The Beginner’s Guide to Artificial Intelligence For Educators

The Beginner's Guide to Artificial Intelligence

This is the second in a 7-part series on the Future of Learning. You can read the first post here, and join the discussion on our Facebook page: The Future of Learning.

Artificial intelligence is already all around us. Any time a device, machine, or digital object has cognitive functioning abilities we are witnessing artificial intelligence. Think about thermostats like Nest, that know what temperature the house should be without it being input by a human. We don’t see it as “artificial intelligence”, but just a new and cool technology, that eventually becomes normal.

As Tim Urban (of points out, Artificial Intelligence is currently popping up in your life in many different places including:

  • Cars are full of AI systems, from the computer that figures out when the anti-lock brakes should kick in, to the computer that tunes the parameters of the fuel injection systems. 
  • Your phone is a little AI factory. When you navigate using your map app, receive tailored music recommendations from Pandora, check tomorrow’s weather, talk to Siri, or dozens of other everyday activities, you’re using AI.
  • Your email spam filter is a classic type of AI—it starts off loaded with intelligence about how to figure out what’s spam and what’s not, and then it learns and tailors its intelligence to you as it gets experience with your particular preferences. 
  • You know the whole creepy thing that goes on when you search for a product on Amazon and then you see that as a “recommended for you” product on a different site, or when Facebook somehow knows who it makes sense for you to add as a friend? That’s a network of AI systems, working together to inform each other about who you are and what you like and then using that information to decide what to show you. Same goes for Amazon’s “People who bought this also bought…” thing—that’s an AI system whose job it is to gather info from the behavior of millions of customers and synthesize that info to cleverly upsell you so you’ll buy more things.
  • Google Translate is another classic AI system—impressively good at one narrow task. Voice recognition is another, and there are a bunch of apps that use those two AIs as a tag team, allowing you to speak a sentence in one language and have the phone spit out the same sentence in another.
  • When your plane lands, it’s not a human that decides which gate it should go to. Just like it’s not a human that determined the price of your ticket.
  • Google search is one large AI brain with incredibly sophisticated methods for ranking pages and figuring out what to show you in particular. Same goes for Facebook’s Newsfeed.

What’s interesting is that AI has grown by leaps in the past decade, and the past few years. So much so that Elon Musk created an organization, OpenAI, to help in the advancement and also control of AI in the coming years (he’s a bit scared of the implications).

Here’s what Wired magazine said about the coming AI revolution:

But in the field of AI, the change is real. Inside places like Google and Facebook, a technology called deep learning is already helping Internet services identify faces in photos, recognize commands spoken into smartphones, and respond to Internet search queries. And this same technology can drive so many other tasks of the future. It can help machines understand natural language—the natural way that we humans talk and write. It can create a new breed of robot, giving automatons the power to not only perform tasks but learn them on the fly. And some believe it can eventually give machines something close to common sense—the ability to truly think like a human.

In education, AI is already having an impact, but most of us don’t see it in our classrooms. As author Kevin Kelly says, “If AI can help humans become better chess players, it stands to reason that it can help us become better pilots, better doctors, better judges, better teachers.”

The Basics of AI

Here are the basics, before I jump into more detail: Artificial intelligence (AI) is intelligence exhibited by machines. In computer science, an ideal “intelligent” machine is a flexible rational agent that perceives its environment and takes actions that maximize its chance of success at some goal.

For example, wouldn’t it be nice if you could answer all those student questions you have in class with the same level of understanding and humanity as a short face-to-face conference, but at anytime and anywhere? IBM Watson has created an open and free way to do that with Watson, their AI interface (Watson is just one of many different Open AI options for creating cognified software).

You can go to IBM now and within 10-minutes have a chatbot setup to answer student questions about a specific assignment or topic, without you ever being in the room (or online). Then every time a student asks a question that Watson doesn’t understand, you can help train the program to answer responses that are like this with answers that are already programmed into the AI.

Crazy stuff right? But it’s here and happening right now. (Note: I’m currently building my first chatbot and it’s a lot of fun. You can too, with directions from IBM here.)

The Three Types of AI

While there are many different types or forms of AI since AI is a broad concept, the critical categories we need to think about are based on an AI’s caliber. There are three major AI caliber categories that Urban describes in his epic article on AI:

AI Caliber 1) Artificial Narrow Intelligence (ANI): Sometimes referred to as Weak AI, Artificial Narrow Intelligence is AI that specializes in one area. There’s AI that can beat the world chess champion in chess, but that’s the only thing it does. Ask it to figure out a better way to store data on a hard drive, and it’ll look at you blankly.

AI Caliber 2) Artificial General Intelligence (AGI): Sometimes referred to as Strong AI, or Human-Level AI, Artificial General Intelligence refers to a computer that is as smart as a human across the board—a machine that can perform any intellectual task that a human being can. Creating AGI is amuch harder task than creating ANI, and we’re yet to do it. Professor Linda Gottfredson describes intelligence as “a very general mental capability that, among other things, involves the ability to reason, plan, solve problems, think abstractly, comprehend complex ideas, learn quickly, and learn from experience.” AGI would be able to do all of those things as easily as you can.

AI Caliber 3) Artificial Superintelligence (ASI): Oxford philosopher and leading AI thinker Nick Bostrom defines superintelligence as “an intellect that is much smarter than the best human brains in practically every field, including scientific creativity, general wisdom and social skills.” Artificial Superintelligence ranges from a computer that’s just a little smarter than a human to one that’s trillions of times smarter—across the board. ASI is the reason the topic of AI is such a spicy meatball and why the words “immortality” and “extinction” will both appear in these posts multiple times.

As of now, humans have conquered the lowest caliber of AI—ANI—in many ways, and it’s everywhere. The AI Revolution is the road from ANI, through AGI, to ASI—a road we may or may not survive but that, either way, will change everything.

The Basics of Machine Learning (How AI Works)

In order to understand the implications of Artificial Intelligence (remember, it’s already all around us), it’s good to get a basic understanding of how it works, and why exponential growth in this field is not a question of “will” it happen but “when” it will happen. AI is powered by large amounts of data, incredibly fast computing and data processing, and machine learning (in the form of algorithms).

Adam Geitgey wrote a long and detailed series on Machine Learning this year. It is worth the read if you are interested in learning more, but for now, I want to share some of his key points on Machine Learning 101:

Machine learning is the idea that there are generic algorithms that can tell you something interesting about a set of data without you having to write any custom code specific to the problem. Instead of writing code, you feed data to the generic algorithm and it builds its own logic based on the data.

For example, one kind of algorithm is a classification algorithm. It can put data into different groups. The same classification algorithm used to recognize handwritten numbers could also be used to classify emails into spam and not-spam without changing a line of code. It’s the same algorithm but it’s fed different training data so it comes up with different classification logic.

This machine learning algorithm is a black box that can be re-used for lots of different classification problems.

“Machine learning” is an umbrella term covering lots of these kinds of generic algorithms.

Two kinds of Machine Learning Algorithms

You can think of machine learning algorithms as falling into one of two main categories — supervised learning and unsupervised learning. The difference is simple, but really important.

Supervised Learning

Let’s say you are a real estate agent. Your business is growing, so you hire a bunch of new trainee agents to help you out. But there’s a problem — you can glance at a house and have a pretty good idea of what a house is worth, but your trainees don’t have your experience so they don’t know how to price their houses.

To help your trainees (and maybe free yourself up for a vacation), you decide to write a little app that can estimate the value of a house in your area based on it’s size, neighborhood, etc, and what similar houses have sold for.

So you write down every time someone sells a house in your city for 3 months. For each house, you write down a bunch of details — number of bedrooms, size in square feet, neighborhood, etc. But most importantly, you write down the final sale price: 

This is our “training data.”

Using that training data, we want to create a program that can estimate how much any other house in your area is worth:

We want to use the training data to predict the prices of other houses.

This is called supervised learning. You knew how much each house sold for, so in other words, you knew the answer to the problem and could work backwards from there to figure out the logic.

To build your app, you feed your training data about each house into your machine learning algorithm. The algorithm is trying to figure out what kind of math needs to be done to make the numbers work out.

This kind of like having the answer key to a math test with all the arithmetic symbols erased:

Oh no! A devious student erased the arithmetic symbols from the teacher’s answer key!

From this, can you figure out what kind of math problems were on the test? You know you are supposed to “do something” with the numbers on the left to get each answer on the right.

In supervised learning, you are letting the computer work out that relationship for you. And once you know what math was required to solve this specific set of problems, you could answer to any other problem of the same type!

Unsupervised Learning

Let’s go back to our original example with the real estate agent. What if you didn’t know the sale price for each house? Even if all you know is the size, location, etc of each house, it turns out you can still do some really cool stuff. This is called unsupervised learning.

Even if you aren’t trying to predict an unknown number (like price), you can still do interesting things with machine learning.

This is kind of like someone giving you a list of numbers on a sheet of paper and saying “I don’t really know what these numbers mean but maybe you can figure out if there is a pattern or grouping or something — good luck!”

There are lots of other types of machine learning algorithms. But this is a pretty good place to start.

What Is The Impact on Education?

There seems to be two camps of people when you talk about Artificial Intelligence and our collective future. One camp of people see a massive potential for GOOD, and are excited about the possibilities on mankind with AI by it’s side.

The other camp of people are extremely weary of the possible implications, and are currently putting safeguards in place so AI can not get to a place where it has the ability to dominate our way of life.

Like any technology, this can be used for good or bad. Often our varying perspectives see the worst or the best in these types of situations.

I’ve spent a lot of time looking at the implications AI is currently having on education (specifically learning) and what it means for our future as parents, teachers, and educators.

Three specific implications of AI that could disrupt (and are currently disrupting) education:

1. The Death of Standardized Tests: Currently AI-powered apps like PhotoMath have shattered our perception of homework, and what students can do in and out of Math class. PhotoMath is one of many examples where AI has taken tasks that used to take a lot of training, learning, and struggling (figuring out how to solve Algebraic equations) and simplified it to putting your phone over a problem and watching it solve itself.
The same type of AI is working to automatically translate words from one language to another (with a small device in your ear), and can correct grammar, style, and content on your essay. In short, with these AI abilities already available, it’s easy to imagine a world where the current types of questions on standardized tests become obsolete.

2. A Different Definition of Learning: Although learning has drastically changed over the years (how, when, where and why we learn), the idea of learning has stayed much the same. We acquire new knowledge and then activate that understanding when we apply it in some way physically or mentally. However, if we are living in a world where knowledge is easy to come by, and application can be assisted by an AI, then learning becomes something new and altogether different.

And example of this might be best observed in learning a language. The goal of learning a language is to converse with those that speak a different language, but many of us don’t do this at a young age and then struggle as adults. Well, if AI makes it easy to understand different languages in real-time, and therefore eliminates much of that language-barrier…then learning language looks different. Instead, it is more focused on learning the AI interface and helping it to get better at understanding local dialects etc.

3. The Emergence of AI Buddies: There is an awesome new App called Wonder. As described by Techcrunch:

There’s a lot of information that we can’t access via a Google search, but instead tend to make a mental note of in order to recall. Sometimes, we might jot these things down in Notepad, but often we forget to do that, too. A new bot called Wonder wants to help by remembering anything you want, then return the information you need via a text message.

It’s a pretty simple but clever idea. After you go to the Wonder website and provide your phone number, the bot sends you a text that explains how it works.screen-shot-2016-08-12-at-10-40-23-am

Basically, you just text Wonder the information you’ll need to recall at a later date, and it stores that for you in its system. When you’re trying to later remember something, you just text Wonder a question, like “Who’s our company’s dental insurance provider?,” “When’s the next company meeting?,” or whatever other information you’ve previously fed into it by way of text message. The bot will promptly respond with the answer.


What does this mean? It is the emergence of “Siri-like” apps that serve as personal assistants with better memory and cognitive capabilities than most of us have as adults! These “AI Buddies” will be tied into our students’ everyday lives and able to help the recall, analyze, and solve all types of easy and hard problems as they go throughout life.

Artificial Intelligence is only starting it’s exponential growth, but I believe it has never been more important (as an educator and parent) to understand where it is headed and how it will impact our lives, our children’s lives, and our world as we know it.

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Join the discussion 4 Comments

  • Ron Abate says:

    I can see AI being used to personalize instruction: (1) determine what will best motivate a child to learn, (2) the best way to instruct a child, and (3) assessing fatigue or boredom.

  • Mia says:

    Thanks for crafting this insightful post on AI! I was especially intrigued by the idea of AI buddies becoming an integral part of student life.

    I tried out the Wonder app after reading your post and played around with it for about 3 minutes before I started thinking more deeply about data collection and privacy. I think it’s a helpful tool and have no qualms with using it but I’m curious about whether someone who grows up with AI will think of it as a convenient and natural way of life without the levels of discomfort that older generations may have.

    I think it will be amazing if students eventually have an AI buddy that could serve as a one-on-one tutor. One benefit is that an AI buddy might not have the same conscious or unconscious bias that a human might have, which could potentially help level the playing field. Also, AI tutoring has great potential to make education more individualized, affordable, and accessible if we strive toward ensuring all students have access to powerful smart-devices.
    One downside of an AI buddy is that the intelligence, although vast, might be homogenized. Although an AI buddy might be able to provide direct and accurate information, it might not necessarily be able to impart unconventional information that might otherwise come from a human who is able to have a unique perspective based on cultural background and a lifetime of experiences. (I could be completely wrong here – who knows!) In any case, AI buddies will need to be an addition and not a substitution for human relationships. As you mention in another post, “it all comes back to relationships”. Students will need to learn how to effectively (and perhaps safely?) cultivate a relationship with an AI buddy versus a human, and understand the differences and values of both types of relationships.

    Thanks for inspiring me to think about what lies ahead. I look forward to your other posts in your Future of Learning series!

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