We have developed key aspects of our regulatory approach in consultation with higher education stakeholders. We recognise that consultation influences the quality of our relations with the higher education sector and can be an important way of collecting evidence which allows us to meet the objects of the Tertiary Education Quality and Standards Agency Act 2011 (TEQSA Act).

Current consultations

There are no current consultations at TEQSA.

Previous consultations

Regulatory Risk Framework consultation

(Closed 30 April 2026)

TEQSA is seeking feedback on updates to the Regulatory Risk Framework (RRF) to test sector understanding of the RRF as one of the key inputs informing our regulatory responses and decision making in relation to matters of higher education quality and provider-level risk.

TEQSA has developed a set of consultation questions to support focused feedback on the draft RRF. Respondents may choose to comment on any of the questions that are relevant to them and are also welcome to provide additional feedback.

• Download the draft consultation paper (PDF, 460 KB)

Fees and charges consultation

(Closed 26 September 2025)

TEQSA is proposing an updated version of the Cost Recovery Implementation Statement (CRIS) with adjustments to our fees and charges to take effect on 1 January 2026.

Guidance notes consultation

(Closed 22 August 2025)

TEQSA is working to improve the efficiency of our regulatory operations and support greater self-assurance within the higher education sector by continuing to enhance our suite of guidance notes.

To support this project, TEQSA opened consultation on the following 3 draft documents:

• Course approval and accreditation
• Orientation and progression
• Qualifications and certification.

Guidance notes consultation

(Closed 18 July 2025)

TEQSA is working to improve the efficiency of our regulatory operations and support greater self-assurance within the higher education sector by continuing to enhance our suite of guidance notes.

To support this project, TEQSA opened consultation on the following 3 draft documents:

• Information for prospective and current students
• Information management
• Representation.

Interim regulatory guidance

(Closed 27 March 2025)

TEQSA is seeking feedback on new regulatory guidance that has been developed to support safety and wellbeing in higher education.

TEQSA is consulting on 2 documents:

• Interim statement of regulatory expectations: Student grievance and complaint mechanisms (PDF, 183 KB)
• Interim guidance: Managing external actors (PDF, 148 KB).

Fees and charges consultation

(Closed 28 October 2024)

In accordance with the Australian Government Charging Policy, TEQSA annually reviews the operation of our Cost Recovery Implementation Statement (CRIS).

Following an internal review of the 2023 version of the CRIS, TEQSA has developed a consultation paper for the sector.

This paper outlines several proposed adjustments to ensure our fees and charges (to take effect from 1 January 2025) accurately reflect the cost of our regulatory activities.

• Download the consultation paper

Revised service charter

(Closed 20 May 2024)

TEQSA commenced a service charter review in early 2023. The first phase of consultation was a stakeholder survey about our service charter in August 2023.

We’ve now developed a revised service charter informed by the survey results.

• Read more about the revised service charter

This was the second phase of consultation and sought further feedback from stakeholders.

Draft stakeholder engagement strategy

(Closed 20 May 2024)

To support the development of a draft stakeholder engagement strategy.

• Draft stakeholder engagement strategy consultation paper (MS Word, 160 KB)

Fit and proper person requirements

(Closed 20 May 2024)

Consultation for a proposal to amend the Tertiary Education Quality and Standards Agency Fit and Proper Person Determination 2018 (Determination).

• Download the consultation paper (MS Word, 157 KB)

Guidance notes on diversity and equity, student grievances and complaints, and wellbeing and safety

(Closed 15 March 2024)

In 2023, TEQSA consulted stakeholders on the following guidance notes:

• Diversity and equity
• Student grievances and complaints
• Wellbeing and safety.

These notes focus on 3 sections of the Threshold Standards that are unified in their intent to protect and provide support to students. TEQSA sought further stakeholder feedback that identified:

• what additional information can be included in the guidance note to make it useful and up to date
• any points or areas that require clarity
• suggestions to assist providers in delivering effective self-assurance.

Guidance notes on Staffing, Corporate Governance, and Corporate Monitoring and Accountability

(Closed 9 February 2024)

To support TEQSA’s ongoing work to improve the efficiency of our regulatory operations and support greater self-assurance within the higher education sector, we are continuing to enhance TEQSA’s suite of guidance notes for higher education providers.

• Corporate governance
• Corporate monitoring and accountability
• Staffing

Guidance notes on course design, learning outcomes and assessment and learning resources and educational support

(Closed 20 November 2023)

To support TEQSA’s ongoing work to improve the efficiency of our regulatory operations and support greater self-assurance within the higher education sector, we are continuing to enhance TEQSA’s suite of guidance notes for registered higher education providers.

• Course design
• Learning outcomes and assessment
• Learning resources and educational support

Assessment reform for the age of artificial intelligence

(Closed 20 October 2023)

TEQSA invited feedback on the proposals outlined in the Assessment reform for the age of artificial intelligence discussion paper, including the principles and propositions.

• Download the discussion paper

At the end of the consultation period, TEQSA and the lead authors of this document will consider all feedback received before publishing the final guidelines in late November 2023.

If you have any questions about this consultation, or the guiding principles, please email us at integrityunit@teqsa.gov.au.

Fees and charges consultation

(Closed 25 September 2023)

In accordance with the Australian Government Charging Policy, TEQSA annually reviews the operation of our Cost Recovery Implementation Statement (CRIS).

Following an internal review of the 2022 version of the CRIS, TEQSA developed a consultation paper for the sector. This paper outlined several proposed adjustments to ensure our fees and charges for 2024 accurately reflect the cost of our regulatory activities and the changes we’ve made to streamline processes since the 2022 version of the CRIS was developed.

• Download the consultation paper (PDF, 1.3 MB)

See: How we consult on fees and charges for more information

Service charter review survey

(Survey closed 25 September 2023)

Insights from the survey will help us to develop a draft service charter, which we will release for further comment at a later date. Following this consultation, TEQSA will consider stakeholder feedback before adopting our revised service charter.

See: Service charter review for more information.

Guidance notes on facilities and infrastructure, academic monitoring and academic and research integrity

(Closed 10 August 2023)

To support TEQSA’s ongoing work to improve the efficiency of our regulatory operations and support greater self-assurance within the higher education sector, we are continuing to enhance TEQSA’s suite of guidance notes for registered higher education providers.

• Academic and research integrity
• Academic monitoring, review and improvement
• Facilities and infrastructure (Summary of external consultation on Guidance Note: Facilities and infrastructure, PDF, 173 KB)

Consultation for sexual harm good practice note

(Closed 13 July 2023)

Since the release of the Good Practice Note: Preventing and responding to sexual assault and sexual harassment in the Australian higher education sector (the 2020 good practice note), TEQSA acknowledges there has been significant work across the sector to embed strategies to prevent and respond to sexual assault and sexual harassment, however, the issue remains a key risk.

• Sexual harm good practice note consultation (MS Word, 159 KB)

Guidance notes on diversity and equity, student grievances and complaints, and wellbeing and safety

(Closed 13 July 2023)

To support TEQSA’s ongoing work to improve the efficiency of our regulatory operations and support greater self-assurance within the higher education sector, we are continuing to enhance TEQSA’s suite of guidance notes for registered higher education providers.

• Diversity and Equity
• Student Grievances and Complaints
• Wellbeing and Safety

Guidance notes on academic governance, recognition of prior learning, and delivery with other parties

(Closed 7 March 2023)

To support TEQSA’s ongoing work to improve the efficiency of our regulatory operations and support greater self-assurance within the higher education sector, we are continuing to enhance TEQSA’s suite of guidance notes for registered higher education providers.

• Summary of external consultation on Guidance Note: Academic Governance (PDF, 163 KB)
• Summary of external consultation on Guidance Notes: Credit and Recognition of Prior Learning and Delivery with Other Parties (PDF, 180 KB)

Consultation for proposed amendments to Register Guidelines 

(Closed 16 December 2022)

TEQSA has commenced a consultation process for proposed amendments to the Tertiary Education Quality and Standards Agency (Register) Guidelines 2017 (Register Guidelines).

The reason for the proposed amendments is to promote transparency regarding TEQSA's regulatory decisions and actions and remove any doubt about which trading names the Register must include in respect of registered providers' higher education operations.

• Further details

Summary of feedback

TEQSA received two submissions during the consultation period. 

Both submissions supported the inclusion of the additional information proposed in the consultation paper. One submission opposed the removal of previous trading names and the other supported it (while noting that this information may be useful to future students).

Guidance Note: Research requirements for Australian universities

(Closed 7 September 2022)

To support TEQSA’s ongoing work to improve the efficiency of our regulatory operations and support greater self-assurance within the higher education sector, we are enhancing TEQSA’s suite of guidance notes for registered higher education providers.

This work will reinforce the role of guidance notes to provide guidance that focuses on a specific section of the Higher Education Standards Framework (2021) while drawing attention to connections with other sections and highlighting potential compliance issues.

Following sector feedback during consultation last year, this project will ultimately reduce the number of guidance notes from 32 to 28 to ensure each guidance note aligns with a section of the Standards framework. Sector feedback has also informed the development of a new, simpler template for guidance notes.

The draft guidance note outlines what TEQSA will look for when considering university research in relation to requirements outlined in the TEQSA Act and Higher Education Standards Framework (2021).

Summary of external consultation

• Download the summary of external consultation (PDF, 167 KB)

Revised Guidance Note: Research and Research Training

(Closed 6 July 2022)

To support TEQSA’s ongoing work to improve the efficiency of our regulatory operations and support greater self-assurance within the higher education sector, we are enhancing TEQSA’s suite of guidance notes for registered higher education providers.

This work will reinforce the role of guidance notes to provide guidance that focuses on a specific section of the Higher Education Standards Framework (2021) while drawing attention to connections with other sections and highlighting potential compliance issues.

Following sector feedback during consultation last year, this project will ultimately reduce the number of guidance notes from 32 to 28 to ensure each guidance note aligns with a section of the Standards framework. Sector feedback has also informed the development of a new, simpler template for guidance notes.

The Guidance Note outlines, with regard to the Higher Education Standards Framework, what TEQSA will look for and common issues associated with Research and Research Training.

• Summary of external consultation

Register and information guidelines

(Closed 26 November 2021)

The Register Guidelines is a legislative instrument that sets out the information that TEQSA must enter on the National Register in respect of each registered higher education provider.

The Information Guidelines is a legislative instrument that sets out the Commonwealth authorities and the State or Territory authorities to which TEQSA may disclose higher education information under sections 189 and 194 of the TEQSA Act.

• Further details

Revised compliance guides

(Closed 10 November 2021)

On 1 July 2021 the new Higher Education Standards Framework (Threshold Standards) 2021 (HESF) came into effect. 

TEQSA is reviewing the current suite of guidance notes to ensure they reflect the requirements of the new HESF. 

As part of this review, TEQSA developed a new template to streamline our guidance materials.

• Further details

TEQSA fees and charges consultation

(Closed 3 June 2021)

On 30 April 2021, TEQSA released the TEQSA Fees and Charges Consultation Paper for feedback from the sector. The consultation paper outlined the details of TEQSA’s proposed approach for transitioning to the new cost recovery arrangements.

• Further details

Draft legislative instrument

(Closed 28 April 2021)

In February 2021, the Australian Parliament passed the Higher Education Legislation Amendment (Provider Category Standards and Other Measures) Bill 2020. The Bill gives effect to the Australian Government’s decision to implement all 10 recommendations arising from the Provider Category Standards review conducted in 2019. 

Among other things, the Bill amends the Tertiary Education Quality and Standards Agency Act 2011 (TEQSA Act) to allow TEQSA to make a determination of the matters which it must have regard to when assessing the quality of the research undertaken by a provider which is registered, or applies to be registered, in the Australian University category. By approval from the Minister, this determination becomes a legislative instrument.

TEQSA proposes to make a determination which sets out a number of matters which are relevant to an assessment of research quality. The list is non-exhaustive and does not specify benchmarks or thresholds for quality; it is a determination of considerations in an assessment of research quality.

• Further details

Discussion paper: Making and assessing claims of scholarship and scholarly activity 

(Closed 14 December 2020)

TEQSA sought to review whether its current approach to assessing claims of scholarship and scholarly activity (as described in the Guidance Note on Scholarship) is adequate, or if the approach needs to be reconceptualised. The purpose of this discussion paper was to set out, for consideration by the sector and other stakeholders, draft principles that were proposed to guide providers in making claims related to scholarship, and to inform TEQSA’s assessments of such claims.

Information Guidelines

(Closed 27 March 2020)

TEQSA sought feedback on the Commonwealth, State and Territory bodies that we proposed to include in an update to our Information Guidelines. 

The Information Guidelines is a legislative instrument that sets out the Commonwealth authorities and the State or Territory authorities to which TEQSA may disclose higher education information under sections 189 and 194 of the Tertiary Education Quality and Standards Agency Act 2011.

Fit and proper person considerations

(Closed 1 December 2017)

As a consequence of the passing of the Education Legislative Amendment (Provider Integrity and Other Measures) Act 2017, TEQSA is able to specify matters that the agency may have regard to in deciding whether a person is a fit and proper person for the purposes of the Tertiary Education Quality and Standards Agency Act 2011.

Sector consultation on proposed changes to the publication of TEQSA’s decisions

(Closed 14 March 2017)

TEQSA sought feedback, via a consultation paper, on the proposed changes to the publication of regulatory decisions.

The consultation focused on proposed changes to the frequency and way we published decisions.

Questions about whether we should publish more information, including rejections, involve an important balance between the interests of higher education providers, students and other stakeholders.

The developments in our practices and in the approaches of other agencies meant that it was timely to revisit these issues. 

We proposed that a simplified set of principles be adopted, informed by approaches of other Australian Government agencies, to guide our future approach.

As part of the consultation process, we will carefully consider all feedback before we make any changes to our approach. We are committed to ensuring that all stakeholders have an opportunity to provide us with their views.

Summary of consultation

• View the summary report of the consultation process for the public reporting of TEQSA's decisions

Principles of consultation

Our approach to consultation is guided by the regulatory principles of: reflecting risk, proportionality and necessity. Our consultations are also guided by the principles outlined in TEQSA’s approach to consultation.

WEBSITE BLOCKED

Access to this website has been blocked because it has been found to facilitate a contravention of section of the provisions of the Tertiary Education Quality and Standards Agency Act 2011 (TEQSA Act) regulating academic cheating services.
 

The relevant provisions in the TEQSA Act

• make it an offence for any person to provide or advertise academic cheating services relating to the delivery of higher education in Australia, whether that person is in Australia or elsewhere
• allow TEQSA to apply under section 127A to the Federal Court for an injunction requiring carriage service providers to take steps to disable access to websites found to contravene or facilitate a contravention of sections 114A or 114B of the TEQSA Act
• provide for financial and custodial penalties where an offence is proven of up to 500 penalty units, 2 years imprisonment, or both. The TEQSA Act distinguishes between cheating services provided on a commercial basis, and where the cheating service is provided without payment.

If you use academic cheating services you might not learn all the skills you need for your career. You also risk losing your money, your enrolment, and even your student visa. In some cases, you could be blackmailed by the cheating service providers who might threaten to tell your institution or a future employer that you cheated.
 

Cheating is never the right answer.

Why are we blocking academic cheating websites?

Australia’s anti-cheating laws make it an offence to provide or advertise academic cheating services in higher education, with penalties of up to 2 years’ imprisonment or fines of up to 500 penalty units ($111,000 on 30 June 2021) or both.

TEQSA  is working to disrupt access to these sites to protect students and the integrity of higher education.

Which academic cheating services have been blocked?

• View the alphabetical list of blocked illegal cheating websites

Complain about a commercial academic cheating website

How to complain

You can help to stamp out academic cheating. Complete the form to complain about suspected commercial academic cheating services (cheating websites).

If you are unable to submit your complaint using the online form, you can make a complaint by emailing TEQSA at integrityunit@teqsa.gov.au.

TEQSA may use any information it receives from any person who contacts it about suspected commercial academic cheating services in taking enforcement action (including in the pursuit of civil penalty or criminal penalty proceedings pursuant to sections 114A and 114B of the Tertiary Education Quality and Standards Act 2011), either independently or in conjunction with other agencies.

Complain or raise concerns about the blocking of a website

You can tell us why you think a particular website shouldn’t be blocked (you don’t have to give us any personal details) or that you have concerns about the blocking, by submitting the form you can access using the link above.

If you are unable to submit your complaint or concerns using the online form, you can make a complaint by emailing TEQSA at integrityunit@teqsa.gov.au.

TEQSA may use any information it receives from any person who contacts it about the disruption of access to an online location in taking enforcement action (including in the pursuit of civil penalty or criminal penalty proceedings pursuant to sections 114A and 114B of the Tertiary Education Quality and Standards Act 2011), either independently or in conjunction with other agencies.

This page contains resources to support institutions, staff and students in considering the potential impacts and benefits generative artificial intelligence (gen AI) tools pose for teaching, learning and assessment. TEQSA understands the uniqueness of each provider’s circumstances and therefore offers the following case studies and resources as examples of approaches being taken, both in Australia and beyond, for consideration. The resources on this page are not intended as guidance but rather seek to share approaches and practice.

Author: Benito Cao, The University of Adelaide

Focus area: Making academic integrity visible

The sudden irruption of generative artificial intelligence (gen AI) in higher education has sparked widespread concerns regarding the viability of essays as a form of assessment. Indeed, the argument often goes that large language models (LLMs) such as ChatGPT signal the death of the essay. But do they? Will they? Or, to paraphrase Mark Twain, is it that reports of the essay’s death are greatly exaggerated?

This case study outlines a pedagogical initiative designed to promote academic integrity and secure the essay in the age of gen AI. The initiative reimagines the ‘two-lane’ approach that proposes the combination of secured assessments (lane 1) with unsecured open assessments (lane 2).

The reimagined approach resembles the two-lane model in its partial reliance on in person assessments to validate student learning. Yet, it challenges the unrestricted nature of the Lane 2 approach by illustrating the value and validity of a ‘middle lane’ approach which focusses on the ecosystem to foster and facilitate authentic learning (Curtis 2025). The initiative relies on a pedagogical ecosystem designed to develop trust between students and teachers notwithstanding that academic integrity requires that we ‘trust but verify’ in cases of potential academic misconduct.

The pedagogical ecosystem includes the following elements:

1. an exploration of the potential for gen AI tools to fabricate information, with illustrations of real-world ‘hallucinations’
2. the provision of clear guidelines, with references to university policies and industry standards to showcase the rationale and relevance of the guidelines
3. the requirement to include a gen AI appendix when students use gen AI in the production of their essays
4. a reminder that students are expected to fully understand every aspect of their essay, and that if there is a concern about the use of gen AI tools exceeding the assessment guidelines, they may be asked to discuss the assignment before the mark is finalised
5. explicit advice to keep drafts, notes, annotated readings and any other materials students have used, as evidence of how their essay has been produced in case its authorship is questioned
6. the reliance on secured (in-person) assessments, worth between 30% and 50% of the overall mark, to help validate student learning and to compare with the essay preliminary mark if there are academic concerns regarding the production of the essay.

This pedagogical ecosystem is designed to enable the (relatively secured) implementation of a ‘middle lane’ approach, that permits a limited use of gen AI. Specifically, students are allowed to use gen AI tools to assist with idea generation and language expression. For example, I tell students:

• if they struggle to come up with ideas for their essays, they can use gen AI but any ideas suggested by the tool must be validated
• while they can use gen AI to assist with language expression, they should not allow the tool to take control of the narrative, that the narrative should reflect their own voice.

In essence, students are allowed a limited use of gen AI but are expected to remain the authors of their essays and to be transparent regarding their use of gen AI tools. Students are reminded of this basic expectation of transparency in the assignment submission portal. This is the last thing they read before uploading their essay:

Don't forget to include a gen AI appendix if you have used gen AI tools (for example, ChatGPT, Copilot, Gemini, Claude, Grammarly, etc.)  in the production of the essay. The absence of this appendix is equivalent to stating: I did not use GenAI. If this statement turns out to be false, this would constitute a breach of academic integrity. Remember the slogan: Don't be sorry, just declare it.

The approach which I have titled, Don't be sorry, just declare it, reflects the integration of four normative principles: caution, trust, relevance and transparency  (Cao 2025). It is a slogan used by Australian Customs and Biosecurity warning people who arrive in Australia to declare all goods they might not be permitted to bring into the country, rather than apologise afterwards for the lack of a declaration.

The evidence suggests that this approach can go a long way in addressing some of the most urgent pedagogical challenges posed by gen AI, particularly concerns with academic integrity. The evidence also suggests that this approach can improve security of the essay and thus contribute to its preservation as a valuable form of assessment in the age of gen AI.

References

Cao, B. (2025). Don’t Be Sorry, Just Declare It: Pedagogical Principles for the Ethical Use of ChatGPT, Master Bullshit Artist of Our Time. In: 11th International Conference on Higher Education Advances (HEAd’25). Valencia, 17-20 June 2025.

Curtis, G. J. (2025). The two-lane road to hell is paved with good intentions: why an all-or-none approach to generative AI, integrity, and assessment is insupportable. Higher Education Research and Development. (Published online: 18 March 2025).

Authors: Tanya Henry and Associate Professor Christine Slade, Institute for Teaching and Learning Innovation (ITaLI), The University of Queensland

Focus area: Assessment design

The Lead through learning strategy 2025 - 2027 (the Strategy) is a whole-of-university strategy at The University of Queensland (UQ) aimed at addressing the impact of generative artificial intelligence (gen AI) in education and sits within the UQ’s AI in Education Action Plan (2025 – 2027). This initiative is a partnership between 5 faculties and the Deputy Vice-Chancellor (Academic) (DVC(A)) portfolio which aims to ensure graduates can use gen AI ethically and responsibly and that assessment practices assure learning outcomes.

Learning designers are embedded in the faculties for 3 years to spearhead the cultural change in assessment and teaching practices in the light of gen AI.  As this is one piece of a broader program of work within the Strategy, the Learning Design (LD) team is led by a Strategic Lead based in the central teaching unit, who provides leadership and mentorship to the team of faculty-based learning designers and is the conduit between LDs and the DVC(A).

The Strategy has 2 main goals:

• Preparing students for responsible gen AI use by equipping students with ethical and practical skills they can use in their studies, careers and communities, and preparing them to lead and shape the future of gen AI integration in their fields.
• Maintaining the integrity of the learning process by ensuring that academic standards are upheld through secure and credible assessment practices.

Partnering with faculties to achieve these goals enables contextualised approaches within disciplines, with each faculty developing an operational plan that reflects their individual context. The central teaching and learning unit, the Institute for Teaching and Learning Innovation (ITaLI), complements this approach, upskilling gen AI use and assessment transformation, providing institutional guidance and facilitating collaboration.

Within faculties learning designers, in collaboration with teaching staff, are developing and delivering workshops to support staff in using gen AI including how to enhance the validity and security of assessments. Across faculties staff are engaging in communities of practice including the establishment of an AI Steering Committee to explore the development of a whole-of-faculty gen AI curriculum.

Key lessons or points for implementation

• Define success and leverage existing data:
  Clearly articulate what success looks like in advancing the project’s core goals and how progress will be measured. Engage with colleagues who can identify existing data sources and explore future possibilities to support evidence-based decision-making.
• Integrate with other initiatives to maximise impact and minimise change fatigue:
  Assessment transformation should align with other strategic initiatives, such as inclusive design and indigenising the curriculum, to create synergies rather than silos. This approach fosters collaboration, reduces duplication of effort and helps avoid staff fatigue by streamlining change.
• Support educators through incremental, reflective change:
  Meet educators where they are and guide them through manageable, meaningful steps in assessment reform. Celebrate small wins, reflect on what works and what doesn’t, using a continuous improvement approach.
• Contextual partnerships across the university:
  Connecting both top down and ground up goals is important to support staff buy-in where success requires teaching and assessment practices to change.

Authors: Professor Ruth Greenaway, Dr Zachery Quince, Dr Joanne Munn, Southern Cross University

Focus area: Assessment design

Generative artificial intelligence (gen AI) enables students to generate sophisticated academic outputs with minimal effort, challenging traditional assessment methods and raising concerns about academic integrity. Southern Cross University (SCU) has responded to this challenge by developing the Assessment Adaptation Model – Gen AI (AAM-Gen AI), a comprehensive, pedagogically grounded model designed to help educators adapt assessments to be resilient and meaningful in the gen AI era.

Gen AI tools have made traditional assessment vulnerable to misuse, necessitating systemic changes that move beyond reactive policies and detection-based approaches, advocating for proactive, authentic assessment designs that foster deep learning, critical thinking and ethical reasoning.

Authentic assessments, mirroring real-world complexities that require personal engagement, are less susceptible to gen AI misuse and promote transferable graduate skills. SCU’s AAM-Gen AI model arises from this context, aiming to align assessment design with both academic integrity and the evolving digital landscape.

The AAM-Gen AI model consists of seven interrelated components spanning the assessment lifecycle. It promotes a holistic, proactive approach that integrates gen AI considerations into every stage of assessment, encouraging transparent, ethical and capability-building practices rather than punitive measures.

• Design: 
  Craft assessment tasks that emphasise higher-order thinking, contextual relevance and personal engagement reducing gen AI misuse and enhance learning.
• Analyse: 
  Critically evaluate assessments using a security risk matrix to identify and mitigate vulnerabilities to gen AI exploitation.
• Act: 
  Implement strategic changes like multi-stage tasks using security rating scales to strengthen assessment integrity.
• Inform: 
  Clearly communicate gen AI usage policies to students to support fairness and ethical learning.
• Educate: 
  Develop students’ AI literacy and critical thinking to foster ethical and informed engagement with gen AI tools.
• Check: 
  Verify authenticity through nuanced, evidence-based approaches while promoting a culture of trust and accountability.
• Evaluate: 
  Continuously review and refine assessment practices to ensure alignment with learning goals and responsiveness to gen AI developments.

Key lessons or points for implementation

• Spend time considering current assessment and proactively redesign with gen AI in mind.
• A security risk matrix is a conversation starting point to reconsider assessment design, it is not a definitive measure of assessment security.

Assessment Adaptation Model-Gen AI (AAM-Gen AI)

 

Guy Curtis, University of Western Australian

Since the release of ChatGPT in November 2022, a major concern for many academics has been students copying and pasting text produced by generative artificial intelligence (gen AI) programs into their assignments without acknowledgment. Such unacknowledged copying and pasting meets the traditional definition of plagiarism and is a case of academic misconduct.

Substantiating cases of academic misconduct requires proving on the balance of probabilities that misconduct has occurred. This means that the evidence shows that misconduct is more likely to have occurred than not. A detected case is one that meets this standard of proof and is not overturned on appeal (Ellis et al., 2020). Finding sufficient evidence to prove plagiarism from gen AI is more challenging than substantiating plagiarism from published sources.

In general, there is a strong case that substantive and systematic assessment redesign is needed in the age of gen AI (Corbin et al., 2025). In particular, highly secure assessments should be used to assess or verify key learning outcomes at a program level. In so doing, excellent guidance can be found in the University of Sydney’s Two-lane approach where assessments in lane one are highly resourced and secure and would occur at key points in a course (or unit) to gain assurances of student learning outcomes  and assessments to facilitate learning which are not as highly resourced or secure would be in  the more open lane 2  (Bridgeman, Liu, & Weeks, 2024; Liu & Bridgeman, 2023). Using artificial intelligence tools responsibly in your studies and assessments places take-home written assessments, which would typically be a concern for instances of plagiarism, in the “open” (lane 2) category, where gen AI use is permitted but must be acknowledged.

In applying the two-lane approach to a written assessment, it is still necessary to detect instances of plagiarism in the form of unacknowledged inclusion of gen AI content. In addition, it has been argued that for educational reasons, in limited circumstances, educators may need to restrict the use of gen AI in some written assessments that are not completed under closely supervised in-class conditions (Curtis, 2025). Because of this, some capacity to detect plagiarism from gen AI is needed.

Given that assessment security involves both making it more difficult to engage in misconduct, and easier to detect misconduct, an important consideration is whether take-home written assessments can be made more secure.

Securing take-home written assessments

Pre-gen AI, a typical take-home written assessment, such as an essay, would be completed by a student in their own time on their own device and they would only submit a completed piece of work, such as a Word or PDF document.  Although text-matching software provides security for such work against traditional copy-paste plagiarism, such assignments have always been relatively low in assessment security and vulnerable to academic misconduct such as contract cheating. They are particularly insecure when educators recycle assignment topics year after year.

Some measures have been suggested that can be put in place to make academic misconduct, such as contract cheating and copying and pasting from gen AI, easier to detect in take-home written assignments. As well as improving ease of detection, such barriers to academic misconduct may also dissuade students from attempting to breach assessment rules, such as not acknowledging the inclusion of content pasted from gen AI, because the ability to detect such actions is more obvious.

Strategy 1

To improve security of take-home written assessments, students can be required to maintain and submit a verifiable version history of their work (e.g., Berukov, 2025). Using technologies such as Google Docs , Microsoft 365, or Overleaf, students may be able to record and provide evidence of their process of compiling a take-home written assessment.

Strategy 2

Instruct students to work within programs, or with programs, that are designed to track the writing process. Commercial programs such as Cadmus, Inktrail, Turnitin Clarity, and Grammarly Authorship, use functions such as recording when content is pasted into the writing platform and regularly auto-saving work such that the process of writing may be effectively “replayed”. These programmes may have the added benefit of tracking important data that can be used to identify instances of contract cheating, such as login times, durations and IP addresses.

Using techniques such as monitoring version history and writing-in platforms provides educators with an opportunity to give students feedback on their process of writing an assessment, not just feedback on the final product.

Securing take-home written assessments is a first-line defence against unacknowledged plagiarism from gen AI. Nevertheless, further options must be considered in how to detect plagiarism from gen AI when such security measures are used, and when they are not.

Gen AI detection tools

Since the early 2000s academics have relied on technological support to detect plagiarism in the form of text-matching software. However, while text-matching software links text to verifiable published sources and other students’ assignments, text produced by gen AI tools is not stored or published and therefore cannot be linked to text in a student’s assignment.

In response to this problem, there have been various “gen AI detector” programs developed that attempt to estimate whether text was produced by gen AI. Such “gen AI detectors” examine linguistic and structural characteristics, including perplexity, burstiness and sentence structure, comparing them against patterns observed in both human and AI-generated text. This analysis leads to a probability estimate that text was AI-generated. However, people can display gen AI-style characteristics in their writing and gen AI tools can include “humanise” features or add-ons.

As a consequence gen AI detector programs can at times falsely indicate that human-written text was AI-generated. Such false positives are highly problematic in the context of investigating plagiarism from gen AI and can create a high stress situation for students who have been false accused of misconduct. As a result, institutions should use such detection tools with caution.

Current evidence for the accuracy of gen AI detector programs is mixed. These programs can reasonably distinguish 100% human-written and 100% gen AI-written text but are much less reliable when gen AI text is edited by a human, mixed with human-produced writing or documents are short (e.g. less than 300 words) (Weber-Wulff et al., 2023). Additionly, most detection programs can currently be bypassed by gen AI add-ons that “humanise”.

Issues to consider when using gen AI detection tools to identify instances of academic misconduct:

• The “AI score” alone is insufficient to bring an allegation of misconduct. Additional evidence is required to make an allegation of gen AI misuse.
  • low gen AI scores may also indicate gen AI-written text where an additional step has been taken to humanise the text. Again, any score, either high or low, is insufficient evidence by itself to allege misconduct
• “Humanisation” add-ons can bypass gen AI detectors.
• A score on a gen AI detector program is not the probability that the assignment was AI-generated. For example, if a detector has a 1% false-positive rate, it will flag 1 assignment in 100 as having a high score (e.g., 80-90%). If no students in a class of 100 used gen AI, one assignment will have a score of say 80-90% but the real probability that this assignment was AI-generated is zero.
• Unlicensed gen AI-detector program use that is free or via a personal subscription to a third-party platform may be a breach of your IT policy, privacy rules, intellectual property rules or copyright.
• To mitigate the risk of confirmation biases educators and investigators should look for evidence that disconfirms gen AI use in addition to evidence that may confirm gen AI use for assignments that have been flagged for gen AI content.

Clear signals of gen AI use in written assessments

• Obvious indicators of gen AI use that have unintentionally been pasted directly into an assessment such as,
  • “Certainly, I can give you an answer….”
  • “As a large language model…”
  • prompts used by students included with the text pasted into their assignment etc.
• Inability of the student to answer questions about the assignment content, e.g. post-assignment viva.
• Admission by student of unacknowledged use of gen AI.

Possible signals of genAI use in written assessments

• Disparity in student’s skill level — a mismatch is evident between the skill demonstrated in class and between assessments (e.g. supervised vs unsupervised, written vs oral). This may raise suspicions of other forms of misconduct, such as contract cheating.
• Made-up (mashed-up) references — a reference that does not match another source in a text-matching program is a potential clue that the reference is fabricated. A mashed-up reference may be highlighted by text-matching software with different sources matching the title and journal, for example. Fabricated references are typically academic misconduct in and of themselves and may constitute a breach of academic integrity without any need to prove that they occurred because of the use of gen AI.
• Perfectly written, mistake-free submissions—perfectly written, quickly produced submission may be a signal of misconduct (see Word document properties, information on copy/paste chips in write-in programs such as Cadmus or Inktrail, and/or the time taken to write or LMS metrics). It is important to remember that perfectly written text is not in itself a concern and may simply indicate good writing, permissible automated grammar checks and gen AI editorial assistance.
• Awkward, inappropriate or unusually sophisticated word-choices, verbosity — waffle may be a stylistic clue that indicates the use of a paraphrasing tool or gen AI use.
• Uniformly written responses — a lack of critical analysis that misses the point or fails to include key sources can be a signal of gen AI use.
• Responses based on the title of the work — questions or summaries of sources appear to address key words in the title and not the content of the work.
• Assignments that are produced quickly — assignments completed in extremely short time (see Word document properties for editing time or information on copy/paste chips and/or the time taken to write, or LMS metrics such as login times or time spent to answer a question).
• Text volume lacking edits — a large volume of text produced quickly with no or minimal edits (see Word document properties or information on copy/paste and/or the time taken to write, or LMS metrics).
• Lack of editing or evidence of writing process — text pasted into a document rather than typed (see Word document metadata [RSID codes] or information on copy/paste chips).
• Assignment structure — answers or assignment content are mainly written as bullet points or numbered lists.
• Whistleblowers — whistleblowers can be helpful in raising concerns about academic misconduct, their allegations must be independently verified with other evidence as it is possible for allegations to be malicious.

References

Bridgeman, A., Liu, D., & Weeks, R. (2024). Program level assessment design and the two-lane approach. 

Berukov, N. (2025). Version control: how I combat the rise of generative AI in the classroom. Nature.

Corbin, T., Dawson, P., & Liu, D. (2025). Talk is cheap: why structural assessment changes are needed for a time of GenAI. Assessment & Evaluation in Higher Education.

Curtis, G. J. (2025). The two-lane road to hell is paved with good intentions: why an all-or-none approach to generative AI, integrity, and assessment is insupportable. Higher Education Research & Development. 

Ellis, C., van Haeringen, K., & House, D. (2020a). Technology, policy and research: Establishing evidentiary standards for managing contract cheating cases. In T. Bretag (Ed.) A research agenda for academic integrity (pp. 138-151). Edward Elgar.

Liu, D., & Bridgeman, A. (2023). What to do about assessments if we can’t out-design or out-run AI?

Pitt, P., Dullaghan, K., & Sutherland-Smith, W. (2021). ‘Mess, stress and trauma’: Students’ experiences of formal contract cheating processes. Assessment & Evaluation in Higher Education, 46(4), 659-672. 

Weber-Wulff, D., Anohina-Naumeca, A., Bjelobaba, S., Foltýnek, T., Guerrero-Dib, J., Popoola, O., ... & Waddington, L. (2023). Testing of detection tools for AI-generated text. International Journal for Educational Integrity, 19(26).