#innovation

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.

For the past few years, I've been experimenting with various large language models (LLMs) and tools to better understand the sense and none-sense of generative AI (GenAI). In my quest for meaningful and efficient uses, I've created various tools for personal and professional use (think: article collector and personal home voice assistant). My sixth iteration of an LLM-based qualitative analysis tool is a prime example of where these models can be quite handy. Here’s what I've learned through trial, error and extensive reading.

There is a Japanese word, 'tsundoku,' for the habit of buying books and letting them pile up unread. My pile was digital: a long list of blogs and articles on teaching, learning, generative AI... The pile grew. So did the feeling that I was falling behind.

Every truly revolutionary tool on campus has a story. Not the story of its features, but of its impact: a colleague who finally perfected their dream seminar, a team that unlocked a new way to collaborate with students. These are the moments that matter. They're the sparks that make us lean in and ask a trusted peer, “How did you do that?”