A new report from Massachusetts Institute of Technology (MIT) on AI use in teaching and learning argues that generative AI is changing education too fundamentally for providers to respond simply through minor adjustments to existing policies and assessments. And although the report was written for MIT, many of its conclusions are still highly relevant to VET providers.
The report finds that AI is already being widely used by students and educators at MIT, offering personalised assistance and greater efficiency while also making it harder to determine what students had learned. It reported signs of reduced student confidence, weaker engagement with teachers and peers, and confusion about when AI use was permitted.
The report adopts a deliberately balanced position. Its eight guiding principles include being both humble and bold, keeping humanity at the centre, teaching intentionally, avoiding a single approach for every course, and favouring “augmentation rather than automation”. That is, AI should help learners explore, practise and undertake more ambitious work, but should not remove the productive struggle through which skills, judgement and confidence develop. For VET educators, this means starting with the competency a learner needs to demonstrate and then deciding whether AI assists or undermines that learning (as per the new ASQA case study), not introducing a tool simply because it is efficient or engaging.
Assessment is one of the report’s strongest areas of relevance to the Australian VET sector. MIT concludes that many conventional take-home assignments can now be completed credibly by AI and are therefore becoming less reliable indicators of individual learning. It recommends oral assessments, portfolios, staged projects, practical and experiential activities, and assignments accompanied by in-person conversations in which students explain their reasoning. For VET providers, this means continuing with assessment practices including direct observation, demonstrations, professional discussions and evidence of how a learner approached a workplace task, particularly where competence depends on practical performance, communication and/or safety.
MIT also warns against relying heavily on AI-detection tools. These systems can miss work in which AI has been used selectively and may also incorrectly identify the writing of students from non-English-speaking or neurodivergent backgrounds as being AI-generated. Excessive surveillance may also create an adversarial relationship between students and educators.
The report recommends that for every course a provider offers they should have “a clear policy about the use of generative AI, posted prominently in the syllabus and on the course website.”
The report goes on to argue that “all instructors (should) make sure that their policies include a clear rationale, tied explicitly to the learning goals of the given course, for why AI must, may, or may not be used.” For example, if an instructor wants to ban the use of generative AI tools, instead of simply declaring that AI use is a form of cheating, it is more effective to explain how AI tools shortcut students’ ability to learn the fundamentals of the course or to practice solving the kind of problems they will encounter in the real world. This approach is more likely to foster self-awareness and a healthy culture around AI use.
Similarly, educators who want to encourage AI use in a particular assignment should make it clear why using AI is important to the educational experience. For assessment tasks that feature AI, providers may wish to include exercises in which students reflect on when and how AI helps or harms their learning, thinking, and morale, and how it changes or expands the work they produce.
Australian VET providers do not have the resources of an internationally renowned institution like MIT – but they can review teaching and assessment strategies, test AI tools before deployment, share effective practices, and identify the implications for privacy, accessibility and equity of AI use.
The report’s central message is especially pertinent: the goal should not be to protect current teaching methods for their own sake or to automate education as quickly as possible, but to use AI in ways that strengthens practical learning, human relationships and learners’ capacity to think and act independently.
