#innovation

Since 2023, nearly every educational AI tutor has adopted the same design philosophy: withhold the direct answer and offer a guiding question in return. Khanmigo, ChatGPT's study mode and Gemini's Guided Learning all rely on this approach. The underlying pedagogy is sound, yet two recent trials suggest we have been focusing on the wrong thing entirely.

Every university I know is running AI pilots, and I think that is right. Experimenting is how we work out when, how and why these tools belong in our teaching. It is part of the negotiation, and some of the most useful lessons I have seen came from small trials that went a bit sideways.

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, every conversation about GenAI in education lands on the same question: who is responsible for figuring this out? Senior leadership? IT? The faculty development office? A working group with a mandate expiring in June?

Up until now, I have only ever used AI chatbots as “simple” yet powerful tools. I give a command, and the system executes a task. I have never felt any urge to use them for casual chatting or in any genuinely conversational way. Because of this, the well-known "amnesia problem" of AI also never really bothered me.

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.

If you feel a bit overwhelmed by the sheer number of AI tools out there right now, you are certainly not alone. With a new model or breakthrough announcing itself every week, simply figuring out what to actually use this technology for has become a task in itself.

Higher education institutions create remarkable teaching innovations. Early adopters experiment, sometimes grants fund pilots, and conferences celebrate successes. Yet, a frustratingly consistent lifecycle unfolds: innovations remain trapped in their local silos. They flourish within the boundaries of specific modules or departments, but often evaporate as soon as the pilot funding is exhausted or the pioneering professional moves on.

I've got hundreds of papers in Zotero. When I need to reference something, I know it's in there somewhere—a study about student feedback, that framework on assessment design, the article with the perfect quote. But finding it means scrolling, searching, hoping I tagged or summarised it properly six months ago.