Artificial Intelligence (AI) “I’m sorry, I don’t know how to help with that … Yet.”

For anyone who has invited Google Home, Amazon Echo, or other intelligent personal assistant devices into your home, the effect can be, well, disarming. Alexa (on Amazon Echo) likes to be addressed by name, while Google Home responds to “OK, Google” as the conversation starter. These are two of the leading intelligent personal assistants on the market, designed to help you manage your life, stay on top of your hectic schedule, and respond to your in-the-moment need for all types of information. And they’re just getting started.

Whichever of these “assistants” you might have welcomed into your home, it’s an intriguing taste of where artificial intelligence (AI) is at, and a portent of where it might be headed. Google Home can do everything from calling out the weather forecast for the day, to managing your shopping list, to telling jokes, to hauling out a definition of whatever new terminology might be puzzling you. She can recognize your voice, so with more than one household member talking to her, she can personalize better to the individual. If on occasion you ask her something that’s beyond her reach, she might respond that “I’m sorry, I don’t know how to help with that yet”, but, given the range of things she can help with, that “yet” at the end might feel just a little ominous.

And that’s the exciting and maybe even scary thing. The “intelligence” of AI is growing all the time. And AI is out there in many forms, from Amazon’s product recommendations to self-drive cars.

Let’s step back for a minute, though, and clarify what exactly AI is.

AI is a broad umbrella term for making software simulate the things that humans can do, like solve problems, play chess, or drive a car, and ultimately do those things better and faster than humans. In other words, AI is about building machines capable of intelligent behavior. The scope of AI is broad, with AI powering features on your smartphone, in self-driving cars, in your Amazon recommendation list. And then there are the amazing new advances that we haven’t figured out yet that might change our world dramatically in the not too distant future.

Because of its scope, we have different categorizations of AI, from “narrow” and “weak” AI to “general” AI. Narrow is machine intelligence that specializes in just one area, such as chess or driving. And this is the kind of AI that some predict will result in a huge number of jobs being replaced. General AI, at the other end of the spectrum, refers to AI systems that can theoretically handle any task – just like human intelligence; such systems are a lot less common than their narrow AI cousins.

There are lots of exciting advancements in the AI field, including in the learning space. At Learning Technologies 2017, Donald Clark offered insight into a few of the emerging examples of AI at work in learning. From the online tutor bot that successfully supported students through a semester in an AI module at Georgia Tech, without the vast majority of students realizing it wasn’t a human, to the AI “student” that performed in the top 20% of students the University of Tokyo entrance exams, it’s clear that AI is not only creeping into learning, but has huge potential in terms of application.  

And while these types of educational projects may not be very common, aspects of AI offer several affordances that can be used today in learning. Everyday apps such as Netflix, YouTube and Amazon use AI to make personalized suggestions or recommendations to us based on our prior activity, our usage patterns, repetitions, and so on. This kind of personalization is readily applicable in learning, guiding learners to what to study next based on previous usage and history.

Data can also help uncover better ways to design learning – helping to identify students who might be at risk of failure, pinning down what’s working, what’s not, what’s needed, and how to improve our teaching and learning approaches. And with robot writers offering a future where content is produced in a different way, the possibilities are huge for learning environments that utilize the best of system-based and human-based intelligence.

So, if you’re involved in learning, perhaps it’s time to start looking at where, what, and how AI can enhance the way we design, develop, deliver, and optimize workplace learning. AI, along with clever use of data, has much to offer in helping us shape more powerful user-centered experiences that meet the needs and wants of modern learners and the changing nature of work.

And while there are certainly some things that your virtual home assistant can’t assist you with yet, one can’t help thinking that the “can’t help” response may soon become a thing of the past.

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