In Python, a loop is simple. You iterate over a list, repeat a process, and stop when you’re done. It doesn’t adapt. It doesn’t change. It just follows instructions.
But AI loops are different.
In machine learning, each pass through the data changes the model. That’s the point. It learns from mistakes, adjusts its parameters, and tries again. These loops are adaptive—and powerful.
But here’s the catch:
If the training data is biased, uncleaned, or unvalidated, the loop doesn’t just repeat the error—it amplifies it.
One well-known example? A résumé-screening AI trained on historical hiring data learned to downgrade applicants with female-associated names. Why? Because the loop learned from flawed patterns. And no one stopped it.
That’s what makes AI loops dangerous:
They overfit on noise.
They codify bias into logic.
They forget useful old knowledge when over-optimized on the new.
Unlike a Python loop, an AI loop can spiral unless we add guardrails.
And here’s the important part for teachers:
Most of us aren’t training our own models. We’re using AI tools built by others. So our guardrails are different.
We teach students to prompt wisely.
To question outputs.
To ask: What did this AI learn—and should it have?
Because at the end of the day, we’re not just teaching code.
We’re teaching the judgment to break the loop, before it learns too much.
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