The Computer Program that Can Sense Your Boredom

Whether it’s at work or at university, it can be very easy to slip into an inattentive boredom. Luckily, you either snap out of it before too long or manage to fumble your way through it, just like the majority of Tony Abbott’s political career.

Well prepare to wipe that dazed stupor of your unimpressed face, because the 2016 NMC Technology Outlook for Australian Tertiary Education report reckons “affective computing” will be introduced to online learning platforms in the next 4 to 5 years.

The idea is that the program can recognise when a student is bored (along with a range of other emotions) by analysing them through video. If you think that sounds ultra creepy, it’s because it is.

The paper forecasts “online learning situations wherein a computerised tutor reacts to facial cues of boredom in a student in an effort to motivate or boost their confidence.”

Head of learning technology at Open Universities Australia, Brenda Frisk, says software will have the ability to recognise minute changes in a student’s expression.

“Software technology will literally learn to learn, interpreting and responding to learners’ most nuanced gestures and emotions – whether they are feeling bored, intimidated or satisfied,” she says.

And it turns out the MIT Media Laboratory in the US already has already developed such technology. Its name is Tega, a smartphone robot that reads emotional queues via video, then adjusts its approach accordingly. Terrifying.

It’s unsure just how the tutoring style will change should a student become bored, but it will be interesting to see how it all pans out and what kind of motivators it uses. There is a whole lot of research into the psychology of what the human brain responds to, particularly when it comes to addictive behaviours like gambling. Perhaps some flashing lights and catchy music is on the cards?

Not only will affective computing snap students into gear when learning, it will also create tonnes of useful data around what we respond to, which teaching strategies work the best and how learning can be implemented in its most efficient form.

Welcome to a horrifying future where machines will soon be better teachers than human beings. Skynet, anyone?

Source: AFR