Do We Still Need Tutors in the Age of AI?

In the 21st century, and particularly in 2026, there is a question increasingly sitting on the lips of parents and students alike:

Do we really need tutoring anymore?

Why pay someone to teach you when you can simply go online?

You can watch a YouTube video explaining quadratic equations. You can search Google for an explanation of photosynthesis. You can ask ChatGPT to explain Newton's laws, generate practice questions, mark your answers, identify your mistakes and even explain the topic again in simpler language. AI can do things that would have seemed extraordinary only a few years ago.

So perhaps the tutor is becoming obsolete.

Perhaps.

But I think we are asking the wrong question. The question is not necessarily "Do we need tutoring?" The deeper question is:

"Can I be guided appropriately so that I don't have to make every mistake myself?"

That is a very different question. Education has never simply been about access to information. It has always been about learning how to use information. And that distinction matters enormously in the age of AI.

Information Has Never Been the Same as Understanding

One of the internet's great developments is that knowledge has become incredibly accessible. A student struggling with simultaneous equations no longer necessarily has to wait until the next lesson to ask a teacher.

They can search.

They can watch.

They can read.

They can ask AI.

This is extraordinary.

But access to information does not automatically produce understanding. Imagine giving someone access to every book in a library. Have they suddenly become educated? Of course not.

The books contain the information, but the learner still needs to know what to look for, how to interpret it, how to connect it to what they already know, how to practise it and how to determine whether they have understood it correctly.

The problem has therefore changed. For centuries, one of the major educational problems was:

"Where can I find the information?"

Today, increasingly, the problem is:

"What information do I need, what do I do with it, and how do I know whether I understand it?"

That is where the teacher, tutor, coach or mentor continues to have enormous value.

AI Is Brilliant — But Brilliant Does Not Mean Complete

There is no point pretending that AI is not transformative.

It is. AI can explain concepts at different levels, generate examples, create quizzes, provide instant feedback, summarise information and help students practise. It can be available at almost any time and can adapt its responses based on what the learner asks. These are remarkable capabilities. But AI still operates within an important limitation:

It responds to the interaction it is given.

If a student knows exactly what they don't understand and can articulate the problem accurately, AI can be incredibly useful. But what happens when the student doesn't even know what they don't understand? That is where things become more complicated.

A student might say:

"I don't understand algebra."

But that could mean several different things. Perhaps they don't understand inverse operations. Perhaps they struggle with negative numbers. Perhaps they understand the procedure but cannot recognise when to use it. Perhaps they can solve an equation when it is presented directly but cannot extract the equation from a word problem. Perhaps the underlying problem began two years earlier with fractions. Perhaps they understand everything intellectually but panic when faced with unfamiliar questions.

The student may simply see "I am bad at maths."

An experienced tutor may see something completely different:

"There is a foundational gap here."

That distinction is important.

The Tutor Diagnoses What the Student Cannot See

A good tutor is not simply an answer machine.

They are a diagnostician.

They observe.

They question.

They listen to how a student thinks.

They look at the working, not merely the final answer.

They notice hesitation.

They notice recurring mistakes.

They recognise when a student has memorised a method without understanding the principle behind it. And sometimes they can identify the problem before the student can articulate it themselves. That is one of the enduring values of human teaching.

A student might arrive saying:

"I just don't get simultaneous equations."

After ten minutes of questioning, the tutor might discover that the student doesn't actually understand substitution. And after another ten minutes, they may discover the problem goes further back: the student isn't confident manipulating algebraic expressions. The tutor is therefore not merely teaching the topic in front of them.

They are tracing the chain of understanding.

That is fundamentally different from simply providing information.

You Cannot Always Prompt What You Do Not Know

This is one of AI's most fascinating paradoxes.

We often hear:

"You just need to learn how to prompt AI."

There is truth in that.

But consider what that means for education. To ask a sophisticated question, you often need enough knowledge to recognise that a sophisticated question exists. A beginner may not know the terminology required to describe their problem. They may not know which assumption is wrong. They may not know which part of their reasoning has broken down. They may not even know that their reasoning has broken down. This is why teachers have historically been so valuable.

A student does not need to arrive knowing the perfect question.

The teacher helps them discover the question.

That is teaching.

Consider Something Outside Education

AI can tell you how a house is built.

It can explain foundations, brickwork, plumbing, electrical systems, insulation and roofing.

It can produce diagrams.

It can explain the construction sequence.

It can even help you troubleshoot a problem.

But that doesn't mean you should assume you can build a house just because you have access to an AI assistant.

There is a difference between knowing about something and being competent at something.

The same applies to repairing a car. AI may explain how an engine works. It may describe how to diagnose a fault. It may walk you through a repair. But knowledge does not automatically produce dexterity, judgement or experience. You still have to encounter the real thing. You still have to develop competence. And you still benefit from someone who has done it before standing beside you and saying:

"No. Not that way. Try this."

Education is no different.

The Real Value of Tutoring Is Not Answers

If tutoring is simply about giving students answers, then AI presents a serious challenge.

Why would a parent pay a tutor £30, £40 or £50 an hour to explain something that an AI can explain almost instantly?

That is a legitimate question. The answer is that good tutoring should never have been about giving answers in the first place.

It should be about developing the person who can eventually produce the answers independently.

The goal is not:

"Let me solve this for you."

The goal is:

"Let me teach you how to solve problems like this yourself."

That changes the entire nature of tutoring.

A good tutor should progressively make themselves less necessary.

The student should move from:

"I need you to help me."

to:

"I know how to approach this."

and eventually:

"I can figure this out myself."

That is success.

From Tutor to Coach

Perhaps this is where tutoring needs to evolve.

The traditional image of a tutor is someone sitting beside a student explaining a topic.

But the tutor of the future may increasingly look more like a coach.

A coach does not merely demonstrate.

A coach observes performance.

They identify weaknesses.

They provide correction.

They establish a training programme.

They set challenges.

They monitor progress.

They encourage discipline.

They adapt the strategy.

And eventually, they want the person they are coaching to perform without them.

This is particularly relevant in education.

A student does not simply need to know the content.

They need to learn how to learn.

They need to know how to approach unfamiliar problems.

They need to recognise patterns.

They need to evaluate their own mistakes.

They need to practise effectively.

They need to know when they understand something and when they merely recognise it.

They need to develop the intellectual confidence to attempt difficult problems without immediately looking for an answer.

These are not merely subject-specific skills.

They are learning skills.

And they become increasingly valuable in a world where information is everywhere.

The Paradox: AI May Actually Increase the Need for Better Teachers

There is an interesting possibility here.

AI may not destroy tutoring.

It may expose bad tutoring.

If a tutor's primary function is to explain a textbook definition or work through routine questions, AI can probably do much of that work.

And perhaps that is not a bad thing.

It forces tutors to ask:

What value am I actually providing?

The answer cannot simply be:

"I know the syllabus."

AI can know the syllabus.

It cannot simply be:

"I can explain this topic."

AI can explain topics.

The tutor's value increasingly has to be in the relationship between knowledge, learner and outcome.

That means diagnosing.

Planning.

Questioning.

Challenging.

Correcting.

Encouraging.

Holding accountable.

Connecting concepts.

Developing independence.

And knowing when to step in — and when to step back.

The Aim Is Competence, Not Dependence

There is another danger in both traditional tutoring and AI.

A student can become dependent.

With a tutor, they might constantly ask:

"Is this right?"

With AI, they might constantly ask:

"What's the answer?"

Both can produce the illusion of learning.

The student completes the question.

The student receives the explanation.

The student feels better.

But feeling that something makes sense is not necessarily the same as being able to reproduce it independently.

True learning eventually has to answer a much harder question:

Can you do it without me?

Can you solve the problem tomorrow?

Can you explain it to someone else?

Can you recognise the concept when the question is presented differently?

Can you apply it to an unfamiliar situation?

Can you identify your own mistake?

Can you build upon the knowledge?

That is the difference between exposure and mastery.

Perhaps the Greatest Educational Skill Is Learning How to Think

This is ultimately why tutoring still matters.

Education should not merely produce students who can answer questions.

It should produce people who can solve problems.

A mathematically competent student should eventually be able to encounter a problem they have never seen before and think:

"I don't know the answer yet, but I know how to begin."

That sentence represents enormous educational progress.

The goal is not to eliminate difficulty.

It is to develop the capacity to work through difficulty.

And that is why tutoring, at its best, is not about making education easier.

It is about making the learner stronger.

The Future Isn't Tutor vs AI

The most useful question may therefore not be:

"Should I use a tutor or AI?"

It may be:

"How should I use both?"

AI can become an extraordinary educational assistant.

A student can use it to generate additional practice.

To ask for another explanation.

To test themselves.

To explore ideas.

To receive immediate feedback.

To revise outside tutoring sessions.

Meanwhile, a tutor can provide the human layer that connects all of this together.

They can say:

"This is what you need to work on."

"This is why you're making this mistake."

"You're ready for something more difficult."

"You're relying too much on the method — explain the principle."

"Don't ask me yet. Try it yourself."

And perhaps most importantly:

"I can see that you understand this now."

That final judgement matters.

Because education is not merely about having a machine that answers questions.

It is about developing a human being who can eventually ask better questions, think more clearly, solve harder problems and help someone else do the same.

So, Do We Still Need Tutoring?

Yes — but perhaps not tutoring as we have traditionally understood it.

The age of AI demands a higher standard.

If tutoring means paying someone simply to repeat information that a student could obtain freely online, then its value is increasingly difficult to justify.

But if tutoring means guidance, diagnosis, deliberate practice, accountability, challenge, feedback, mentorship and the development of independent thinking, then its purpose becomes even clearer.

The world has not run out of information.

It has an abundance of it.

What remains scarce is the ability to navigate it intelligently.

And that is where education has always had its deepest purpose.

The teacher's job was never merely to put information into a student's head.

It was to help the student become capable of using what is in their head.

AI can accelerate access to knowledge.

It can become a powerful learning tool.

But tools still need users who understand how to use them.

And learners still need guidance before they can become guides themselves.

Perhaps, then, the future of tutoring is not about teaching students what to think.

It is about teaching them how to learn, how to think, how to solve and, ultimately, how to teach themselves.

A tutor's ultimate goal should not be to create a student who always needs a tutor.

It should be to create a learner who eventually doesn't.