Content in active development
How universities can tap into the hidden expertise of their teaching and learning professionals.
At Monash Online, researchers gave the same AI-powered Socratic chatbot to a group of postgraduate computer science students and watched two entirely different learners emerge from identical software. One student used it the way most of us use a search engine: ask a narrow question, take the answer, move to the next topic. The other directed it: asked it to connect ideas across weeks of material, pushed back when an explanation didn't add up, caught it in a mistake, and pulled new concepts back into their own disciplinary context (de Barba, 2026). Same model, same interface. What separated them wasn't the tool.
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.
When the university rolled out a new intranet, every staff member was auto-sorted into a department, a manager, and a job family, pulled straight from the HR system. Hers came back blank. Nobody in IT or HR could explain why, beyond agreeing that her job didn't sit anywhere the system had a box for. Someone finally asked, half-joking, what she actually did all day. Nobody in the room had a clean answer. That specific anecdote might be imagined, but the reality it describes is universally recognizable to anyone who does this work. That blank space, that lack of a clean answer, isn't a glitch. It is exactly what the role is built from.
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.
Earlier today I attended a webinar organised by the Community for Educational Innovation (a European Commission initiative) titled Educating to Thrive in the Digital World. One of the interventions was by Julian Estevez, Professor at the University of the Basque Country, who posed a deceptively simple question: Personalised education with AI — a myth? His presentation was brief introduction for a full argument, and it was the kind of thing that immediately starts pulling threads.
Conversations about pedagogy and technology in higher education tend to produce a quick consensus: pedagogy first, technology second. Define the learning goals, then choose the tools. It is a position most educators feel comfortable with, and for good reason — it keeps the focus on purpose rather than on the tool itself. What is worth exploring, though, is whether that sequence actually holds up in practice.