How Will AI Change Schools in the Future?

Schools today vs. schools tomorrow

Today's schools were built on a "30 students per class, same content, same pace" model of group instruction. Textbooks and timetables are identical for everyone. With one teacher for 30 students, adapting to each individual student is practically very difficult.

When AI is introduced, this assumption changes. Different students at different levels, speeds, and interests can receive different materials. Instead of teaching everyone the same thing, teachers can spend more time on individual challenges. This is still in the experimental phase — tests are running worldwide, but no finished picture exists yet.

8 school situations that AI will change

These 8 situations are either already underway somewhere in the world, or will realistically be common in schools soon.

Real-world examples already underway

Khanmigo (US)

The free learning platform Khan Academy built an AI tutor using OpenAI's GPT. Instead of giving direct answers to student questions, it uses a "Socratic method" — drawing out thinking through follow-up questions. Pilot tests are running in thousands of US schools.

Squirrel AI (China)

A China-based service that uses AI to analyze each student's level of understanding, then delivers materials focused specifically on their weak spots. Already used by millions of students, it's internationally recognized as a leading example of personalized learning.

Japan — MEXT Guidelines

Japan's Ministry of Education issued provisional guidelines for generative AI use in K-12 education in July 2023. A revised version moved away from "blanket bans" toward "promoting appropriate use." Local governments are pairing the GIGA School initiative's 1-device-per-student rollout with AI tools in various pilot programs.

Remaining challenges

At the same time, AI use in education comes with a number of unresolved challenges:

• The risk of "hallucination" — AI confidently teaching students the wrong answer
• Gaps in device access and internet connectivity at home (the digital divide)
• Students copying AI output directly into reports and assignments
• Protecting privacy of student learning data
• Teacher training and guidance systems for AI use

One of the most debated questions worldwide is how to fairly handle whether or not students use AI in their work.

Another important question is what happens to learning data. Which questions a student got wrong, how long they spent, what they asked — this is useful for learning support but also personal information. You should ask: who can see this data, and when can it be deleted? Don't just focus on convenience.

When using AI at school, following your teacher's rules also matters. Using AI to understand a practice problem is very different from having AI write your entire submitted essay for you — the latter can count as academic dishonesty. The important thing going forward is being able to explain clearly "what I used AI for."

Pitfalls to watch out for

Why does this matter for your future?

The generation entering university in the 2030s will be "the first generation who studied alongside AI." How you've used AI during your schooling is increasingly becoming a point of evaluation in university admissions and job applications — as evidence of your learning style. Rather than relying passively on AI, people who can articulate how they've integrated it into their own learning will thrive.

AI entering the classroom doesn't make memorization or thinking obsolete. If anything, the ability to hear an AI explanation and restate it in your own words, catch errors, ask questions to teachers and classmates — these become more visible and valuable. Using AI as "a tool to support your thinking process" rather than "a machine that produces answers" makes for deeper learning.

What you can do starting today

Summary