Real Questions About Artificial Intelligence in Education
Don’t doubt it: Machine learning is hot—and getting hotter.
For the past two years, public interest in building complex algorithms that automatically “learn” and improve from their own operations, or experience (rather than explicit programming) has been growing. Call it “artificial intelligence,” or (better) “machine learning.” Such work has, in fact, been going on for decades. (The Association for the Advancement of Artificial Intelligence, for instance, got rolling in 1979; some date the ideas back to the Greeks, or at least to the 1940s during the early days of programmable digital computers.)
More recently, Shivon Zilis, an investor with Bloomberg Beta, has been building a landscape map of where machine learning is being applied across other industries. Education makes the list. Some technologists are worried about the dangers. Elon Musk, for instance, has been apocalyptic about his predictions, as the New Yorker wrote. He sparred this past week with a more sanguine Mark Zuckerberg. (The Atlantic covers it here.)
Investors are nonetheless racing ahead: this week, Chinese language learning startup, Liulishuo, which uses machine learning algorithms to teach English to 45 million Chinese students, raised $100 million to accelerate its work.
To explore what machine learning could mean in education, EdSurge convened a meetup this past week in San Francisco with Adam Blum (CEO of OpenEd), Armen Pischdotchian, (an academic technology mentor at IBM Watson), Kathy Benemann (CEO of EruditeAI), and Kirill Kireyev (founder of instaGrok and technology head at TextGenome and GYANT). EdSurge’s Tony Wan moderated the session. Here are a few excerpts from the conversation:
EdSurge: Artificial intelligence has been promising to transform education for generations. How close are getting? What’s different now?
Benemann: There’s so much more data than ever before. For us at EruditeAI, data is more precious than revenue. With better data, we can better train our algorithms. But the important point to remember is that the makers of AI are ultimately us, humans.
Pischdotchian: If you think back on the education model of your earlier years, we called it the factory model. Teachers broadly taught same subject to all students. That isn’t what we’re talking about today. Groups such as the Chan Zuckerberg Initiative are looking to overhaul this model. Learning can’t be done according to the factory model any more. It isn’t sustainable. What will industry require for today’s kids to flourish doing what we call “New Collar” work?
Kireyev: We’re seeing a data explosion in education content—both data for and from students. We can see what students are doing, far more rapidly than in the past. When kids work on Scratch, for instance, their work is web-based: You can see when they start watching a video, when they stop, when they’re bored. You get a lot of insight into their behavior. Transparent data collection is incredibly valuable. And there’s greater availability of the technology—things that you can literally use out the box. So more people are trying to do things with AI and machine learning.
Okay, we’ve heard about the data explosion and about the need to change school models. What else is going on?
Blum: There are two big trends going on—and we’re just at the beginning of this. We work with IMS Global Learning. Technical standards, such as Caliper, and xAPI (or Experience API)are just taking off. And second, there are a whole lot of areas, education is one of them, where you don’t have long-term data. So if you want to pick the next best thing [problem] for a student, you have to use a different approach called reinforcement learning. So if I don’t have a million data records, I can explore as I go. It’s how Google solved the AlphaGo challenge.
What applications do we see of AI in education? Are we using it already?
Pischdotchian: This is about finding patterns in learning experiences.
