AI and the Reckoning of HigherEd

LLMs are revealing a lot about students and even more about education as a system. What does this mean for the role of educators?

AI is forcing education to evolve.

And educators are watching it happen in real time.

As students rely more heavily on large language models to produce work, something subtle but important is happening. With AI assistance, many students can submit work that better meets the rubric, sounds more fluent, and appears more “correct”, often in a fraction of the time and with far less effort.

The result is a growing mismatch: the appearance of learning goes up while the quality of learning goes down.

Many instructors have shared this with me. They can often tell when a submission has been generated or heavily assisted by an LLM, but they struggle to prove it using existing assessment practices.

One of the central functions of an educator is to evaluate learning. Traditionally, this happens by proxy: educators review an output that is assumed to represent a learner’s understanding, rather than observing the learner in process.

Modern education has long relied on visibility, examination, and normalization to make learning legible.

So why does so much of the response focus on preventing cheating instead of rethinking how learning is evaluated?

The answer may be uncomfortable.

Evaluating outputs is far more time-efficient than examining students one-by-one during the learning process. As education continues to scale, with larger class sizes and increasing pressure to reduce costs, instructors have less time available per student.

That reality pushes evaluation toward automation rather than personalization.

Which raises a deeper question:

Can AI be used to personalize and automate evaluation of learning processes, not just outputs? And if so, what does that mean for the role of educators themselves?

#AI #llm #highered