Why Kids Still Need to Learn Coding Skills in the Age of AI

Why Kids Still Need to Learn to Code in the Age of AI

Raspberry Pi, known for making computing accessible and sparking programming enthusiasm as seen in the 1980s and 1990s, has issued a clear warning: relying solely on AI-generated code is not enough for young learners. Their position paper, "Why kids still need to learn to code in the age of AI," stresses that simply feeding prompts into generative AI and accepting the output without critical thought misses the point of coding education.

While tools like GitHub Copilot can generate functional code, Raspberry Pi argues that skilled human programmers remain essential. These programmers think critically, solve problems, and make ethical decisions—skills that come from learning to code directly. The process helps young people develop mental models and fluency necessary to write quality software.

Lessons from the Past

There is a historical parallel: decades ago, some engineers resisted newer programming languages like C, insisting on learning machine code first to truly grasp computing. Today, generative AI can produce code from natural language prompts, tempting learners to skip fundamentals. But Raspberry Pi points out that this convenience carries risks.

AI tools can hallucinate, meaning they sometimes produce plausible but incorrect code. Without the ability to spot errors, learners risk building on shaky foundations. This highlights why understanding the logic and structure behind programming remains critical.

The Value of Friction in Learning

The team emphasizes that the friction involved in converting human reasoning into precise code isn’t a flaw—it’s a vital part of learning computational thinking. Unlike natural language, programming languages require clear, logical expressions, which challenge learners to think carefully and systematically.

Even as AI lowers the barrier to generating code, it takes a skilled programmer to evaluate if that code is safe, efficient, and ethical. This human judgment is crucial in translating real-world problems into computational solutions.

• AI can assist but cannot replace critical thinking and ethical judgment.
• Learning to code builds mental models essential for problem-solving.
• Understanding why code works protects against AI-generated mistakes.

For educators and developers interested in strengthening programming skills alongside AI tools, resources such as the Complete AI Training courses offer practical guidance to balance traditional coding skills with AI-assisted development.