As tech companies release a slew of generative AI updates, there’s a growing risk that educational practices and policies are struggling to keep up with new capabilities. From the Future of Being Human Substack.
It’s a common occurrence: A colleague asks me about students using artificial intelligence in class, and it turns out that their perceptions of what generative AI does and how students are using it are 18 months and a lifetime out of date.
Im exaggerating of course, but in a week where OpenAI, Google, and Apple, have all made major announcements around increasingly advanced uses of AI, the lag between what educators think is happening, and what is actually happening, is widening at a frightening rate. And this has implications all the way from the policies and pedagogies used by individual instructors, to institutional policies around the uses and misuses of AI.
The past few weeks have seen conflicting messaging around whether the seemingly exponential growth of AI capabilities in recent years is finally slowing down. There’s certainly a sense in some quarters — but by no means all — that advances in the large language models underpinning many generative AI platforms aren’t fueling the transformative leaps we were seeing a year of so ago. And this has led to some cynicism over projected AI futures — including its ability to bring about transformational leaps in education.
Yet this cynicism overlooks a considerable lag between what emerging models are capable of, and how this is being incorporated into real-world applications. And in the field of education, this lag is being amplified by outdated perceptions of both the capabilities of AI and how these are being utilized.
Although I’m sure some will disagree, there are indications that large language models are following a classical tech innovation “s-curve” — a curve that’s characterized by slow initial development followed by near-exponential growth, and finally a tailing off as new capabilities plateau out …