Monthly Archives: November 2018

Hey Teacher: AI is Your Friend, Not Enemy


We are told from left and right that artificial intelligence (AI for short) is going to take our jobs: four million jobs in the UK in danger to disappear, 47 percent of US jobs are going away, as much as half of all professions vanishing during the next decade.

Luckily not all news stories are as gloomy. One research dealt with a thousand companies and estimated that AI will create new jobs in 80 percent of the cases. More moderate predictions claim that professions are going to both appear and disappear. A recent study by PricewaterhouseCoopers (PwC) stated that in the years 2017-2037 the UK job market is going to see seven million jobs to go away, but 7,2 million new ones to emerge.

You Cannot Replace a Teacher with a Machine

How about teachers, then? Are they going to be fine? Yes, claims PwC. Teaching is one of those professions which are hard to replace with computers or automation. I couldn’t agree more. Deep knowledge of learners’ personalities, ability to adapt to dynamics in the classroom context, and interacting with the study group accordingly are in the hard core of teaching. No one or nothing knows the class better than the teacher.

Instead of replacing teachers, AI can help them in doing their job better. Teaching involves several routine tasks which could be partly or completely automatized. For example in Bingel, Sanoma Learning’s gamified learning environment for primary education pupils, checking the answers of simple exercises is automated. Assessment has further opportunities for automation and AI, as does for example planning the upcoming weeks, days, or lessons during a semester.

AI can help also the learners, for example by virtue of personalized learning. A machine can learn to provide the best possible exercises and other content to each learner, as long as it has access to enough data of the learning history. The accuracy can be further improved by adding other context attributes into the equation. For example the social dynamics of the study group, time of the study activity, and even the learner’s emotional state could help to select the most appropriate content. Doing all this can increase the motivation of the learner, which in turn can improve the learning results. In other words: the machine learns to make the human learn.

Learning materials are always designed and tested together with the users of the materials, namely teachers and learners. This applies also to intelligent systems which learn. Even the best data scientist or developer cannot implement good pedagogical solutions without the precious know-how of education professionals. The machine learns to make the human learn, but it all starts with the human teaching the machine.

This post was originally published in Finnish on Sanoma Pro’s blog.