How universities can tap into the hidden expertise of their teaching and learning professionals.
As a native Flemish speaker, I am often disarmed by the way my neighbours in the Netherlands use language. They have a distinct knack not just for coining brilliant terms, but for casually dusting off older, deeply resonant words that I had entirely forgotten existed. Together, these words capture the messy human reality of work, cutting straight through standard corporate jargon.
Smart glasses can already read a live transcript, generate an answer, and float it an inch above the wearer's own eyeline, invisible to anyone looking at their face. Nick McIntosh put it bluntly on LinkedIn: "smart glasses have quietly dissolved the one assessment format everyone thought was AI-proof" (McIntosh, 2026). The format institutions dug up specifically because it felt unhackable lasted, in practice, about as long as it took a wearables company to ship a product.
A programme team accepts a change to their assessment brief from someone who has never taught the course, on the strength of a five-minute conversation. A year earlier, someone else in the same room proposed almost the same change and got nowhere. Same idea. Different person carrying it. That is what decided the outcome.
We don't reason our way into new behaviour. We act, uncertainly and messily, and then make sense of it by telling a story about what happened. I keep coming back to this when I watch institutions respond to AI and other challenges. Sensemaking is retrospective. Strategy is prospective. The gap between them is where people get lost.
The language of "top-down" and "bottom-up" runs through almost every conversation about change in higher education. It carries implicit moral weight: bottom-up sounds authentic and practitioner-led, yet also ungoverned and hard to scale; top-down sounds imposed and procedural, yet also coherent and capable of giving scattered efforts a shared direction. The distinction feels self-evidently useful.
At some point in the last two years, most universities will have convened a working group on generative AI. Some will have produced policy frameworks. Many will have run staff development sessions. A good number will have updated their academic integrity guidelines, published guidance for students, or commissioned an internal review. All of this activity is genuine, and some of it is genuinely useful.