Does the curriculum prepare young people for an AI future?
It’s been ten years since the current National Curriculum was launched… and what a ten years it’s been! The letters GPT meant little back then, and it wasn’t until 2018 that OpenAI paved the way for the current explosion of generative artificial intelligence. Dall-E brought generated art to the masses in 2021, and video, audio, and text are now routinely produced using Artificial Intelligence (AI), raising concerns about misinformation, Deep Faked celebrity exposes and political manipulation.
How can an aging curriculum possibly remain relevant and up to date?
In 2014 there was a recognition that the previous ICT curriculum faced an uphill task; how to maintain relevance and skillsets in a rapidly evolving technology ecosystem, without constant infrastructure refreshes and teacher reskilling. Instead, there was a shift towards enduring concepts in computing such as algorithms, data structures, communication across networked systems and the building blocks of programming.
In doing so, the curriculum became unbound from technology. Opinion was split between those loyal to a skills-based ‘user’ approach to ICT learning, and those embracing the ‘builder’ capabilities of computer science, as to whether this represented progress – a debate that continues unabated!
Preparing for an AI future
The questions that arise the most around this subject include: could ICT have kept up with the developments in AI over the past ten years? Does computing, alongside other STEM subjects, present a more durable approach to understanding AI?
At the recent STEM Learning AI Roundtable event, thought leaders from across industry and education shared their views.
- What knowledge and skills does every young person need to navigate an AI future?
- How do the needs of AI ‘users’, ‘implementers’ and ‘builders’ differ, and how does school prepare young people for these differing roles?
- What are the foundational capabilities, comprising both knowledge and skills, that relate to AI?
It could be argued that the answers to these questions can be found in curriculum subjects with a deep history, such as maths, computing, engineering, and science, if we’re prepared to update the context in which these subjects are learned.
Data literacy is a combination of knowledge and skills developed across the curriculum, underpinned by mathematics. For instance, to understand how machine learning algorithms can make predictions based on data, it is first necessary to understand how data is handled.
Foundation knowledge includes simple statistical techniques such as average-finding and understanding why gradients and intercepts matter in lines of best fit. The underlying knowledge and skills can be traced right back to early primary maths and science. Young people need help to see these, and other, connections and teachers need CPD and curriculum resources that join the dots.
Harnessing the current curriculum
Computing covers the AI ‘builder’ fundamentals of programming and data manipulation, as well as the ‘user’ knowledge of privacy and security, ethics, and an understanding of the impact of technology on society. The curriculum is open enough to embrace generative AI, alongside other information technology tools, for creating a range of media and data products, equipping young people with the evaluative skills they need to remain safe and responsible users and makers of technology.
You’ll struggle to find the term ‘Artificial Intelligence’ spelled out anywhere in the current school curriculum, and an update may be due. Read between the lines of the curriculum, however, and you’ll find conceptual AI knowledge, and opportunities to practically apply it everywhere, supporting the critical thinking and skills to help young people thrive in this new era.
We don’t need to wait for a new curriculum when we can bend the current one to our will.
About the author
Dave Gibbs is the Education Strategy Lead at the National Centre for Computing Education.