu.edu.au

Hash and captured version

Guide for students: best practice when using Generative AI · 2026-10-20T23:13:00Z

Representation unspecified in this historical record; do not assume an original-file hash.

f4dafbf640430ff726327475c32331321c3f506becbdec299de38ac4cc4ccfda

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

Official source ↗

FAQs about AI

learningandteaching.anu.edu.au

University official source

learningandteaching.anu.edu.au

Hash and captured version

FAQs about AI · 2026-10-20T23:13:00Z

Representation unspecified in this historical record; do not assume an original-file hash.

dc615efccb81982ad6bf53da2256fed2eed85fc4859c7ae84f292a68aaca19d8

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

Official source ↗

Use of Generative AI Tools in Assessment - Guidance for ANU Law Students

law.anu.edu.au

University official source

law.anu.edu.au

Hash and captured version

Use of Generative AI Tools in Assessment - Guidance for ANU Law Students · 2026-10-20T23:13:00Z

Representation unspecified in this historical record; do not assume an original-file hash.

1feb20643879b74b6a7e8726b607225f7a1cacaf6034052d5f2b143eba291c1c

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

Official source ↗

Generative AI and Assessment - ANU Learning and Teaching

learningandteaching.anu.edu.au

University official source

learningandteaching.anu.edu.au

Hash and captured version

Generative AI and Assessment - ANU Learning and Teaching · 2026-10-20T23:13:00Z

Representation unspecified in this historical record; do not assume an original-file hash.

65b8c9cf2abce99c6cfce4c62d333ff23eb71ee042e4c4b90f70668acf97a049

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

Official source ↗

AI Essentials - ANU Learning and Teaching

learningandteaching.anu.edu.au

University official source

learningandteaching.anu.edu.au

Hash and captured version

AI Essentials - ANU Learning and Teaching · 2026-10-20T23:13:00Z

Representation unspecified in this historical record; do not assume an original-file hash.

368d26ba42d07f956756ccb6bc4566db9db7dbc1996fa6195efe3f653bd1afca

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

Official source ↗

Referencing and generative AI - Artificial Intelligence including generative AI LibGuide

libguides.anu.edu.au

University official source

libguides.anu.edu.au

Hash and captured version

Referencing and generative AI - Artificial Intelligence including generative AI LibGuide · 2026-10-20T23:13:00Z

Representation unspecified in this historical record; do not assume an original-file hash.

40f47aa9174485cad2a1c6e1e3d7d76e9ff77aa33e464291cf9aebbe186f7396

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

Official source ↗

ChatGPT: What ANU academics need to know

learningandteaching.anu.edu.au

University official source

learningandteaching.anu.edu.au

Hash and captured version

ChatGPT: What ANU academics need to know · 2026-10-20T23:13:00Z

Representation unspecified in this historical record; do not assume an original-file hash.

1223317d00fb55b63da5ef7c2a769d59dc50f25527eccf1efbe2e75ac3a964c6

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

Official source ↗

AI Guidelines for CAP Students

asiapacific.anu.edu.au

University official source

asiapacific.anu.edu.au

Hash and captured version

AI Guidelines for CAP Students · 2026-10-20T23:13:00Z

Representation unspecified in this historical record; do not assume an original-file hash.

3e258f61a779fcc14517bbdd57e55f6757e2460178ee0f81e84e2ba936573d93

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

Official source ↗

Chat GPT and other generative AI tools: What ANU academics need to know (PDF)

learningandteaching.anu.edu.au

University official source

learningandteaching.anu.edu.au

Hash and captured version

Chat GPT and other generative AI tools: What ANU academics need to know (PDF) · 2026-10-20T23:13:00Z

Representation unspecified in this historical record; do not assume an original-file hash.

596f3a57445d657da0ef2a05eebca2b5354e6885b540ff576fc8fd8b4f8e0667

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

Official source ↗

Microsoft Copilot AI | Services

services.anu.edu.au

University official source

services.anu.edu.au

Hash and captured version

Microsoft Copilot AI | Services · 2026-10-20T23:13:00Z

Representation unspecified in this historical record; do not assume an original-file hash.

0e36d1aca4efd18cc79cdece23d91a8e8d5bfb7c39bce9bfb325aad061646123

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

Official source ↗

Academic Integrity Rule 2021 for Learners | Services

services.anu.edu.au

University official source

services.anu.edu.au

Hash and captured version

Academic Integrity Rule 2021 for Learners | Services · 2026-10-20T23:13:00Z

Representation unspecified in this historical record; do not assume an original-file hash.

3f2d83db751409c6e737c61bc386189c9dc9e7d8351888e4f044f1c8534ade2d

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

Official source ↗

Academic Integrity Rule 2021 (Australian National University)

www.legislation.gov.au

University official source

legislation.gov.au

Hash and captured version

Academic Integrity Rule 2021 (Australian National University) · 2026-10-20T23:13:00Z

Representation unspecified in this historical record; do not assume an original-file hash.

6376e448c385da241e70efcd784820927273e7048fc613c620878c1edd2c3c00

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

Official source ↗

AI writing detection now available in Turnitin - TELT @ SMP

telt.weblogs.anu.edu.au

University official source

telt.weblogs.anu.edu.au

Hash and captured version

AI writing detection now available in Turnitin - TELT @ SMP · 2026-10-20T23:13:00Z

Representation unspecified in this historical record; do not assume an original-file hash.

58edd97e0948aea446d1cbeff8d68f841dc1aa0fcabd5df8f82c5d856a4ed78d

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
v6-target-r7-candidate-20261021
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.

  • Primary legislative instrument F2021L00997 is preserved as made in July 2021 (commenced 1 December 2021). While current ANU Services learner guidance confirms ongoing operation of the 2021 Rule, independent verification that no subsequent compilation or amending rule was enacted between 2021 and 2026 is retained as an explicit temporal currentness gap.
  • No standalone central university-wide exam AI policy was established in this review; exam conditions depend on individual course assessment outlines and invigilator instructions.
  • No explicit university-wide AI policy for admissions was identified in the reviewed official corpus.
  • Inherited Claim text for claim-anu-002b mentions 'without student consent', but the underlying raw evidence snippet ('academic staff are not permitted to upload student data or academic work to GenAI platforms. Therefore, it is not currently possible to generate student feedback') does not establish a student-consent exception for staff feedback. Canonical Claim text and raw evidence are preserved intact; new presentations strictly separate staff upload restrictions from user personal data consent rules.
        Current reviewed JSON
        Questions students ask

        Questions students ask

        Can I use AI for my coursework?

        ANU delegates AI assessment permissions to course convenors. Students must check class summaries and assessment outlines for specific AI rules in every course. Unapproved drafting is prohibited.

        View evidence →
        Do I need to disclose AI use?

        For ANU Law School submissions where AI tools are used, students must declare the application names, purpose, and extent/frequency; the declaration should be in the first footnote. For other ANU assessments, follow the course-specific instructions.

        View evidence →
        Can I upload sensitive data?

        Academic staff are not permitted to upload student data or academic work to GenAI platforms. Users must not use AI to collect, use, store or disclose personal information without express consent of the individual.

        View evidence →
        Which tools can I access?

        Microsoft Copilot Enterprise (with Commercial Data Protection) is available to all ANU staff and students via SSO. Adobe Firefly is provided for staff and specific creative cohorts.

        View evidence →
        What are the examination rules?

        This review did not establish a current central ANU rule specifically permitting or prohibiting generative AI in examinations. Check the instructions for the particular assessment; general misconduct rules do not establish a blanket exam ban.

        View evidence →
        What rules apply to research and publication?

        Under Australian Research Council (ARC) policy cataloged by ANU Library, researchers applying for ARC grants must ensure the integrity and accuracy of applications and must disclose any use of generative AI. Under Association for Computing Machinery (ACM) policy cataloged by ANU Library, generative AI tools such as ChatGPT may not be listed as authors of an ACM published work; AI-assisted content creation must be fully disclosed.

        View evidence →
        What counts as academic misconduct?

        Under the ANU Academic Integrity Rule 2021 framework, suspected breaches are addressed via informal review or formal inquiry; students are entitled to written notice and a support person (such as ANUSA or Dean of Students); findings are classified into No Breach (no record), Poor Academic Practice (unintentional/careless; penalties include reprimand, re-assessment, or mark reduction up to 25% of assessment marks, or up to 10% for first 48 units of undergraduate coursework; recorded internally, not on transcript), or Academic Misconduct (serious/intentional; formal penalties up to course fail, facility denial, suspension, or exclusion).

        View evidence →
        What guidance applies to teaching?

        Teaching staff are encouraged to design assessments that emphasize authentic learning and provide clear syllabus policies regarding generative AI.

        View evidence →
        What security and procurement requirements apply?

        All technical solutions being used for University business and/or on ANU managed devices must be approved by the University.

        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?

        33 · Full policy evidence

        Full policy evidence →