Why teaching kids to code is key – even with generative AI

Students learning to code by designing their own game controller device in this undated photo. CodeBrave/Handout via Thomson Reuters Foundation

Students learning to code by designing their own game controller device in this undated photo. CodeBrave/Handout via Thomson Reuters Foundation

Learning to code is essential to build cognitive skills that remain relevant as tech evolves, but can be adjusted to prepare children to leverage AI tools

Clementine Brown is the co-founder of CodeBrave, a non-profit equipping Lebanon's youth with the tech skills to lift themselves out of poverty.

Generative AI exploded into our lives and work in 2023, with a potential compound annual growth rate (CAGR) of 42% over the next ten years and its potential impact extending to virtually all industries. It is transforming the job of computer programmers too. Already, you can build an app without writing a single line of code, and use AI to create fully-fledged video games. This all beckons an uncomfortable question: after a decade of multimillion-pound changes to school curricula, including the US government’s $4 billion investment in 2016, was teaching kids to code a waste of time?

As the head of a coding education non-profit that upskills disadvantaged youth in Lebanon, I have been asked many times this year whether learning to code is still relevant. In my five years in tech education, I have come to understand that learning to code remains essential for nurturing cognitive skills such as problem-solving, critical thinking, logical reasoning, and creativity. These skills will remain relevant as technology evolves. In the face of concerns about AI displacing jobs and marginalising humans, coding education is more important than ever - but should be adjusted to emphasise the human skills needed to effectively leverage AI.

The organisation I co-founded, CodeBrave, seeks to do exactly this by training young people from underserved communities in Lebanon in coding, robotics and AI. Given AI's rapid and exponential impact on efficiency and innovation, I believe that establishing the foundation to harness AI through coding education presents a once-in-a-generation opportunity for youth from disadvantaged backgrounds to leapfrog ahead.

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Claims that kids no longer need to learn to code - like those made by the OECD’s education chief Andreas Schleicher - misunderstand the nature of software engineering. Non-techies may assume that a coder’s job is mainly to code. Even prior to the rise of AI, the job of a software engineer focused less on actually writing code than on using libraries of pre-written code, analysing what the code does and adjusting it for purpose. Increasingly, that pre-written code is now generated by an AI. In both cases, the engineer’s role is mainly about critical thinking and problem-solving.

How do young people build critical thinking and problem-solving skills? Steve Jobs' decade-old insight that “everyone should learn to code because it teaches you how to think” remains as valid as ever. A 2019 study found that one month of computational thinking and coding activities is equal to seven months of standard maths and science for developing executive functioning in children's brains.

When CodeBrave enters new schools in Lebanon, we encounter twelve-year-olds who struggle to understand concepts like conditionals (if this happens, then Event 1 should happen, otherwise, Event 2 should happen). By engaging them in real-world problem-solving in team settings, they learn to think logically and creatively.

One project this year tackled water usage in agriculture. Intentionally limiting resources fosters brilliant ideas: students fashioned an auto irrigation system for plants from straws and a servo-motor to transport water from the tank directly to the plant pot, then used bananas as conductors to link humidity sensors to a microcontroller that analysed soil humidity. These practical exercises cultivate essential non-technical skills such as communication, critical thinking, creativity, and problem-solving. These abilities have been highlighted by experts like TechTarget as necessary for effectively harnessing AI and are crucial for adapting to the continuously evolving work environment.

One teaching method that prepares children to leverage AI, called PRIMM (Predict, Run, Investigate, Modify, Make), involves getting students to review pre-written code, predict its behaviour, run and analyse it, make adjustments, and finally, create something new based on what they've learned. Twelve-year-olds might investigate block-based code for an animated dance party with a ghost, anticipating the ghost's movements based on the code, fixing bugs, and then adding new elements. Such comprehension and critical analysis skills build foundations for working with AI-generated code.

Amid a flurry of sensationalist news headlines and overblown fears of the role of AI in the future, it is important to demystify how it actually works. In one lesson, our 12-year-old students train a computer system to differentiate between images of cats and dogs. They explore how biases can arise from uneven data, leading to skewed outcomes. Imbalances in breed, age, or pose make it harder for the system to accurately classify images that differ from certain patterns. When students understand the mechanics of AI algorithms, they are better equipped to leverage AI tools in future.

Learning to code is still the most efficient way to build the cognitive skills needed to effectively leverage AI tools and take advantage of emerging job opportunities like “AI prompt engineering”, identified by the World Economic Forum as one of top 3 jobs of 2024. Nowhere is that more important than in underserved communities, as the gap widens between those who can harness AI for advancement and those lagging desperately far behind.

Any views expressed in this opinion piece are those of the author and not of Context or the Thomson Reuters Foundation.


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