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Last week, I was lucky enough to speak with colleagues at the University of Toronto about where we’re at with AI and education and where we might go next.

The goal of learner discovery is to deeply understand the “why” of your learners. Research shows that by doing this, we can optimise our designs for learner motivation and, as a result, learner achievement. 

There are a lot of AI-powered “summariser” tools on the market. These tools allow us to paste in unstructured text and have AI identify important sentences, extract key phrases and summarise the main points of the document. My research shows that lots of us are using AI summariser tools to help us to learn more from notes that we take in class, in work, while reading documents, watching videos and listening to podcasts etc. But, while summarising and giving structure to information can help to manage cognitive load and support basic recall, it doesn’t in itself help us to learn

My initial research suggests that just six months after Open AI gave the world access to AI, we are already seeing the emergence of a significant AI-Education divide. If the current trend that continues, there is a very real risk that - rather than democratising education - the rise of AI will widen the digital divide and deepen socio-economic inequality. In this week’s blog post I’ll share some examples of how AI has impacted negatively on education equity and - on a more positive note - suggest some ways to reverse this trend and decrease, rather than increase, the digital and socio-economic divide.