Digital Garden
A collection of growing thoughts on higher education, edtech, and the future of learning
Slowly but steadily it is happening
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.
Cultivating Shared Vision in an Era of Radical Complexity
Step into the lobby of almost any university, and you will likely find a mission statement etched onto the glass façade. It usually speaks of "excellence", "innovation", and "global citizenship". Yet, a mere few hundred metres away in a lecture hall, the reality often feels worlds apart from those lofty aspirations.
Leading, Not Managing
The landscape of higher education is increasingly defined by complexity. As institutions navigate financial pressures, technological disruption, and shifting student demographics, the nature of academic leadership is being actively renegotiated. While strategic plans frequently emphasise "transformation" and "agility", the operational reality often reveals a different trajectory: one characterised by intensified management and data-driven oversight.
How I actually used GenAI in 2025
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.
Fixing the Broken Bridge of Educational Innovation
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.
Building Capacity for Educational Excellence: The Role of Digital Learning Support
Conversations about technology in higher education often swing between two extremes. On one side, there is immense optimism that digital tools will revolutionise teaching. On the other, there is a wary scepticism about whether technology adds any real value to the student experience. A more balanced view suggests the reality is far more nuanced: the success of digital learning is inextricably linked to the human element of education.
AI Without the Handover: Managing Research with Model Context Protocol
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.
The Struggle We Don’t Name: AI and the Grief of Professional Identity
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.
The Peril of Pedagogical & Strategic Paralysis
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.
Building my sixth tool for qualitative analysis with Generative AI
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.