As we build The School of Entrepeneuring, we’ve realized that the fundamental challenge in education today isn’t about content or delivery.

It’s about the problems we ask students to solve.

The problems aren’t just dull but dangerously irrelevant. The result, as expected, is that we’re boring kids to death helping them hone obsolete skills while the skills of tomorrow go untaught.

The approach is proving catastrophic.

2 out of 3 high school students in the USA are disengaged.

More insidiously, the problems we ask students to solve are increasingly at odds with the requirements of the real world and technological shifts that are fast approaching. (translation: AI)

Yes. The problem is the problems.

We’re teaching students to get really good at tic-tac-toe, then sending them out into the real world to play in a poker tournament.

Types of problems

To understand this misalignment, let’s understand the nature of problems using what I’ll call the Learning Challenge Matrix which has 2 axes:

• x: the method to solve the problem (known or unknown)
• y: the outcome (known or unknown) 

This gives us four types of problems:

1. Routine Problems (known method, known outcome)
2. Prescriptive Problems (known method, unknown outcome)
3. Exploratory Problems (unknown method, known outcome)
4. Complex Problems (unknown method, unknown outcome)

I describe each quadrant and problem-type below and what you’ll observe is that our education system is heavily skewed towards the left side, i.e. the Routine and Prescriptive problems. This is the wrong area to focus on which I’ll expand upon below. 

Routine Problems

Description: These are problems where both the outcome and the method to solve them are known. They are straightforward, repetitive, and often mundane.

Examples:

• Solving standard math equations.
• Conducting routine scientific experiments.
• Completing worksheets with clear instructions and expected results.

Prescriptive Problems

Description: Here, the method to solve the problem is known, but the outcome is unknown. These problems require applying established methods to predict or determine outcomes.

Examples:

• Predicting the results of a science experiment based on hypotheses.
• Using statistical methods to interpret survey data in social studies.

Exploratory Problems

Description: These problems have a known outcome, but the method to achieve it is unknown. They require creativity and innovation to find the best approach.

Examples:

• Developing a unique project or presentation on a given topic.
• Finding innovative solutions for a community issue in a social studies project.
• Designing an art piece or a science fair project with a specific goal.

Complex Problems

Description: Both the outcome and the method to solve these problems are unknown. They are ambiguous, requiring critical thinking and deep understanding.

Examples:

• Addressing climate change and proposing actionable solutions.
• Innovating new technologies or products.
• Managing and solving crisis situations, such as disaster response plans.

The dangers of left side problems

If life came with a user manual, left side problem-solving would be fine. 

You’d flip to the index, find your problem, and follow the steps to solve it. 

Life doesn’t work that way, yet we focus education on the routine and prescriptive problems teaching students to follow a manual that doesn’t exist today (nor has it ever).

The irony is that kids are naturally good at right side problems. Watch a group of children figure out a new game. They’re exploratory problem solvers by nature. Our education system seems designed to beat this out of them.

The Left Side Fallacy

The left side of our problem matrix — routine and prescriptive problems — represents a comforting illusion. It’s the idea that if we learn enough formulas, memorize enough facts, or master enough procedures, we’ll be prepared for whatever life throws at us.

But reality is stubbornly resistant to this neat categorization.

Real World Examples:

1. Business: No business plan survives first contact with customers. Just ask any startup founder.
2. Relationships: There’s no formula for a perfect marriage or raising kids. If there were, divorce lawyers and family therapists would be out of business.
3. Career: The most successful careers often look nothing like what was planned. 
4. Technology: The internet wasn’t invented by following a prescribed set of steps. It emerged from exploration and complex problem-solving.
5. Politics: If governing was just about following a set procedure, we’d have solved most of our problems by now.

It is not surprising that our industrial schooling system likes to teach problems on the left side. If you wanted graduates who were better at memorization than creation and more compliant than capable, this is exactly what you would teach.

The Dangers of Left-Side Thinking

Putting aside the risks of automation for a second, left-side thinking is dangerous for several reasons:

1. Fragility: When the real world deviates from the script — as it inevitably does — left-side thinkers are lost.
2. Lack of Innovation: Following known methods doesn’t lead to breakthroughs. It leads to incremental improvements at best.
3. False Confidence: Mastering left-side problems can give a false sense of preparedness for real-world challenges. 
4. Opportunity Cost: Time spent on routine and prescriptive problems is time not spent developing more valuable skills.

I believe that the focus of education should be on teaching students to think, to critically analyze, to solve problems that they cannot just Google. – Dr Eric Mazur, Harvard professor of physics & father of peer instruction

Automation Magnifies Our Problem with Problems

Routine and Prescriptive problems are at high risk of being automated away, while Exploratory and Complex problems remain largely in the human domain. Even before the current AI boom, the US Department of Labor suggested that 65% of students will end up in jobs that don’t exist today.

Technological acceleration increasingly means we’re teaching kids to solve problems that won’t exist.

Problems at high risk of automation

Routine Problems

These problems are prime candidates for automation. Their repetitive and procedural nature means AI and computers will handle them efficiently. This category includes tasks like basic calculations, routine experiments, and completing standardized tests.

Prescriptive Problems

Prescriptive problems, while slightly more complex, still heavily rely on data and established methods. Predictive analytics, historical analysis, and statistical interpretations are increasingly handled by sophisticated algorithms and AI.

What problems are at a lower risk of automation?

Exploratory Problems

Exploratory problems require human creativity and innovation. While AI can assist by providing data or generating ideas, the unique approaches and creative processes needed are inherently human.

Complex Problems

Complex problems involve navigating unknowns and ambiguities, making them difficult to automate. Addressing issues like climate change or crisis management requires human judgment, critical thinking, and adaptability.

Getting to the right side

Our education system, with its focus on standardized tests and predetermined curricula, is optimized for a world that doesn’t exist. 

The result? We produce graduates who are excellent at following instructions. What we need to do is force students to grapple with ambiguity and create their own paths.

We need to change how we teach (note: peer instruction is a phenomenal replacement for the sleep-inducing lecture)

The right side of our matrix — exploratory and complex problems — isn’t just important because of future automation. It’s important because that’s where real life happens today. 

The schools and institutions that grasp this will produce the leaders, innovators, and problem-solvers of tomorrow. The rest will be producing excellent instruction-followers for a world that increasingly doesn’t need them.