Surprise!!!! Our advanced AI models—don’t actually think!
Apple released a paper two days ago and everyone is talking about it.
Models are fluent. They’re fast. But they don’t reason. According to Apple’s 2025 research, these models excel at pattern recognition and surface-level fluency. They do great on simple tasks, stumble on anything unfamiliar, and collapse when reasoning gets hard. As complexity increases, the models don’t step up, they often step back. Even when given the right algorithm, they fail to apply it.
So, what’s really in a model?
Data: Trained on massive text and code corpora
Prediction: Built to guess what comes next
Understanding: They mimic, but don’t comprehend
This matters in education. Because our students are growing up in a world full of tools that sound smart. And if we’re not careful, they’ll mistake fluency for intelligence.
Here’s how we build real AI literacy in the classroom:
Fluency ≠ Understanding
Just because something sounds right doesn’t mean it is. Teach students to question the how, not just the what.Prompt for Inquiry
Don’t settle for one answer. Vary the prompt. Compare outputs. Reflect on why the responses change.Predict–Observe–Explain
Let students guess what AI will say, check the result, then explain where it worked and where it didn’t. That’s metacognition.Test Generalization
Can the logic from one problem transfer to another? With students, eventually yes. With AI, often not.Foster Critical Thinking
Teach students when not to trust the tool. Teach them to reason better than the machine.
It is kind of like the difference between Bloom’s knowledge level and the analysis, evaluation, and synthesis students are capable of when they’re taught to think.
So yes, AI is powerful. But it doesn’t understand. And that’s good for teachers, because that is our job! So over the summer relax, but when we return in August, make sure our students don’t just use AI and we teach them how to outthink it.
https://ml-site.cdn-apple.com/papers/the-illusion-of-thinking.pdf
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