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(We should fill you in on the first three posts. To explain—no, there is too much—to sum up, Part 1 was an overview of adaptive learning, Part 2 was about personalized learning, and Part 3 zeroed in on why AL and simulation training dovetail so well.)
AI + AL = The Future of Learning
Provided the world doesn’t end in a zombie apocalypse, it seems clear that adaptive learning is the future of education. But not just adaptive learning: adaptive learning powered by artificial intelligence (AI).
Adaptive Learning = Efficient Learning
In almost every aspect of modern life, we are constantly striving for increased efficiency. Adaptive learning is more efficient than traditional one-size-fits-all classroom methods because it aims to personalize the learning path for each student, according to their unique strengths and needs. Adaptive learning methods can be used with human instructors, or with automated e-learning programs. And it’s with automated AL that we find the greatest potential for gaining efficiency.
Computer-based AL can, for instance, deliver extra content on topics that are giving a particular student trouble. Or, it can figure out that another student already knows the material, and skip them onto the next lesson. Right out of the gate you gain efficiencies 1) from letting the students who have mastered content proceed “unshackled” and thereby shortening their training time, and 2) by not sending other students on to a new module until they have mastered the content in the preceding one, thereby ensuring they’re not wasting their time attempting to build on concepts they don’t yet understand.
But most importantly, because all of this is done via software and is not limited by the number of human instructors or the budgets to pay them, it can yield tremendous efficiencies through scalability.
Obviously, at this point in time, a one-on-one human instructor-student relationship is still more intuitive and nuanced than even the best AL programs can claim to be. Human teachers and tutors have a wealth of experience and knowledge. They have deep knowledge of the subject matter and have the advantage over computers by, say, being able to interpret body language that demonstrates boredom or frustration. They can instantly see why a learner is having trouble with a concept, and what the misconception is.
But, while this ideal of individual tutoring and apprenticing is available to some, it is extremely expensive. The future of learning, not just for schoolchildren but in other applications such as employee training, is computer-based adaptive learning driven by artificial intelligence that can more closely approximate the guidance and instinct of a human instructor, but distribute it to vast numbers of learners.
Enter Artificial Intelligence
Earlier we gave the example of AL software sequencing content to suit learner’s abilities. But there’s much more to it than that. For instance, education research has shown that emotional states such as engagement, frustration, impatience, or discouragement can radically impact the quality of learning. So, in order to optimize student success (which is efficient), the AL software has to be attuned as much as possible to the individual learner’s needs so they remain engaged and not frustrated or discouraged. This can mean custom-tailoring not only the sequence of the course content for optimal engagement, but even adapting assessment in real time in order to avoid discouraging learners. There are a lot of moving parts.
And this is where AI and machine learning come in. As the field of computer-based AL evolves, it will depend more and more on complex AI algorithms fed by big data. The more learners that go through the course, the more data that is generated for the algorithms, and the more the system “learns” how to better personalize content for optimal individualized learning, and the more the content maps are refined. It all results in a continuous improvement cycle, whereby the AI-powered AL becomes increasingly more human. (Not as in becoming sentient and taking over the world, just becoming more natural.)
China’s Futuristic Learning System
As it happens, the learning system of the future may already exist.
In China, where academics are prized, competition is stiff, and parents will make huge sacrifices so their children can get ahead, there is a market for individualized learning tools. One such tool is the Yixue Squirrel AI Learning, an AI-powered adaptive learning system that tutors children in a variety of subjects in the Chinese curriculum. Over two million students are signed on. They learn either online from home, or attend one of over a thousand Squirrel AI learning centers across China, which children in remote and rural areas to access the same quality of education as urban children.
Squirrel AI uses a three-pronged strategy of diagnosis, content, and assessment. It begins with an assessment to discover a student’s weaknesses and strengths, and then only teaches the material that the student doesn’t know. The diagnosis is extremely granular, resulting in highly individualized courses. It If a student makes mistakes, it tracks them back to where they originally went wrong, works on that material with them, then sends them back to where they left off. Because it is only covering material that the learner doesn’t know, it Yixue claims that it actually reduces student screen time and gives them more leisure time. Students work at their own pace, and Squirrel is constantly collecting data to improve the content, delivery, and assessment.
Learning of the future will be much like this, say experts. As with the Squirrel AI system, human teachers will continue to be required for content prep. Some experts argue that in the future, AL will be “open-ended and assessment-based” because of the difficulty of developing “very deep” AL content for certain subjects.
Well, that’s it for today, Troubleshooters! Tune in again next week as we continue exploring the future of learning. We’re going to be talking about how the learning system of the future will seek to discover and cater to student’s individual learning styles and preferred learning resources, and use those to deliver relevant content. In the next few weeks, we’ll also be looking at another feature that’s certain to have a role in the future of learning—gamification.
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