Where is AI in the classroom headed?

Where do you think AI is going in classrooms over the next few years?

In the next five years, AI will be a real game-changer, mostly because it can automate many time-consuming tasks. Imagine grading that doesn’t take hours, assessment data that’s already analyzed so you can quickly spot which students need targeted support, and routine tasks handled well enough that teachers can spend more time teaching and talking with students. When the heavy lifting of paperwork or surface-level feedback becomes faster, the human parts of teaching (conversation, judgment, coaching) get more time and space.

Not all AIs are the same

Should teachers think of “AI” as one thing?

No — and this matters a lot. There are broadly two ways to think about modern AI:

• Generalist (open) models (the large language models you’ve heard about) are trained on massive, open data sources — essentially all corners of the internet. That scale can be powerful, but it also includes misinformation, outdated methods, and content that hasn’t been vetted for pedagogy. As a result, a generalist model can produce plausible but incorrect answers (what people call “hallucinations”). If you upload student data or ask a generalist model to create targeted lessons, it may pull something from a fringe corner of the web that’s not pedagogically sound.
• Specialist or context-aware (closed) models are pointed at a specific set of content — for example, an 8th-grade math corpus, a district curriculum, or a particular assessment bank. Those models can give more targeted, trustworthy analysis or feedback because their scope is constrained to what’s relevant. In short: generalist = breadth, specialist = dependable depth.

The real risks — and how they show up in classrooms

Teachers can run into trouble when they treat every AI output as reliable:

• Hallucinations and low-quality content. A model might produce incorrect steps in a math problem or suggest an instructional approach that doesn’t fit the standard you teach. That creates more work, not less.
• Context mismatch. Large models aren’t inherently trained on your district’s pacing, assessments, or curricular priorities. If the model isn’t aware of that context, its suggestions may be off-target.

Because of these risks, the practical point is this: don’t hand student data to a system unless you understand what the model was trained on and how it’s being used. “Trained” can mean “trained on too much,” including parts of the web that aren’t pedagogically sound.

Practical checklist for evaluating an AI tool

Use this quick list when you’re trying out a tool:

1. Curricular alignment: Can it be configured to your standards, pacing, and assessments? Does its output match what you expect for your grade and unit?
2. Proven pedagogy: Does the vendor or product show how its suggestions were developed or validated by educators?
3. Explainability: Will the tool show how it reached a conclusion or give enough detail for you to judge it?
4. Privacy and data use: Is student data anonymized? Is the company clear about whether your data trains future models?
5. Error behavior: How does the tool handle uncertainty? Does it say “I don’t know” or does it confidently produce questionable answers?
6. Time saved: Does it reliably shorten a task (grading, feedback, triage) without adding new work later?