AI Can Give Students Feedback in Seconds. I’m Still Not Sure That’s the Point

A student can now get feedback on a university assignment in under ten seconds.

Photo by fabio on Unsplash

That alone should make us pause.

Not because it is impossible. It is already happening. Students paste in a paragraph, upload a draft or ask an AI tool what is wrong with their argument. Back comes a polished response, almost instantly.

Sometimes it is useful.

Sometimes it is vague.

Sometimes it only sounds intelligent.

As a university lecturer, I can see why these tools are gaining ground so quickly. Feedback takes time. Careful feedback takes even longer. Most of us are working within crowded timetables, heavy marking loads and the constant expectation to do more with less.

So when AI offers faster responses, round-the-clock access and help with early drafting, it is hard to dismiss.

But I keep coming back to the same thought.

Feedback is not only about speed.

That is where I think the conversation often goes wrong.

The question is usually framed like this: can AI give students feedback?

Of course it can.

The more important question is this: does that feedback actually help students learn?

For me, that is the real issue.

When I think about the best feedback I have given, it was never just a set of corrections. It was not simply “clarify this sentence” or “expand this point”. Good feedback helped students see what they were trying to do, where the work was falling short and what a stronger version might look like.

It gave direction.

It gave perspective.

Sometimes, it gave confidence.

That kind of feedback is not just informational. It is interpretive. It depends on context, disciplinary judgement and a sense of where the student is in their learning.

And that is precisely where I become cautious about AI.

AI feedback tools are often fluent. They are often immediate. They are often convincing.

But convincing is not the same as educational.

A tool can say “your analysis needs to be more critical” or “the structure of your argument could be improved” and sound perfectly academic. Yet comments like these can still be too generic to help. They may look like feedback without really functioning as feedback.

That matters.

Students do not need sentences that merely sound authoritative. They need guidance that is tied to the task, the criteria and the discipline. They need responses that can distinguish between weak writing and unfinished thinking. They need someone, or something, that understands what kind of work they are actually trying to produce.

AI does not truly understand that. It predicts plausible language.

Sometimes that is enough to be helpful. Often it is not.

To be fair, I do think AI can play a useful role. It can help students test an early draft. It can point out awkward phrasing. It can highlight patterns in structure or clarity. For students who are hesitant to ask for help, it can offer a low-stakes starting point.

That is not trivial.

There is real value in having a tool that nudges students to revise before they submit rather than after they have already underperformed. There is value in immediate response, especially when access to human feedback is limited.

But there is also a risk.

The risk is that we begin to confuse faster feedback with better feedback.

The risk is that institutions start treating feedback mainly as a workload problem.

The risk is that students learn to write for the machine rather than for the discipline.

That last point stays with me.

University writing is not just about producing cleaner prose. It is about learning to think with precision, make claims with care and develop a voice within a field of knowledge. That process is often messy. It involves trial, uncertainty and revision. Not everything valuable appears polished in its first form.

A human teacher can see that.

A human teacher can notice the interesting idea buried inside the clumsy paragraph.

A human teacher can say, in effect, “there is something here, keep going”.

I am not sure AI can do that in the ways that matter most.

So I am not against AI feedback tools. I am against the temptation to hand over too much to them too quickly.

Used carefully, they can support learning.

Used carelessly, they can simulate support while thinning out what feedback is meant to do.

For me, the most sensible place for AI is at the edges of the process. Let it help with drafting. Let it help students notice patterns. Let it help reduce some repetitive labour. But do not mistake that for the full work of feedback.

Because feedback is not just a service. It is part of teaching.

And teaching is not just the delivery of comments. It is the cultivation of judgement.

That is why I remain cautious.