A new Digital Education Council guide synthesises the candid perspectives of students drawn from universities across six global regions, and the message is consistent: they are already using AI, institutional rules are unclear, they are learning by trial and error, and they want their providers to lead rather than react. The same dynamics are unfolding in VET, with one difference: RTOs operate under a regulatory framework that turns the absence of clear AI governance into a compliance risk, and what that means for RTOs, trainers, assessors and learners is the subject of this article.
The Conversation Has Already Started
The Digital Education Council recently published a guide that should be required reading for every training provider in Australia, regardless of whether they consider themselves an AI-forward organisation. Titled "Student Voices on AI: An Actionable Guide for Institutions and Faculty," it draws on a global student panel convened across North America, Latin America, Europe, Africa, the Middle East and Asia Pacific, students from universities on six continents who were asked a simple but powerful question: what is your lived experience of AI in education right now?
The answers are not theoretical or speculative. They describe what is already happening in classrooms, on online platforms and in assessment submissions around the world. And while the guide was produced for the higher education sector, its findings carry direct and urgent implications for Australian vocational education and training. If students across six global regions report that they are already using AI in their daily study, that institutional policies are unclear, that they are learning AI skills through trial and error rather than structured guidance, and that they want their institutions to lead rather than react, then the same dynamics are unfolding in VET. The difference is that VET providers operate under a regulatory framework that makes the absence of clear AI governance not just an educational gap but a compliance risk.
The following table maps the report's central findings to the obligations they touch under the 2025 Standards.
|
What the report found |
What students are asking for |
The obligation it touches |
Why it matters for compliance |
|
In the absence of guidance, students use AI covertly, by their own private ethics |
Clear, consistent, shared rules so AI can be used openly |
Outcome Standard 2.1 (clear, accurate information) and 1.4 (fair, context-appropriate assessment) |
Vague or absent AI rules create inconsistent assessment outcomes that may not withstand scrutiny |
|
Overreliance on AI erodes critical thinking and reasoning |
Assessment that protects reasoning while allowing AI as a support |
Outcome Standard 1.4 (validity and sufficiency) and 1.1 (structured practice and feedback) |
Process-based evidence is more defensible than a polished output that may not be the student's own |
|
The appropriate role of AI differs by discipline |
Differentiated policies, not a blanket rule |
Outcome Standard 1.2 (current industry practice) |
A single permit-or-prohibit policy misrepresents the workplace each qualification prepares learners for |
|
Training covers prompting, not evaluation |
To be taught to verify and critically evaluate AI outputs |
Outcome Standard 3.1 (professional development for staff) and 1.2 (industry currency) |
Trainers cannot teach AI evaluation they have not been developed in; it is now a workforce capability |
|
Learning is fragmented; students want leadership |
A positive institutional culture and less reliance on detection |
Outcome Standard 4.1 (governing persons lead a culture of integrity) and 4.4 (monitoring and continuous improvement) |
Ignoring AI is a governance gap; a documented organisational position is now expected |
1. The Silence Is the Problem
The most striking finding in the report is not about what students are doing with AI. It is about what happens in the absence of institutional guidance. When providers fail to set clear expectations, students do not stop using AI. They use it discreetly, without shared norms, defaulting to their own personal ethics about what counts as acceptable use.
This creates an inconsistency that is corrosive to assessment integrity. In one classroom, a student uses AI to generate a first draft of a written assessment and considers this acceptable because nobody said otherwise. In the next, a student avoids AI entirely, assuming any use would be misconduct. Both are acting in good faith. Both are guessing. And the RTO has no defensible position, because it never told either of them what the rules were.
Under the 2025 Standards, this is not just an educational shortcoming. Outcome Standard 1.4 requires assessment to be conducted fairly, including that it is appropriate to the context and the student. Outcome Standard 2.1 requires that students have access to clear and accurate information, including assessment requirements. When an RTO has no AI use policy, or one so vague that students cannot determine what is permitted, it is creating the conditions for inconsistent assessment outcomes that may not withstand regulatory scrutiny. The report captures this precisely: students are calling for clarity, consistency and shared expectations so that AI can be used openly rather than covertly. In VET terms, they are asking for what the Standards already require: transparent rules, clearly communicated and consistently applied.
|
Silence Is Not Neutral |
|
An RTO without an AI position has not avoided making a decision. It has made one, by default, and handed the rule-setting to each individual student's private judgement. That is the opposite of the clear, consistent information Outcome Standard 2.1 requires and the context-appropriate assessment Outcome Standard 1.4 expects. Choosing not to have a policy is itself a policy, and it is the one least likely to survive an audit. |
2. Process Over Output: What Assessment Reform Actually Looks Like
The second major insight challenges how many RTOs design their assessments. Students themselves expressed concern that overreliance on AI compromises the development of critical thinking and reasoning. They are not asking for unlimited AI access. They are asking for assessment designs that protect core cognitive development while recognising AI's role as a support tool.
This inverts a common assumption. The usual fear is that students want AI to avoid doing the work. The reality these students describe is more nuanced: they want to use AI intelligently, but they also want to be sure they are actually learning something. As one student observed, if you cannot explain your work without AI's help, then you have not really learned it.
For VET providers, this points toward a fundamental redesign of assessment methodology. The traditional approach of assessing a final written output, whether a report, a case study or a project plan, is increasingly vulnerable to AI-assisted completion in ways that are difficult to detect and arguably impossible to prevent. The alternative, which students themselves advocate, is to assess the process rather than the product. In practice, that means assessments that require students to demonstrate their reasoning journey: graded drafts that show how thinking developed, reflection logs that explain what decisions were made and why, documented revisions that show how feedback was incorporated, and explicit disclosure of where and how AI tools were used. The competency is demonstrated not by what the final document says, but by whether the student can explain, defend and build upon the work they submitted.
This aligns with the Standards rather than straining against them. Under Outcome Standard 1.1, training must be structured to provide sufficient time for instruction, practice, feedback and assessment, and process-based assessment builds in exactly the iterative practice and feedback that develops genuine competence. Under Outcome Standard 1.4, the principles of validity and sufficiency require that evidence adequately demonstrates the student possesses the skills and knowledge described in the training product. A process-based assessment that includes reasoning documentation, draft iterations and AI use disclosure provides richer and more defensible evidence than a polished final document that may or may not have been the student's own work.
|
Practice the Reasoning, Assess the Reasoning |
|
If the final artefact can be generated in seconds, the artefact can no longer carry the weight of the judgement. The defensible move is to assess the thinking: drafts, revisions, a reflection on decisions made, a viva where the student explains and defends the work, and an honest declaration of where AI was used. None of this bans AI. It simply moves the evidence of competence to the place AI cannot occupy, the student's own reasoning. |
3. Discipline-Sensitive AI Policies: One Size Has Never Fitted All
The report's call for differentiated AI policies across disciplines resonates strongly with VET, where the diversity of training products makes a blanket AI policy not just unhelpful but potentially counterproductive. The following table illustrates how the appropriate role of AI shifts across qualifications.
|
Qualification example |
Appropriate role of AI |
Assessment approach that protects competence |
|
Certificate IV in Business |
Legitimate for drafting communications, analysing data and developing plans, mirroring how AI is used in contemporary business practice |
Process evidence and a viva component; AI use disclosed; reasoning explained and defended |
|
Certificate III in Electrotechnology |
Limited; the core competencies are physical skills, safety procedures and practical application |
Workplace observation, practical demonstration and safety compliance, inherently resistant to AI shortcuts |
|
Community services qualification |
Permitted for research and case-study analysis |
Entirely unassisted performance in client-interaction role-plays |
|
Hospitality qualification |
Permitted for menu costing exercises |
Demonstrated practical skill in food preparation and service |
Banning AI from a business assessment would arguably misrepresent the reality of the workplace the student is being prepared for. Equally, in electrotechnology, where competencies involve physical skills and safety in live environments, AI has a far more limited role, and assessment centred on practical demonstration is naturally more resistant to AI-assisted shortcuts. Between these extremes lies a vast range of qualifications where the appropriate role of AI varies by unit, by assessment method and by the specific competency being assessed. The implication is that AI policies need to be developed at the training product level, not the organisational level. A single blanket policy that says AI is permitted, or that AI is prohibited, fails to account for the diversity of competencies, assessment methods and industry expectations across qualifications. What is needed is a framework that lets each qualification's training and assessment strategy specify where AI use is appropriate, where it is restricted and where it is prohibited, with clear rationale linked to the competencies being assessed and the industry the qualification serves.
4. Teaching Students to Evaluate AI, Not Just Use It
Perhaps the most forward-looking finding is students' own recognition that they need to learn not just how to use AI tools but how to critically evaluate what those tools produce. Students told the council that institutional AI training focuses almost entirely on prompting, on how to ask AI the right questions. What is missing is structured guidance on how to assess the quality, accuracy and reliability of what comes back.
This is a critical gap, because AI tools generate outputs that are fluent, confident and often entirely wrong. In VET, where assessment evidence must demonstrate genuine competence against nationally defined standards, the ability to identify AI-generated inaccuracies, biases and fabricated references is not a nice-to-have skill. It is essential to maintaining assessment integrity. The students specifically asked for what the report describes as AI auditing capabilities: the ability to validate AI-generated arguments logically, verify the sources AI claims to cite, and exercise contextual judgement about whether an output is appropriate for a specific purpose. They are asking, in effect, to be taught the critical thinking that makes them effective users of AI rather than passive consumers of its outputs.
For VET providers, this is both a challenge and an opportunity. The challenge is that most trainers and assessors have not themselves been trained in AI output evaluation. Under Outcome Standard 3.1, providers must facilitate access to continuing professional development to enable staff to effectively perform their role. If that role now includes guiding students through AI-integrated learning environments, then AI literacy for trainers and assessors is no longer optional. It is a workforce capability requirement. The opportunity is that providers who build AI evaluation skills into delivery will produce graduates who are genuinely more capable than those who merely know how to generate AI outputs. An accounting student who can use AI to draft a BAS reconciliation and then verify the output against the tax legislation is more competent than one who submits whatever the AI generated. A project management student who can evaluate an AI-produced risk register and identify the gaps is demonstrating higher-order thinking that employers value. Outcome Standard 1.2 requires that training reflects current industry practice, and in 2026 current practice increasingly involves working with AI tools, which means being able to evaluate and verify their outputs, not just generate them.
5. From Fear to Framework: Building Institutional AI Culture
The report's final major theme is the need for institutions to adopt a positive culture toward AI literacy rather than treating AI as a threat to be policed. Students described their AI learning as fragmented and informal, occurring through trial and error rather than structured guidance. They want their institutions to lead.
This is perhaps the most challenging recommendation for VET, because it requires providers to move beyond the reactive posture that has characterised much of the sector's response. The dominant conversation in VET compliance circles over the past two years has centred on detection: how to catch students using AI, how to prevent AI-assisted cheating, and how to maintain integrity where AI can generate competent-looking outputs. These are legitimate concerns, but they are only half the picture. The other half, which the report illuminates through the voices of students themselves, is preparation: how to ensure students can use AI effectively, ethically and critically in the workplaces they are being trained for.
The report specifically recommends that institutions reduce their reliance on AI detection tools, a recommendation that will be uncomfortable for many providers but deserves serious consideration. Detection tools are unreliable, producing both false positives that penalise innocent students and false negatives that miss AI-generated content. Building an integrity strategy on the foundation of detection tools is building on sand. The alternative, which aligns with the process-based assessment approach, is to design assessments that make AI use visible, documented and evaluated rather than hidden and policed. For RTOs under the 2025 Standards, this cultural shift has practical compliance dimensions. Outcome Standard 4.4 requires systematic monitoring and evaluation to support continuous improvement. If student feedback, industry engagement and workforce development data all point toward AI integration as a quality improvement opportunity, then providers have an obligation under their own continuous improvement frameworks to respond. Ignoring AI is not a neutral position. It is a decision to remain static while the environment changes.
|
Detection Is Built on Sand |
|
AI detection tools produce false positives that punish honest students and false negatives that miss the content they are meant to catch. An integrity strategy that rests on them rests on sand. The durable alternative is to design assessment so that AI use is declared, documented and evaluated, rather than hidden and hunted. Make the use visible, and the question stops being "did they use AI?" and becomes "can they demonstrate the competence regardless?" |
6. What VET Providers Should Do Now
The report is a mirror held up to education providers globally, and the reflection should prompt urgent action in VET.
First, establish explicit AI use guidelines at the training product level, communicated to students at enrolment and reinforced at the start of every assessment. These should specify which assessments permit AI use, which restrict it and which prohibit it, with clear rationale tied to the competencies being assessed, and should define acceptable use, such as research assistance, drafting support and data analysis, and unacceptable use, such as submitting AI-generated work as original without disclosure, using AI to bypass required practical demonstrations, or fabricating assessment evidence.
Second, redesign assessments to focus on process rather than output. This does not mean abandoning written assessments or practical projects. It means building in the evidence points that demonstrate genuine learning: draft submissions, reflection journals, reasoning logs, AI use declarations, and viva voce components where students must explain and defend their work.
Third, invest in AI literacy for both students and staff. For students, this means moving beyond basic awareness of AI tools to structured training in output evaluation, source verification and ethical use. For trainers and assessors, it means professional development that builds confidence in facilitating AI-integrated learning and assessing work that may include AI-assisted components.
Fourth, update training and assessment strategies to reflect AI as a dimension of industry currency. For qualifications where AI tools are now part of standard industry practice, the strategy should explicitly address how AI is integrated into delivery and assessment. For qualifications where AI has limited application, it should explain why AI use is restricted in specific assessments.
Fifth, treat AI governance as a leadership responsibility, not a compliance afterthought. Under Outcome Standard 4.1, governing persons must lead a culture of integrity, fairness and transparency. In 2026, that culture must include a considered, documented position on how the organisation approaches AI in training and assessment. The absence of such a position is itself a governance gap.
Conclusion: The Students Are Ahead of the Sector
The most humbling aspect of the report is that the clearest thinking about AI in education is coming from the students themselves. They are not asking for a free pass to use AI without accountability. They are asking for structured guidance, honest engagement and assessments that test whether they have actually learned something. They are asking, in essence, for the educational leadership that providers should already be providing.
The VET sector has a choice. It can continue to treat AI as an integrity threat to be policed through detection tools and blanket prohibitions, an approach the evidence increasingly shows does not work and that students themselves say is counterproductive. Or it can lead: setting clear expectations, redesigning assessments for the AI era, building genuine AI literacy across the workforce, and demonstrating that quality vocational education can embrace technological change without compromising the competencies employers need. The students quoted in the report attend universities on six continents. They speak different languages, study different disciplines and operate in different cultural contexts, but they are saying the same thing: they are already using AI, they want to use it well, and they need their institutions to help them do that responsibly. VET students in Australia are no different. The question is whether their RTOs are ready to respond.
|
Summary: AI, Students and the 2025 Standards |
|
1. Students across six global regions report already using AI, learning it by trial and error, and wanting their institutions to lead rather than react. 2. In the absence of clear rules, students use AI covertly by their own private ethics, which is corrosive to assessment consistency. 3. Vague or absent AI policy sits badly against Outcome Standard 2.1 (clear information) and Outcome Standard 1.4 (fair, context-appropriate assessment). 4. Students want assessment that protects reasoning; the answer is to assess process, not just output. 5. Process-based evidence, drafts, reflections, revisions, AI disclosure and viva, aligns with Outcome Standards 1.1 and 1.4 and is more defensible than a polished artefact. 6. AI's appropriate role differs by discipline, so policies belong at the training product level, not as a single organisational rule, consistent with Outcome Standard 1.2. 7. Students want to be taught to evaluate AI outputs, not just prompts; AI literacy for staff is now a workforce capability under Outcome Standard 3.1. 8. AI detection tools are unreliable; an integrity strategy built on them is built on sand. 9. Designing assessment so AI use is visible, declared and evaluated is more durable than policing it. 10. AI governance is a leadership responsibility under Outcome Standard 4.1, and ignoring AI is a governance gap, not a neutral position, under Outcome Standard 4.4. |
References and Further Reading
Digital Education Council (2026). Student Voices on AI: An Actionable Guide for Institutions and Faculty. https://www.digitaleducationcouncil.com
Digital Education Council (2024). Global AI Student Survey 2024. https://www.digitaleducationcouncil.com
Federal Register of Legislation (2025). National Vocational Education and Training Regulator (Outcome Standards for NVR Registered Training Organisations) Instrument 2025. https://www.legislation.gov.au
Australian Skills Quality Authority (2025). Practice Guides for the 2025 Standards for RTOs. https://www.asqa.gov.au/rtos/2025-standards-rtos
Department of Employment and Workplace Relations (2025). Standards for RTOs 2025. https://www.dewr.gov.au/standards-for-rtos
