HEC’s new rules on student AI use won't work

The Higher Education Commission has taken two big steps on artificial intelligence. It has notified a compulsory three-credit-hour AI course for every undergraduate and postgraduate degree in the country, starting from Fall 2026. It has also circulated a draft policy on the use of generative AI in our universities. Both steps are welcome.

But the rules the draft sets for students rest on two ideas that will not hold. It bans students from submitting AI-generated work as their own, and it asks them to declare which tools they used and why. These are honest instincts. They are also unenforceable. No teacher can verify either one, and the draft admits as much when it rules, correctly, that no student may be punished on the basis of an AI detection report alone.

The problem is not that these rules are wrong. It is that they aim at something we cannot see. We keep arguing about what a student may do at home, when the question we can actually answer is what we choose to grade. Start with one claim, because everything else follows from it. AI multiplies existing ability. It helps whoever already has some, and it does not create ability where none exists.

The strongest case against universities is not that students will cheat. It is that some of what we teach may no longer need teaching. Ask a modern coding assistant to clean a dataset or fit a forecasting model, and it will do the job in minutes. So why should anyone sit through a semester of statistics or machine learning? Half of that is true. Running such a model used to be a skill. Today it is a sentence typed into a chatbot.

But test the claim. On Kaggle, an online platform where thousands of data scientists compete on the same problem, everyone has the same data and the same AI assistants. If the tool were enough, every score would be roughly equal. It is not, and to my knowledge no live competition against strong human teams has yet been won by handing the problem to a model and walking away. These systems do not think. They execute, very well, in a direction a person has chosen. Someone still has to decide which question is worth asking, whether the data can be trusted, and whether an answer that looks excellent is quietly wrong. That is not clever prompting, which anyone learns in an afternoon. It is knowledge of the subject: knowing what the answer should look like before you see it.

So why teach anything a machine can already do? The usual defence is that machines cannot yet do the difficult part. I........

© Dawn Prism