Before you go searching for a new career, consider the role of mirror neurons in human learning. Mirror neurons are “a type of brain cell that responds equally when we perform an action and when we witness someone else perform the same action.” In short, an important aspect of learning in primates is observing and imitating. Human beings learn best from emulating and practicing with other human beings. The teacher in the classroom is a human model of mastery for a particular content area, with skills and values that are uniquely human—and a student is simply not going to relate to AI with the same capacity. Knowing their teacher is not human puts great strain on a student’s ability to empathize and imagine themselves as mastering the content and skills at hand. A computer is a computer. Computers are capable of levels of calculations we could only dream of achieving with our minds alone. But when a human teacher is able to show the benefits to learning content—and indirectly proving not only that it can be done, but that it is something to aspire to—learners are more likely to be inspired to work harder and make progress.

In fact, some argue that due to this lacking connection, robots simply can’t inspire us. And sometimes we underestimate the importance of empathy in the learning process. Don’t. Because despite incredible advances in the field, this is the heart of the limitation to AI as it stands today. The central challenge faced by developers tackling the role of AI as it applies to the real world continues to be: how do you teach a computer context and intuition? As an example, let’s imagine a full humanoid robotic AI educator at the front of the room. Surely, this AI is less likely to make content-area errors. It will be able to access absurd amounts of information in the blink of an eye. It would be able to listen to answer questions. It might soon even have the ability to read the faces of students and respond accordingly. We have this technology, and it is expanding every day.

What it won’t be able to do is combine these elements along with a thousand other human variables to create meaning. When a student shuts down in your classroom, the human educator is able to do just that. We can read a student’s face, body language, appearance, and any number of other pieces of data to infer an emotional state. However, we are also able to cross-reference that inference with context: how does the student usually respond to lessons, what is going on at home, what are you noticing in the general social dynamics of the classroom, did they get in an argument with their best friend this morning, did they eat breakfast, did they sleep well, was a new video game released yesterday, is it particularly humid in the building today, what’s going on in the general school culture right now, has this student been taking tests all day, are elements like depression or anxiety potentially relevant, or is it just an “off day” for a great student? We can then use our intuition to create a solution for that student. Even the most impressive AI would still struggle with appropriately analyzing the complex needs and immediate cues communicated by a classroom full of students. The context of knowing each individual student holistically, combined with the intuition of assessing the richness and complexity of a “classroom moment” is simply out of reach for AI. And despite progress, developers can’t imagine you holding your breath any time soon.

So, chances are good your job is safe for the near future. But it is going to change, and AI is going to be a part of that ignition. For what AI lacks in its empathic abilities, it certainly makes up for in its pure computing power, simplicity of interface, and information storage capabilities. And although robots will not be ushering us from our desks, they will be joining us quite soon … as teaching assistants.


Article by: Busayo Tomoh

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