A STUDENT submits an eloquent essay. The argument flows, the language is polished, the conclusion convinces.
Yet, one question remains: What has the student learnt? That question existed long before generative AI entered our collective consciousness. AI simply makes it harder to avoid.
Universities everywhere are reviewing plagiarism rules, setting expectations for disclosure and drafting policies on responsible AI use. This is necessary.
Governance must protect academic integrity and the credibility of our qualifications. But rules about how students may use AI cannot, on their own, answer a more fundamental question: What should students learn and how should we teach them?
My background is in learning design, so I begin with the intended learning, then consider the authentic experiences, support and assessment students need to achieve it.
Technology is a means to that end; its availability does not determine its educational value. In developing learning and teaching policies, I have seen how closely governance must align with learning design.
A responsible AI policy should help academics make sound teaching decisions and help students understand their responsibilities.
Ask better questions, make thinking visible
It is tempting to respond with tighter restrictions and closer monitoring. But a policy centred on catching misuse offers little guidance on designing worthwhile learning.
The first question is whether an assessment measures the learning it claims to assess. If a conventional assignment can be completed with AI and little contribution from the student, we need to re-examine that assignment. A convincing submission is not necessarily evidence of understanding.
Consider an assignment asking students to describe a public health issue. An AI system can generate a plausible answer in seconds.
A redesigned task could ask students to interpret a local scenario, weigh competing interventions and justify a recommendation using verified evidence, then explain how their decision would change if resources were cut.
A pharmacy student could critique an AI-generated explanation, identify unsafe advice and justify corrections against authoritative sources. A business student could test an AI-generated proposal against a company’s actual constraints.
The principle is simple: an assessment should reveal how students arrive at and evaluate an answer.
Yet, a more complex question does not automatically make an assignment secure because AI can produce analysis and reflection with the right prompt. Evidence may need to come from several sources: selected drafts, feedback discussions, live demonstrations and brief explanations of decisions.
Written assignments and examinations remain valuable. Foundational knowledge matters too because students cannot critique an answer if they lack the knowledge to recognise its weaknesses.
Some learning must still be demonstrated independently, without AI.
Design the learning before assessing it
Assessment reform will have limited effect if everyday teaching keeps rewarding passive reception and reproduction.
Students need to practise the capabilities we assess: discussing competing interpretations, working through cases, receiving feedback and revising their thinking before a final submission.
AI can support this. A lecturer may use it to generate a deliberately flawed response for students to dissect. The educational value lies in what students do with the output. Do they verify it? Do they notice missing context? Can they explain why one recommendation is stronger than another?
Inclusion matters too. An oral defence may reveal understanding but it must not become an unnecessary barrier for students with disabilities or communication difficulties. And where AI use is expected, universities should provide access, so that paid subscriptions do not become an advantage.
Support educators, set clear expectations
The lecturer’s role has always extended beyond delivering information. Academics select worthwhile problems, recognise misconceptions and give feedback suited to the learner.
If AI helps prepare materials or feedback, the academic remains responsible for its accuracy. Asking lecturers to redesign learning and assessment requires meaningful support, including practical training, discipline-specific examples and time to collaborate with learning designers and colleagues.
Better teaching can strengthen academic integrity but clear boundaries are still essential. Before beginning an assessment, students should be told in plain language whether AI use is prohibited, permitted within specified limits or explicitly incorporated into the task, with clear explanations of the rules.
Disclosure should be proportionate: brief note identifying the AI tool used, its purpose and how the output was verified is more useful than an exhaustive log that assessors cannot review.
Suspected misconduct must be handled fairly and based on evidence; an AI-detection score alone should never determine guilt.
Integrity also means accountability. A graduate who accepts an inaccurate AI recommendation without verification may make a poor professional decision, even if AI use was honestly declared.
AI is prompting universities to revisit questions that should always have guided us: what knowledge matters, how understanding develops and what evidence demonstrates learning.
The best AI policy is better teaching because its principles must become visible in learning experiences, assessment and academic support. Policy provides direction; teaching gives that direction meaning.
Our goal should be graduates who can use AI responsibly and know when independent thinking is essential. Universities cannot regulate their way through this transformation. They must invest in the people who design and deliver learning.
While AI may help produce an answer, it is the university’s responsibility to help students develop the understanding, judgement and accountability to stand behind it.
Assoc Prof Dr Enna Ayub is the director at Centre of Learning and Teaching, University of Cyberjaya. Comments: letters@thesundaily.com
The views expressed in this article are the author’s own and do not necessarily reflect those of theSun