AI

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.

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 educational technology often orbit around efficiency, but the rapid rise of generative AI has forced universities into a long-overdue reckoning with a much deeper question: what exactly are we doing when we provide feedback? If feedback is merely the transfer of corrective information, then large language models have already won. They can parse essays, spot logical flaws, and debug code with astonishing speed. However, reducing feedback to a glorified diagnostic tool misses the fundamental reality of how university students actually learn.

Three years in. That's where we are now with generative AI in higher education. ChatGPT's arrival in late 2022 feels like both yesterday and a lifetime ago. The initial panic ("How do we AI-proof assessment?") has given way to something more interesting, more nuanced, and dare I say it, more hopeful.

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.

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.

When we discuss Generative AI in education, the conversation often defaults to technical skills. But there is so much more to this shift than 'literacy' or tool mastery. The further we go, the clearer it becomes that the challenge is also deeply human. It gets personal, it gets messy, and for many educators, it's an upheaval that strikes at the centre of their professional identity.

Nearly three years after generative AI exploded into the mainstream, a strange quiet has settled over much of higher education. The initial, acute panic over plagiarism has faded, but it hasn't been replaced by a unified, urgent call for redesign. Instead, many institutions and their leaders seem to have adopted a posture of cautious observation. This posture suggests a view of AI as a technological shift similar to previous ones, which can be primarily addressed through incremental policy updates or technical solutions. This perspective truly misreads the moment.