AI tutors can’t teach the most important lesson

There’s a lot of excitement about AI in education. But there’s something missing from this vision.

We are racing to create intelligent tutoring systems that can personalise learning, provide instant feedback, and adapt to each student’s needs. The promise is that AI will make learning more efficient and effective than ever before.

So, we assume that learning is primarily about acquiring and retaining information. That if you just present the right material in the right way, students will automatically understand and apply it.

The problem is, that’s not how deep learning works. Real understanding doesn’t come from passively consuming content, no matter how well-designed. It comes from actively engaging with ideas. From struggling with difficult concepts, trying out different approaches, and learning from mistakes. From discussing insights with others, considering alternative perspectives, and synthesising new knowledge.

This kind of active, constructive learning is hard for AI to replicate. Because it’s not just about the transmission of information, but the transformation of the learner. It’s about developing new ways of thinking, not just new stores of facts.

Think about what happens when you really learn something. Whether it’s a new skill, a complex topic, or a fresh perspective, the process is rarely linear or predictable. You might get stuck on a problem and need to try multiple strategies to work through it. You might have an insight that connects seemingly disparate ideas. You might realise that your initial understanding was flawed and need to revise your model.

These moments of struggle, breakthrough, and correction are where the real learning happens. They’re what help you internalise knowledge, make it your own, and apply it in novel situations. But they’re also inherently messy and unpredictable. They resist easy codification or optimisation.

That’s why the most transformative learning experiences are often intensely human. Like the mentor who challenged you to question your assumptions. The study group that pushed you to clarify your thinking. The project that forced you to grapple with ambiguity and persevere through setbacks. In each case, the learning came not just from the content, but from the interaction. From the friction of different ideas and the sparks of new insight.

This is what’s missing from a lot of the conversation around AI in education. It’s not that AI can’t be useful. Intelligent tutoring systems can be great for practicing skills, receiving timely feedback, and customising the pace of instruction. Used well, they can free up teachers to focus on higher-order tasks.

But AI tutors should be thought of as a supplement to human-driven learning, not a replacement for it. Because the most important lessons aren’t about specific subjects, but about how to think, learn, and grow. How to question assumptions, embrace uncertainty, and create new knowledge. How to learn not just for school, but for life.

Right now (and this may change) I believe that the most valuable skills will be those that are hardest to automate. The ability to frame a problem in a new way. To see connections between seemingly unrelated ideas. To craft a persuasive argument. To inspire and motivate others.

These skills aren’t about absorbing and retaining information. They’re about actively making sense of it. Questioning assumptions. Considering different perspectives. Experimenting with new approaches. Learning from both successes and failures.

Building these skills requires a learning environment that encourages exploration and experimentation. Where the goal isn’t just to find the right answer, but to understand how to approach a problem from multiple angles. Where learners aren’t just consuming content, but creating it, critiquing it, and building on it.

AI can support this kind of learning, but it can’t drive it. It can provide personalised feedback and adaptive challenges. But it can’t replace the spark of human curiosity, the friction of competing ideas, or the satisfaction of a hard-won insight.

That’s why, as we integrate AI into education, we need to be careful not to reduce learning to a series of predictable, optimisable tasks. We need to preserve the spaces where messy, generative, collaborative learning happens. The group projects. The open-ended discussions. The hands-on experiments.

In these spaces, learners don’t just acquire knowledge but make it their own. They learn to articulate their thoughts, consider other viewpoints, and revise their understanding. They develop the resilience to tackle complex, ambiguous problems. The creativity to imagine novel solutions. The empathy to collaborate across differences.

Using AI to support this process doesn’t mean automating it. It means leveraging AI to free learners and teachers to focus on the most meaningful, generative aspects of learning. To spend more time discussing ideas, providing feedback, and mentoring growth. Less time grading rote assignments or delivering standardised content.

If you’re working on ed-tech, stop trying to make learning “efficient.” Start figuring out how to create more opportunities for students to struggle productively, to collaborate meaningfully and to think critically. Use AI to handle the routine so humans can focus on the extraordinary.

Because education isn’t about acquiring information. It’s about becoming a different person. And that transformation doesn’t happen through algorithms — it happens through challenge, through inspiration, through human connection.

The future of education isn’t AI. It’s humans, empowered by AI, teaching other humans how to be more fully human.