Teaching Students to Learn
Preparing students for a future we cannot predict
We have several grandchildren and family friends at that familiar crossroads: trying to decide what to study and, ultimately, what profession to pursue.
It is a harder question than it once was.
How do you choose a course of study today when the profession you are preparing for may look quite different by the time you qualify—and may change several more times during your working life?
For generations, education has been built around a fairly simple proposition: teach students what they need to know for the work they are likely to do.
That worked reasonably well when knowledge changed slowly and many of the skills learned at school or university remained useful for decades.
That world is changing.
Today’s students will probably work in jobs that change substantially during their careers, use technologies that have not yet been invented and confront problems for which there is no textbook answer.
Perhaps, then, we are asking the wrong first question.
Perhaps the better question isn’t just “What should they study?” but “Are they learning how to learn?”
Because perhaps the most important thing education can teach today is not simply what to learn, but how to learn.
Knowledge still matters
Learning how to learn doesn’t mean abandoning traditional subjects.
Reading, writing, mathematics, science, history and cultural literacy provide the intellectual scaffolding on which further learning depends.
You need knowledge to recognise patterns, make connections and judge whether new information makes sense.
But acquiring knowledge can no longer be the destination.
It is the foundation for what comes next.
Curiosity is the engine
A good learner begins with questions.
Why does this happen? What am I missing? Is there another explanation? How could I find out?
Schools are very good at teaching students to answer questions. Perhaps they need to spend more time teaching them to how to ask good ones.
Curiosity changes learning from something delivered by a teacher into something increasingly driven by the student.
Purpose helps sustain it. When students can connect what they are learning with something they care about, they have a reason to keep going when learning becomes difficult.
Learning should sometimes be difficult
That difficulty matters.
Learning requires some intellectual struggle: attempting something we don’t yet understand, getting it wrong, discovering why and trying again.
The objective shouldn’t be to remove that struggle. It should be to make it productive.
A student who can say, “I don’t know how to do this yet, but I know how to start finding out” has acquired something more durable than any single piece of knowledge.
Teach the learning loop
Learning how to learn is itself a skill.
The process is simple: Attempt. Feedback. Reflect. Adjust. Try again.
There is nothing new about this. It is how we have long learned complex skills in music, sport and the trades.
You don’t learn the piano simply by reading about it. You play, make mistakes, receive feedback, practise the difficult passages and try again. Tennis is much the same: hit the ball, observe what happened, get feedback, adjust and hit another.
An apprenticeship works on the same principle. An apprentice learns from an experienced tradesperson, attempts the task, sees what works and what doesn’t, receives immediate feedback and tries again. Competence develops through repeated cycles of instruction, practice and correction.
Failure isn’t an interruption to learning. It is part of learning.
Each mistake provides information about the gap between what you intended to do and what actually happened. The teacher, coach or tradesperson helps the learner understand that gap and decide what to change next.
Academic education could borrow more from this model—not simply marking mistakes down, but teaching students how to learn from them.
Attempt. Feedback. Reflect. Adjust. Try again.
AI changes the equation
AI makes all of this both easier and harder.
A student can now ask AI to explain calculus, critique an essay, write computer code, analyse data or argue both sides of a proposition.
That is an extraordinary educational resource.
But there is a catch.
If AI explains the problem, summarises the reading and produces the answer before the student has wrestled with it, we may remove precisely the intellectual effort through which learning occurs.
So AI literacy isn’t simply knowing how to use AI.
It is knowing when to use it.
Sometimes AI should accelerate learning. Sometimes it should challenge reasoning, provide feedback or suggest another approach. And sometimes the better educational decision may be to put it aside and struggle with the problem first.
The objective isn’t to outsource thinking.
It is to use AI to become a better thinker.
Judgment becomes more valuable
When producing an answer becomes easy, evaluating the answer becomes valuable.
Is the evidence reliable? What assumptions are being made? What is missing? Could there be another explanation? Is this confident AI answer actually wrong?
Students therefore need to learn to question information rather than simply consume it.
In a world of abundant information and increasingly capable AI, judgment becomes a core educational skill.
The teacher becomes a learning coach
None of this makes the teacher less important.
It may make a good teacher more important.
The role expands beyond transmitting knowledge to coaching the process of learning.
What do you already know that might help? Where could you look? Why didn’t that approach work? What evidence would change your mind? What should you try next?
Those questions teach something that extends beyond any particular subject.
They teach the student how to proceed when the teacher is no longer there.
Back to the question
So when our grandchildren and young friends ask what they should study, perhaps the answer isn’t simply to identify the profession with the best prospects today.
Interests matter. Aptitude matters. Purpose matters. And professional knowledge still matters enormously.
But there is another question worth asking: Will this education teach you how to keep learning?
We cannot know precisely what today’s students will need to know in 2040, let alone over a working life that may extend decades beyond that.
We can help them develop something more durable: the confidence and capability to learn whatever comes next.
Give them strong foundations. Encourage curiosity. Let them struggle productively. Teach them to seek feedback, learn from mistakes and use AI without surrendering their judgment.
Above all, teach them how to learn.
Because in a world where knowledge, technology and professions will keep changing, the most valuable student may not be the one who knows the most, but the one who knows how to learn what comes next.