What Can AI Really Tell Us About Student Behavior?

Inside Higher Ed reported this week on Austin Community College’s work exploring how artificial intelligence and real-time student data might help staff recognize students who need support earlier and connect them with the right resources.

What caught my attention wasn’t simply the AI.

It was the question underneath it:

What does student behavior actually allow us to know?

I’ve spent more than 25 years working alongside orientation professionals, and I’ve been thinking about that question a lot lately as I work on a forthcoming book chapter about risk, melt, and emerging AI capabilities in orientation.

Because as technology gets better at seeing what students do, I think we need to become more disciplined about what we believe that behavior means.

Behavior is evidence. Behavior is not automatically meaning.

The space between the checkmarks

For a long time, many of the systems surrounding orientation have been very good at recording milestones.

A student registered for orientation.

A form was completed.

A requirement was satisfied.

An orientation was attended.

Complete or incomplete.

Registered or not registered.

Attended or did not attend.

Those checkmarks matter.

But increasingly, technology may make portions of the journey between those checkmarks visible too.

Imagine two students who both ultimately register for orientation.

One moves through the process quickly.

The other starts, stops, returns later, changes something, pauses again, and eventually completes the same registration.

The system may ultimately show the same checkmark for both students.

But they did not take the same journey to get there.

That difference might be worth noticing.

It does not necessarily tell us why it happened.

A signal can give us a question

This is where I think the conversation about AI in higher education gets especially interesting.

AI may increasingly make it possible to examine patterns across amounts of behavioral evidence that no orientation professional could reasonably inspect student by student.

Timing.

Interruptions.

Changes.

Inactivity.

Sequences of activity.

Those observations might give us reasons to become curious.

But recognizing a pattern and understanding what that pattern means are different things.

A student who takes longer to complete orientation registration may be confused.

Or busy.

Or waiting on someone else.

Or reconsidering something.

Or experiencing friction created by the institution itself.

We don’t know simply because we observed the behavior.

That difference may be worth investigating, but the behavior alone cannot tell us why it happened.

Sometimes we should look at the experience, not just the student

There is another possibility that I think deserves more attention.

When we observe unexpected student behavior, our first instinct can be to ask what is happening with the student.

Sometimes we should also ask what is happening with the experience we designed.

Did we give the student too much information at once?

Did we ask for something before they had what they needed to provide it?

Did one institutional process depend on another in a way that wasn’t clear to the student?

Did we create a long period in the orientation journey without meaningful engagement?

Sometimes what looks like student friction may actually be telling us something about the process we designed.

That possibility becomes increasingly important as technology gives us more ways to observe what students do.

Better technology requires better judgment

One thing I appreciated in the Austin Community College story is that the institution isn’t describing AI as a replacement for the people supporting students. The goal is to help those professionals recognize where their attention may be needed sooner.

I think that distinction is important.

Seeing more isn’t the same as understanding more.

And recognizing a pattern, however sophisticated the technology becomes, should not quietly become an explanation for why an individual student behaved the way they did.

The people who understand the institution, the orientation model, and the students still have an essential role in deciding what deserves attention and what should happen next.

In some cases, the right response to a signal may not be an automated intervention.

It may simply be a better question.

That is one of the ideas I’ve been exploring while working on my forthcoming contribution to a NODA-edited book about risk, melt, and the emerging role of AI in orientation.

There is much more to work through.

But I keep returning to one principle:

The technology will keep getting better. Our judgment has to keep up with it.