Artificial intelligence is a hot topic in many areas and education is no exception. A wave of new kind of AI-based digital methods is arising to improve both teaching and learning, but how does it all work exactly? What’s in it for teachers and students? What is required to gain the benefits? Read on for a non-technological idea on how AI can be applied to learning and education.
AI Is Not Required For Adaptive Learning
All learners are unique, and it’s true that there can be no fixed learning path that works well for every student. You may have heard how AI in learning is all about adapting learning paths to the individual learner’s needs. As it turns out, adaptive learning is nothing new and it does not require AI or other technological solutions. We already have this wonderful glue that just makes everything work by adapting curriculums and learning paths for individual learners: teachers.
Teachers get to
know their students and learn the unique properties of each. Using this insight they guide each student through
their learning paths. Another term for this is differentiation and today it’s based on teachers’
professional
evaluation of students and materials. Differentiation can have a big impact on student engagement and motivation.
Traditional teacher-driven differentiation has its challenges, however. Teachers can’t learn or observe all
aspects of every student, and not all learning materials lend themselves to easy differentiation.
And this is where AI can help.
Machine Learning Is Not Magic
There are many ways to define what artificial intelligence means, but for our purposes it is enough to say that AI
is very good at recognising patterns in data. That’s all there is to it, really. The human brain is excellent at
pattern recognition as well, but unlike humans, computers do not have practical limits on the complexity and
amount of data they can consider.
Another advantage AI has over humans is that they are able to stay objective while we are prone to many types of cognitive biases. Whatever patterns an AI does or doesn’t recognise comes purely from the data it is given. The other side of the coin is that AIs are completely dependent on the data they work on. If an AI does not have enough high quality data, it can’t properly recognise all of the patterns.
Continue reading part 2 where we focus on ways to improve learning and teaching using AI.
