Artificial intelligence and machine learning are already impacting on our daily lives.
For example, if you search for an item on Amazon, you are presented with similar products other shoppers have purchased, as its system analyses data (machine learning) to present alternative products to buy (artificial intelligence).
The rise of AI and machine learning, and the scope for processes to become automated, mean that as many as 30 per cent of jobs could be replaced by AI by 2030, according to a report by PricewaterhouseCoopers (PwC).
This could impact on a range of sectors, from financial services (where computers can make faster, more accurate decisions) to driverless vehicles. Meanwhile, industries such as healthcare, and even education, may find AI systems augment, rather than replace, the work of staff.
As such, it has never been more important to teach students about AI and machine learning.
Using AI systems in school
One of the simplest ways to start educating students about the benefits of AI is to show them tools and systems that can enhance their education.
Similarly, systems such as Class Charts analyse a range of achievement and behavioural data to develop ideal seating plans. If your school uses such systems, they offer are great opportunities for students to learn the basics through experience.
Another simple method is to relate to students' own lives and the way YouTube, Netflix or Instagram recommends content based on viewing history.
For younger students, teachers can model this process using the child-friendly version of YouTube and demonstrating how recommendations change according to what videos are watched.
Get students to think like machines
Once students can recognise and understand the benefits of AI and machine learning, the next step is to help them understand how a machine processes information by introducing them to algorithms.
Algorithms instruct computers on how to perform tasks and are an essential building block to enabling machines to collect and analyse the data needed to make decisions. This may sound complicated but simply getting students to understand that algorithms are everywhere, and all tasks are really a series of steps or formulae, can help them to comprehend how programming a computer can work.
For example, you could point out they are already exploring coding principles when they come to school: repetition (hit snooze button three times), sequencing (get bowl from cupboard and then pour cereal in) and conditional logic (if weather is sunny = walk to school).
Learning to code
The foundations of coding are essential for students to be able to eventually build and develop their own machine learning projects. As a starting point, block-based coding applications such as Scratch (and ScratchJr for young children) and Tynker are designed to allow students to use prewritten coding instructions to move characters on the screen.
Using these block-based systems – where the code is simplified into instructions such as move forward, turn left and so on – students can start to apply their algorithmic thinking to developing their own games and activities.
Once students have mastered the basic principles of coding in these block-based applications, they can start to experiment with coding languages. In schools that use Apple hardware such as iPads, the Swift Playgrounds app is a really effective way of developing coding skills that can become quite advanced.
Similarly, on PCs, Python is a computer language that is easy to pick up and can be used to build simple games and activities.
This is not just a nice skill to have either. Getting students to build and run their own code, whether it is using block-based systems or programming languages, is essential to meet the national curriculum objective of analysing “problems in computational terms and having experience of writing computer programs to solve such problems”.
Get machines to think like students
From here, the next logical step is to apply these lessons to near real-world environments. This could be done with physical devices such as drones and robots like the LEGO MINDSTORMS EV3.
These education robots come with a variety of sensors such as colour, touch, ultrasonic and gyroscopes. The sensors can be programmed using a version of the Python coding language to "teach" the robot to learn and respond to external stimuli, providing an excellent foundation for further exploring and creating AI and machine learning experiences.
One such example has been reported by Julie Townsend who has created a class-based activity where a Lego Mindstorm has been programmed to follow a path represented by a black, curved line in a simplified form of machine learning that replicates self-driving cars.
Ultimately, the growth of AI and machine learning will transform the lives of everyone in the future, from self-driving cars to computer algorithms shaping what we see, hear and read.
If we want children to benefit from these innovations, we need them to not only understand what they are and how they work but also have the basic skills to create their own.
AI could change many areas of our lives for the better. With the correct preparation, your students could be the ones to make these developments.
Nic Ford is academic deputy head at Bolton School (Boys’ Division)