Why Coding Should Be Mandatory in Schools: Combining Actual Intelligence with Artificial Intelligence for a Better Future

In today's world, coding isn't just a skill for software engineers—it's quickly becoming an essential literacy for navigating a tech-driven society. But right now, we're treating it as a niche skill, separate from the broader education system, when it should be foundational. Coding should be mandatory in schools, but not as a standalone subject. Instead, it should be taught as an interdisciplinary tool, integrated with other subjects to prepare students for a world where both actual intelligence and artificial intelligence will drive innovation.

Coding as a Tool for Real-World Problem-Solving

The biggest misconception about coding education is that it's just for people who want careers in tech. But coding is a skill that helps people in all fields solve problems. A personal story comes to mind: when my son was eleven, I taught him to code as a way to help him with his math homework. He wasn't excited about coding for its own sake, but he was definitely motivated to get through his homework faster. By creating simple programs to automate math formulas, he not only learned the math but also discovered how coding could make his life easier.

The results were far-reaching. My son ended up mastering the math, but he also developed confidence and practical skills in coding—skills he's now using professionally. Today, he's working in AI at GitLab, a publicly traded company. Teaching him to code by connecting it to a real-world task had a lasting impact on his life. This is the kind of coding education we need in schools: not isolated lessons on syntax and theory, but hands-on projects that let students apply coding to solve real problems and achieve outcomes that matter to them.

The "AI + AI" Concept: Actual Intelligence Meets Artificial Intelligence

The real value of artificial intelligence comes when it's paired with actual intelligence—deep knowledge in a specific subject area that gives context to data and algorithms. This is what I call the "AI + AI" approach: combining actual intelligence with artificial intelligence. The people who will thrive in an AI-driven future aren't just those who can write code, but those who have expertise in a field and can leverage coding and AI to make meaningful improvements in that domain.

In the industry, I've seen a problematic trend: companies treat coding like a plug-and-play skill, moving engineers from one project to another without considering whether they have the necessary domain knowledge. For instance, I've seen engineers with no background in cybersecurity tasked with building cybersecurity products. They have coding skills, but without a deeper understanding of the field, they lack the intuition and insight to solve the unique challenges of cybersecurity effectively.

It's the combination of coding skills and domain expertise that allows engineers to be truly effective. Coders who have spent time in a particular field develop intuition—an almost sixth sense—that guides them to make better decisions and approach problems in innovative ways. This intuition only comes from real, hands-on experience, or what I like to call actual intelligence. To truly prepare students for the future, we need to emphasize both coding skills and the development of subject matter expertise.

Why We Need an Interdisciplinary Approach to Coding Education

The way we currently teach coding, as a standalone subject, is disconnected from other fields of study. This approach limits students' engagement and creativity, making coding feel like just another academic requirement. What we need instead is an interdisciplinary approach, where coding is integrated with other subjects—like science, history, or social studies—so students can see its relevance and apply it to areas they're passionate about.

A great example of this interdisciplinary approach comes from a project on Kaggle, a popular data science platform. One of the first projects that many aspiring data scientists work on is analyzing the Titanic passenger manifest. Using real historical data, students predict passenger survivability based on factors like gender, age, and ticket class. Through this data science lesson, students aren't just learning AI techniques; they're also learning about the history of the Titanic, its social structure, and even the harsh realities of class disparity.

I recently visited the Titanic Museum in Belfast, and I was struck by how much I already knew from working with that dataset. The museum explained the differences in survival rates between first-class passengers and those in lower classes, details I'd already explored through data analysis. This type of project shows the potential of combining coding, AI, and history. When students work on projects like these, they're not just learning to code—they're building a richer understanding of the world, connecting technical skills to real-world stories and societal issues.

Building Better Products Through Domain Expertise and Intuition

The tech industry's habit of treating engineers and data scientists as interchangeable parts is problematic. Companies often move people from project to project without giving them time to develop any real domain expertise. But the people who excel at creating impactful products aren't just good coders; they're subject matter experts with a deep understanding of the field they're working in.

Intuition is key here, especially in fields like cybersecurity or data science. True intuition only develops when you're immersed in a field long enough to see patterns and anticipate challenges. In cybersecurity, for instance, engineers with deep domain expertise can identify risks and threats that others might miss because they've developed an instinct for the work. Similarly, in AI and data science, experts who understand their field can make better decisions about data, models, and applications because they have a feel for what matters most.

If we want to create a workforce that can build truly innovative and effective products, we need to rethink coding education. It shouldn't just be about learning to code; it should be about understanding how to apply code within a specific context, building the actual intelligence that makes technical skills valuable.

Conclusion: Rethinking Coding Education for a Tech-Driven World

The future of coding education isn't about producing more programmers. It's about empowering people across all fields to use coding as a tool for problem-solving, creativity, and exploration. Imagine a curriculum where students don't just learn syntax, but work on projects that connect coding to their interests—whether it's history, science, or social issues. This interdisciplinary approach could help students build both technical skills and a deeper understanding of the world.

By making coding mandatory in schools and teaching it as an interdisciplinary skill, we can prepare the next generation to navigate a world where technology touches everything we do. Instead of treating engineers and data scientists as interchangeable parts, we should aim to create professionals who bring both coding skills and subject matter expertise to their work. These are the people who will drive meaningful innovation in the age of AI.

It's time to rethink coding education—not as an end in itself, but as a bridge to meaningful problem-solving, a tool for exploring complex issues, and a way to build a more interconnected understanding of the world.