Teach Secondary Issue 14.7

analysing large datasets. Physicists will regularly use code to create and run complex simulations, analyse data and complete various data visualisation tasks. Climate scientists are modelling Earth’s climate systems using code-driven simulations. Outside of the science lab, the software that powers our world – frommobile apps, to the vast infrastructure of the wider internet – is entirely built from code. The AI tools that are supposedly making coding obsolete are, of course, complex software systems themselves, built by teams of expert human programmers. Continued development, maintenance and improvement of these AI models will furthermore call for a skilled understanding of machine learning algorithms and software engineering – all of which are, once again, rooted in the practice of coding. Code is everywhere in engineering, too – from the embedded software systems in your car, to the control software governing the robotic manufacturing arm that helped build said car in the first place. Code is the ever-present, invisible force that enables our modern machinery to do what it does. Electrical engineers will regularly use code to design and test integrated circuits. Aerospace engineers write the flight control software that prevents our aircraft from falling out of the sky. And let’s not forget how code gives us a way of expressing and experimenting with various mathematical concepts. It can enable mathematicians to explore new theories, create visualisations and solve problems that would be far harder and slower to attempt by hand. Capable collaborators AI is a powerful assistant that can be harnessed and managed. It can assist scientists with writing data parsing scripts more quickly, or suggest the best algorithm that an engineer should use. Even so, those scientists will still need to understand the basics of data analysis in order to create the right kind of prompt. Our engineer will still need to thoroughly understand their system’s physics if they’re to accurately check whether the AI’s suggestions are viable. The question we should be asking is not whether we should stop teaching coding, but howwe should adapt our teaching, across all subjects, to this new reality. The long- term aimmust be to move away from developing coders who rely on memorising syntax, to developing technical directors who can devise complex systems while using AI as a powerful collaborative resource. AI is indeed transforming the nature of coding – automating repetitive tasks, and allowing the human intellect to focus evenmore on creativity, architecture and complex problem solving. The future of STEMwill be shaped by those capable of collaborating effectively with AI, using their own knowledge, experience and computational thinking skills to guide these powerful tools towards new groundbreaking discoveries and innovations. The process of learning to code will no longer revolve around communicating with a computer, but instead centre on understanding the fundamental logic of the world we will build together. ABOUT THE AUTHOR Rob Wraith is head of learning technology and digital learning at NCG – a group of seven colleges across the UK; for more information, visit ncgrp.co.uk COMPUTATIONAL THINKING Coding is a practical application of computational thinking – a term that doesn’t refer to a single skill, but rather a problem- solving framework that involves the following: • Decomposition The breaking down of a complex problem into smaller, more manageable parts • Pattern Recognition Identifying similarities and trends within a problem or across different problems • Abstraction Focusing on essential details while ignoring irrelevant information in order to reduce complexity • Algorithmic Design The development of a step-by-step solution, or set of rules in response to a specific problem 71 teachwire.net/secondary C O D I N G