What does being 'at' university mean when you're an online student?

I reflected on this for the first time within weeks of starting the MSc Digital Education 'at' the University of Edinburgh. This was more than 4 years ago and since then I've frequently returned to this question, most recently as I took my seat in McEwan Hall last week for winter Graduation.

During Module 1 (Introduction to Digital Education Environments for Learning) I read Being 'at' University: The Social Topologies of Distance Learning. Written by our tutors: Sian Bayne, Michael Gallagher and James Lamb, for the first time I thought more critically about the physical campus and reflected how even as an online student, the campus would be symbolically and materially significant to my learning experience.

It was an intentional choice to be a 'distance' education student, this status defined by my lack of proximity to the physical campus. I was fully aware that my motivations for studying online were very different from students who were more geographically mobile. And even though I opted to learn online I still had to define my relationship with the University: to balance absence with presence, as I committed to years of supported yet independent study. This was not easy after a 30 year gap of being 'at' university and within weeks of starting, I promised myself that if I managed to complete, then I'd make the trip to Edinburgh to visit the physical campus in celebration.

On Graduation day itself I looked forward to meeting students who I'd been on the course with. I began the course at the same time as Sarah Albrecht and Hattie Abretti and we had all travelled to Scotland with partners and family to participate in this university tradition. It was an amazing afternoon, and in her speech Acting Vice-Chancellor Professor Dorothy Miell reminded us of just how special the day was — McEwan Hall hadn't hosted any Graduation ceremonies since March 2020.

The global pandemic has accelerated the digital transformation of education, a future that will remain contested especially whilst we live through the global health crisis. With this as the context, I believe there are two things that we can be certain of: firstly, that we'll see more use of technology for teaching and learning across all education sectors (and including for corporate learning and development) and secondly, there will be more edtech 'unicorns'. And whilst it's an exciting time to be part of this transformation, I also want to ensure that we don't find ourselves stumbling into this future, and that we continue to think intentionally about the ways in which we use technology to enable teaching and learning. As I conclude my studies (for now!) I remain occupied by the questions that have been so central to my studies: firstly, what does being 'at' university mean? And secondly, who benefits as we continue to digitalise the student experience?

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Addendum: AI and learning – don't remove productive friction

August 2026

I wrote that post in the week I graduated, a year before ChatGPT was released. Generative AI had not yet arrived in our everyday lives in the way it has now. Reading it back nearly five years later, the questions prompted by the Covid-19 pandemic about space and place remain relevant, but less urgent when thinking about AI and learning.

Reflecting on my Master's experience, I know I would have used GenAI if it had been available. I studied whilst setting up my own business – I was a 'Weekend Warrior'. One of our first readings was Bayne, Gallagher and Lamb's academic paper on the social topologies of distance students and I read it diligently, but I also found it difficult. And this left me feeling confused.

Had I been able to access a tool to help me summarise the argument, draw out the key concepts, test my understanding and give me questions to reflect on, I would almost certainly have done so. There is a good argument that this would have helped me learn, and I might have reached an understanding of the paper more quickly and been challenged in order to develop a more robust point of view for my first assignment.

At which point, of course, we are getting remarkably close to the things a good tutor does. And that is what makes my reflections interesting. Perhaps then the confusion I experienced wasn't an obstacle standing in the way of the learning? Perhaps the confusion was itself part of the learning?

As an online student I had long periods of time alone with difficult texts to read and concepts to grasp. There were no shortcuts. I had to read, reread, make connections, misunderstand, reconsider and gradually work things out for myself. The process was slow and sometimes frustrating. However, it was also intellectually productive and highly satisfying. So how can we distinguish the friction that gets in the way of learning from the friction through which learning actually happens?

I am not arguing for friction for its own sake. Plenty of what made that programme hard was not always being able to find what I needed on the LMS. That sort of friction adds very little to learning. If AI can help a learner or student navigate a platform, find the right resource or understand what they need to do next, then use it to remove that friction (and frustration).

But some difficulty is learning itself. Working out what a text means. Sitting with an idea you don't immediately understand. Trying to reconcile it with something else you have read. Testing whether you agree. Finding the words to explain why you don't. These are not necessarily problems in the learning experience waiting to be designed away. They are part of the process of learning. Which makes me question some of the claims being made for AI-enabled education.

For anyone building, buying or introducing AI-enabled edtech, we need to be able to say which difficulty we are removing, and why. Are we removing an administrative or operational burden? A barrier to access? Something that is unnecessarily consuming the learner's time? Or are we at risk of removing the intellectual effort that the learning was designed to create?

This distinction matters. And it matters for the learner too as metacognition becomes even more important. If AI is going to sit alongside us as we learn, we need to become better at noticing our own learning: what we understand, where we are struggling and what kind of help we need. Conversely, we also need to embrace any struggle that might actually be really useful.

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Reference

Bayne, S., Gallagher, M.S. and Lamb, J., 2014. Being 'at' university: the social topologies of distance students. Higher Education, 67(5), pp.569–583.