Learning is a multifaceted, multidimensional and dynamic experience made of intricate layers that include reading, writing, listening, watching, thinking, testing and more. These layers weave together to make learning an experience that is personal and relative to every person. There is power in understanding the elements that shape the way we learn. That knowledge, when partnered with artificial intelligence (AI), can enable us to create learning experiences that are supportive to all learners. A learning experience that is adaptive and enhances our natural style of learning with machine intelligence can be thought of as AI-assisted learning (AIAL).
Artificial intelligence is able to identify patterns and make decisions that are valuable to users. When we look at learning as it pertains to humans, there are also many unique patterns. The function of AI in learning is helpful because it can not only detect these patterns at scale, but it can also use data to provide suggestions that improve the way someone is learning in real time. AI-assisted learning considers factors such as a student's background, the subject, modes and environment to create an integrative learning experience.
Understanding different learning methods is important, as it will help design AI models that can aid them, which is what AI-assisted learning attempts to do. The field of learning sciences examines learning as it occurs in different environments, like at school or home. It asks the questions: How do we learn? And how do we apply what we learn?
There are many ways to go about learning something new. For example, a college student learning math theory might choose to read about it in a book. The problem is that there is usually an overwhelming amount of resources and choosing the right ones can be difficult. Reading on its own isn't always sufficient if the person can't relate or identify with the information.
In this instance, AI-powered algorithms can track the way the student is interacting with the text, like where they tend to pause on a page and the duration of time they spend reading about certain topics. Over time, the machine will be able to make an assessment on their comprehension levels and match them with material that is more relevant and purposeful. This same logic can be applied to video learning. Similar algorithms can track a user's clicks, likes and comments to show them videos that are better suited to their learning style.
In the case of a high school student learning algebra, hands-on practice problems will probably be more effective than just reading about it. Algebra is composed of blocks that build off of each other, and it is important to understand the foundations before moving on to the next. AI-assisted learning is conducive to material that needs to be taught in increments.
Machine learning algorithms can take in information such as homework results and test scores and group together the students who performed similarly and suggest supplementary material that has helped students with a similar profile in the past, like the appropriate equations to use and how to apply them. As the system learns, it can generate the most relevant practice problems and personally guide students through them.
Another way AI-assisted learning systems can help students learn is through adaptive gamified systems that keep students focused and engaged. These systems also make learning dense material more digestible. Models have been created that use facial expressions and task information to determine the emotions of the user and predict their performance.
Though there are limitations, researchers are exploring how common attributes such as personalities and learning styles interact with them. For younger students learning basic math principles, the systems could use facial identification features to detect when they are struggling and redirect them to content more aligned with their learning style.
The opportunities for AI-assisted learning in education are endless. With it, the unique ways we absorb and retain information could work in favor of our learning rather than against it. Its ability to detect patterns and adapt at scale makes it a tool that is applicable to anyone.
As the machines grow smarter, they will be able to draw insights that can equip teachers and leaders with important information, like how effective teaching methods are in different subjects or what material to suggest to students. In time, students could be equally enabled and encouraged to explore learning as it pertains to them. Education can become more personalized and accessible as AI-assisted learning is adapted, which will grant students everywhere the freedom to learn at their own pace and style.
CLICK HERE TO WATCH MAKING OF B-AIM: