Technolandy

Day 171 (of 2025/26) #tEChursdAI follow-up to our AI baseline experiment

Last week, I was disappointed that an AI-focused “read/copy/paste” experiment didn’t go particularly well.

(https://technolandy.com/2026/05/29/day-166-of-2025-26-first-little-test-of-ai-learning-this-techursdai/)

Then I was reminded that the “good old days” weren’t exactly overflowing with deep learning either.

Unless it was just me and my cohort, students have always been experts at finding shortcuts. We got distracted. We questioned whether assignments were relevant. We looked for the fastest route to completion. Sometimes we cheated. Sometimes we invested ridiculous amounts of time in projects that mattered to us.

I still remember spending far too many hours on a glacier project and creating a slideshow that I was incredibly proud of. Not PowerPoint. This was the era of HyperCard and 35mm slides. The technology was primitive by today’s standards, but the engagement was real.

At the same time, there were plenty of assignments that I completed simply because they needed to be completed.

This week, I watched a student work through a fairly traditional learning package. Read a section. Answer some comprehension questions. Demonstrate understanding. The student did well.

Then the teacher followed up with a simple assessment check.

“What do you remember from what you just learned?”

The answer?

Very little.

So now I’m wondering if AI is simply exposing something that has always existed.

Whether students are using AI to support a task or simply moving through traditional schoolwork, activities that are more about checking boxes than building meaning may not produce much lasting learning. Completion and learning are not always the same thing.

Which brings me to another question.

Why do we continue to insist on some learning that students experience as irrelevant?

Philosophically, I love British Columbia’s vision of the educated citizen. A broad education. Some humanities. Some sciences. Some arts. Some technical studies. Some mathematics. Exposure to a wide range of ideas and disciplines.

There’s a lot of value in that.

But as we continue to learn more about motivation, engagement, and how people learn, I wonder whether that pathway works equally well for everyone.

We often talk about relevance and meaning as key ingredients for deep learning. Passion matters too.

As a student, I poured far more energy into humanities courses than STEM courses. Ironically, I’m currently loving History of Math 11 because it approaches mathematics through stories, people, and ideas. It feels connected to something larger.

I still appreciate books like Range: Why Generalists Triumph in a Specialized World, and I continue to believe there is tremendous value in breadth. But I find myself increasingly wondering whether some students would benefit from a more personalized journey.

Less “you should know this.”

More “I want to dive deeply into this.”

“Our” next check?

What happens when a student is genuinely connected to the subject they’re learning but also leveraging some significant tutor support … oops, that’s for the socially advantaged… AI. I mean AI 😇