AI

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 The initial, acute panic over plagiarism has faded, but it hasn't been replaced by a unified, urgent call for redesign.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.

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.

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.

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.

The discussion about AI in education often gets bogged down in practical questions: are you allowed to use it, and how do we then ensure ownership and reliability? While relevant, these questions stem from the outdated idea that technology is a neutral tool, separate from the learning process itself.

Generative AI is no longer a futuristic dream; it is here, and it is transforming higher education as we know it. From personalised learning experiences to intelligent tutoring systems, AI is revolutionising the teaching and learning landscape, presenting both opportunities and challenges. For anyone involved in higher education, understanding these changes and adapting to them is crucial.