Artificial Intelligence and Machine Learning are frequently talked about as linchpins for revolutionizing student support and operational efficiency in education. More often than not, however, the two terms are used interchangeably. While Artificial Intelligence and Machine Learning are very similar, there are distinct differences between them. If you’ve ever been uncertain of the difference between the two or what their value in education is, read on.
Artificial Intelligence
Machine Learning
The concept of Machine Learning (ML) actually falls under the broader umbrella of AI. What distinguishes ML from AI is the “learning” aspect of the term. The idea stems from the notion that humans should be able to provide machines with a set of data and allow the machines to “learn” for themselves. Then, through repeated study of different data sets combined with validation of what is correct and what isn’t (either by sensing if the conclusion is right on their own, or by being told from a third party), the machines will “learn” more about the subject matter through finding common patterns and nuances. In short, ML is designed to learn and develop its own capabilities whereas AI is restricted to the data and instruction human programmers provide the machines.
Computer Games
The example I like to give when explaining the difference between AI and ML goes back to the example of computer games. Imagine you’re playing a virtual 5 on 5 basketball game against the computer. Much like in a real game, where the coach of a team can assess the tendencies of the opponent and use that knowledge to make adjustments to counter the opponent’s play, a computer can do the same thing. Throughout the entire game, the computer is gathering data on your playing style, and it changes its behavior to increase its chances of defeating you. If it notices that you use a certain player on your team to score the majority of your points, the computer may elect to double team that player, forcing you to score using someone else, which may be more challenging for you. If this was a best of 3 games series, the computer may start the next game with the double team strategy if it was effective during the first game. These adjustments in response to gathering data signify the “learning” aspect of ML. AI, using the same example, would be the computer being programmed to play in a way that mimics human decisions but won’t “learn” from the data gathered during the current game to impact its strategy for the next game you play against it. won’t change its strategy during the game depending on how you, the user, is playing.
The Education Sector
Now, as far as education goes, what do these two technologies bring to the table? The answer can be its own blog post, or maybe even a series, but here are a few quick examples.
Enormous Potential
There are a number of ways that AI and ML can impact education moving forward. A big reason is the fact that the internet has become so integral to the operations of many organizations and contains endless amounts of information formatted in a way that’s easy for machines to decipher. Since machines have advantages in speed, accuracy, and lack of bias over humans, they will be positioned to take over tedious administrative tasks, freeing up school staff to focus on other areas like student support, and will potentially introduce groundbreaking processes to increase student success. It’s useful to understand the differences between AI and ML so that as we look at ways to build better systems for students and school operations, we’re making the right choices.