One of the amazing things about using Ai is the big learning gains that can be achieved by employing scientific and managed teaching personalised to the student.
When we set out on our mission to build a tool that could help every maths student reach their potential it gaves us the freedom to explore different learning methods and employ science into our approach. It was imperative that we achieved this when we created our Ai tutor because we knew students needed a tool that was easy, accessible and above all didn’t require huge commitment to use. This was especially true of students without exposure to tutoring and perhaps not thriving in the classroom environment.
To make it happen we researched the psychology of learning in great detail and discovered two concepts that have become the backbone of Emmi.ai’s approach.
Chunking is a tool that helps us to get round the bottle neck of short term memory. It’s important because the average person can only manipulate 7 or so pieces of information in their short term memory at any one time. That makes things kind of difficult over a two year maths course. The idea of chunking is based on splitting more complex subjects into small chunks and building knowledge systematically. Chunking is one of the most fundamental ideas for a teacher to learn and Ai is no different. A good teacher helps the students to handle a greater bandwidth of information by chunking that information. Chunking often takes advantage of existing information in our long term memory.
This also brings into play the idea of cognitive loading. Put simply this is more about the pace at which something is learnt and committed to long term memory. By metering the pace to the individual it ensures that overload never occurs. It also means that subjects can be regularly linked and and revisited to ensure the knowledge remains fresh.
The segmenting principle
Much like chunking the segmenting principle was developed by Richard E. Meyer as a part of his learning principles. Richard E. Mayer is an educational psychologist with more than 390 publications, including 23 books. He has developed a set of learning principles. One of those is the Segmenting Principle. That principle states that:
People learn better when a complex continuous lesson is broken into separate segments. Examples include breaking a complex figure into two or more smaller figures dealing with different parts of the original one; presenting one graphic at a time rather than putting multiple graphics in the same figure or breaking a continuous presentation into short chunks that can be paced by the learner. The learner’s working memory is less likely to be overloaded with essential processing when the essential material is presented in bite-size chunks rather than as a whole continuous lesson.
This focus on pace and not overloading is consistent across the two, quite similar concepts. Putting it all together we can summarise that learning will be most effective when
1) It occurs in small chunks that can make it through the bottlenecks of short term memory and cognitive load and those chunks are designed to build upon each other.
2) Those series of chunks build upon each other by calling into use the material learned in earlier chunks, providing both repetition and connection opportunities.
We’ve designed Emmi.ai to teach in exactly this way. Students don’t need to spend hours ineffectively studying. Rather, they can take a systematic approach that has been proven to work. One that requires a smaller time commitment because they know what they study is actually being commited to long term memory and it’s linked to other topics as they progress.
All this adds up to an Ai that is tailor made to teach the individual in the best way possible. Ensuring that big learning gains are made with just a simple 10 minute daily commitment through the duration of a course.
Find out more about reaching your potential and how you learn with Emmi.ai.