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.
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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.
Most universities offer their academic staff some form of teaching development. A workshop on active learning. An induction session for new starters. A seminar series that appears in the calendar each year. The intentions behind these programmes are genuine. The evidence that they change teaching practice is, on the whole, thin.
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.
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.
A constant theme in my recent conversations with teachers, faculty teams and educational developers is the challenge of Generative AI (GenAI) and assessment. There's a palpable sense of pressure in these meetings, an anxious search for a definitive "solution." It’s a feeling I’m sure many in higher education will recognise, as institutions everywhere scramble for policy.