VIEWS & OPINION
Incorporating machine learning-based project work into the curriculum
Comment by CAITLIN BROWN, Education Manager at the British Science Association
When we consider what knowledge and skills children should be learning now to best prepare them for the future, it is impossible to ignore the ever-growing impact of technology on all aspects of our day-to-day life. Artificial intelligence (AI) and
machine learning have been transforming the way we live and work at a rapid pace, creating new jobs and skills demands across all sectors. While AI refers to the range of technologies in which computers are programmed to exhibit complex behaviour, machine learning is a subset of AI that involves machines learning from examples in the form of data. The ability of future workers to be able to adapt to new technologies and digital ‘upskilling’ will be important to stay competitive in a crowded jobs market. Currently, around 23 per cent of the UK population lack basic digital skills. The Government’s recently launched National AI strategy further outlines the importance of having accessible AI programmes in schools, to equip children with the necessary skills and inspire a broader range of people from all backgrounds to pursue AI-related careers. Preparing children with AI and machine learning knowledge helps them to better understand how and why these technologies work, and how they benefit society. This improves their engagement with, and curiosity about, the vast potential of advanced technology.
Project-based learning on AI and machine learning Machine learning is an area of AI that is ever-growing in importance, so developing student knowledge from a young age is a key way to ensure they are prepared for future and able to access a more extensive range of future career paths. While machine learning can initially seem like a complex subject to introduce to children, teaching students via hands-on, project-based work is a brilliant way to help them explore the real-world impact of AI. Project-based learning (PBL) encourages students to take ownership of their work as they develop, carry out and evaluate the findings from a practical investigation. The creative and hands-on element makes a particularly engaging science lesson, and the flexibility of how to run PBL makes it highly accessible for students of different learning abilities. The topic of machine learning is an exciting way to get students
thinking about the future and the potential social and economic impact of new technologies. Teachers may want to consider free PBL resources, such as the CREST machine learning activities which have been created with The Royal Society, to provide easy, accessible projects which can be built into lessons at both primary and secondary level.
Tailoring to students’ interests: ethics in AI From voice-powered smart assistants to self-driving cars and automated crop harvesting, AI is already being used to create more efficient, accurate and cost-effective ways of carrying out complex tasks. As a first step, you may want to ask students to list the different AI applications they currently know of that use machine learning, and to imagine what the world could look like in five, 10, and 20 years’ time, if these technologies continue to improve. What
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challenges and opportunities could this present? Next, give students the chance to explore a range of different machine learning scenarios that spark their interest. By using PBL resources which contextualise machine learning, you
can give students the freedom to investigate a challenge that genuinely excites them. For example, some students might be particularly interested in the ethics of machine learning. To start, you could task them with investigating the level of trust people have in machine-based decision-making, which decisions they would be comfortable with a machine making, and why. This project asks students to develop a survey to assess a specific
demographic’s views on whether machines are trustworthy. They will then need to assess the findings and design a method to present their recommendations to developers who create AI-powered computer systems. This will help students to critically engage with the advantages and disadvantages of machine learning and consider how public perception may impact how we use AI tools in the future. At the same time as honing their understanding of how machine learning works, students will develop valuable life skills including analysis, team working and communication.
Machine learning in healthcare Children are naturally inquisitive, and providing them with opportunities to research independently is a great way to foster motivation and engagement. You may have some students in the class who are more interested in the applications of machine learning in healthcare. For this group, ask students to explore the role of digital technologies in healthcare delivery, such as smart devices and data analysis, which can help to both manage illnesses and better understand and develop solutions for diseases. Start by asking students to imagine that they work in a busy GP surgery and have been asked to investigate a range of healthcare tools which might help to reduce the workload of healthcare staff. For this project, students should evaluate the potential uses,
benefits and limitations of each tool, whether that is an online app, wearable device or telemedicine. This is fantastic method for getting students to think not only about the way the technology will work, but how it may be received by staff and patients. It also raises key questions around the trustworthiness of healthcare tools that use machine learning, their long-term use, and the importance of digital training. Encourage students to work together and discuss the pros and cons – the idea is that there is no right or wrong answer. Science is all about learning through trial and error, and supporting students to test hypotheses and present findings will help them to develop resilience and a sense of great pride and joy in their work. With PBL activities such as CREST, students can also work towards earning a Discovery, Bronze, Silver or Gold Award depending on the level and number of projects they undertake. There are also awards for primary pupils. An award is another effective way to motivate and incentivise students with their science learning. Machine learning will undoubtedly have an even greater presence in the lives of future generations. Teaching students about AI and machine learning in schools crucially exposes them to the different real-life applications and capabilities of advanced technologies. This could pave the way for the next generation of great scientists and mathematicians who will go on to create innovative solutions for a brighter tomorrow.
February 2022
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