If you’re wondering about the future of artificial intelligence, you might start with the present. What’s currently going on in higher education with AI is truly mind-blowing, and it’s moving forward at a pace most would find hard to believe.
While most of the higher ed world has been focusing on distance learning, MOOCs, and online degrees, a select few are already planning the next level of EdTech, and it looks an awful lot like a sci-fi movie.
Australia’s Impending Leap Forward
The University system in Australia is poised to undergo a dramatic shift in the next five years, because of artificial intelligence software that can read students’ emotions.
Already a pioneer in online learning, Australia is taking things to the next level with an online learning program that can read and respond to students’ facial expressions. Using video imagery, the software can detect boredom, for example, and then react accordingly to keep students engaged.
Software can detect boredom on students’ faces and react accordingly.
Alternatively, if a student is distracted, the software can detect that, too. Once that’s sensed, the idea is for the learning platform to do something that will snap back the attention of wandering eyes and brain.
In the 2016 NMC Technology Outlook for Australian Territory Education, a plan is laid out that puts this type of technology in the hands of Aussie universities within the next four to five years.
The software isn’t limited to simply recognizing boredom and distraction. The report describes a system of online learning that will also will help motivate students as well as raise their confidence levels. Programs will also recognize satisfaction, intimidation, and a whole range of human emotions students feel during learning.
What’s more, the software is bona fide artificially intelligent. That is, it “learns to learn”, becoming smarter as it interprets learners’ facial expressions. This is all made possible by a division of computer science called affective computing.
What is Affective Computing?
Affective computing is, in as few words as possible, emotion-sensing software.
According to Joseph Grafsgaard, of North Carolina State University, and whose lab has created an automated tutor system, there are three pillars of affective computing. Machines must be able to perform at least one of the functions described in the pillars in order to be considered artificially intelligent:
2. understand emotion
3. express emotion
One example of affective computing already in use is something surprisingly universal: streaming music. Programs that guess your mood based on the songs you play are already in use. You might one morning choose a few empowering songs whose vocals are belted out, and the software may suggest some Celine Dion. Oh, you’re under 30? Make that Rihanna.
This would satisfy the first pillar of affective computing: recognizing emotion.
Note that none of the pillars covers feeling emotion- current technology isn’t quite there yet. Although we do have AI (or rather, Google does) that can teach itself video games, it’s hardly ready for teaching college students.
For a sense of what a sentient robot might look like, check out the movie Chappie, which tells the story of an AI robot who learns emotion.
What This Means for EdTech
For the EdTech world, the way affective computing works is even more impressive because it has the power to change completely and forever the way we teach in both the K-12 and the post-secondary levels.
Combined with adaptive learning, Affective Computing will change everything for e-learners.
What is Adaptive Learning?
Adaptive learning seeks to take students from passive e-learners to engaged collaborators for their own learning. This too falls within the realm of futuristic EdTech, although it’s much more integrated already here in the U.S.
If we’re going to scale distance learning while ensuring quality education, adaptive learning is key. The unique needs of every individual learner must be taken into account, and computers which can adapt their online learning experience according to how well they perform are already put in place in education and in business.
Machines will soon be able to recognize patterns in how students learn. The software will analyze written work as well as speech, and use that data to tailor instruction for each student. Eventually, students will be able to communicate with machines in natural ways, just as they would with a human.
That opens things up for even deeper collaboration between student and computer-tutor because it transforms learning into an active phenomenon. Pre-affective computing e-learning has been pretty passive up until recently.
Combined, these two branches of technology should help to speed up learning time and improve learning outcomes.
What This Means for the Rest of the World
Over one hundred patents have been filed here in the U.S. for affective computing technologies. Not all are from the educational community, however. The implications for marketers are as game-changing as they are for educators so the ad world is equally thrilled about affective computing.
If marketers could see your face as it reacts to their ads, they could adapt on the spot and either send more of the same if you look pleased or interested or switch things up if you look annoyed.
That’s really what affective computing aims to do for online education. Like a good human teacher, software picks up on non-verbal clues given by students, and actively promotes strategies to keep them engaged. Teachers who fail to recognize emotions on their students’ faces usually make bad examples of their profession.
Ferris Bueller’s economics teacher failed to recognize boredom on his students’ faces, something software can now do.
This is all uncharted territory (excluding the extraordinary imaginations of science fiction authors and Hollywood filmmakers). We’re still a long way off from machines that actually “feel”emotion. For that, they’d have to be self-aware, something that’s not currently possible.
But for the moment, we have Australia to show us the way in what’s possible for online education and the future of EdTech.