I keep seeing the same argument:

Students need to learn the “fundamentals” before they can use AI.

But let’s take a step back.

I didn’t need to understand how to build a car engine to get a driver’s license.
Most college math classes allow calculators.
Professionals use tools every day that abstract complexity.

So why is AI different?

Here’s the uncomfortable question I can’t shake:

When we say we’re protecting foundational learning… are we actually protecting ourselves?

Because every major technological shift has done the same thing:

It reduced the need for certain skills
…and increased the need for others.

Calculators didn’t eliminate math teachers
Spreadsheets didn’t eliminate analysts

But they did change what expertise looked like.

And maybe that’s what’s happening here.

When I look at survey-based research in this space, I think we need to be careful.

A lot of it captures:

  • Perceptions

  • Beliefs

  • Comfort levels

But not always actual performance or outcomes.

This article is a good example of the conversation happening right now:

https://doi.org/10.71741/4pyxmbnjaq.31302475

It raises important questions, but it also made me step back and ask:

Are we measuring learning… or resistance to change?

If a student can:

  • Ask better questions

  • Interpret results

  • Validate outputs

  • Apply knowledge in real-world contexts

Do they really need to master every underlying process first?

Or are we holding onto a model of education that was built for a different era?

Is AI a threat to learning… or a threat to how we define expertise?

Robert Foreman
Doctoral Student, Educational Technology
Central Michigan University

Research Focus: AI-Augmented Learning (AAEL), Adaptive Learning Systems for Neurodivergent Students, and Applied AI in Education

Email: forem1r@cmich.edu
Website: https://nhancedata.com

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