To be honest, when I first came to Edinburgh, I wasnāt sure if I could really adapt to the way things were taught here. I had heard the word āinterdisciplinaryā beforeāusually in brochures or course descriptions, often paired with words like āintegration,ā āinnovation,ā or āboundary-breaking.ā It sounded impressive, but also vague. It wasnāt until I was thrown into it that I realised how much it actually challenges the way you thinkāand not always in a comfortable way.
Our classes often jumped across topics: social theory, AI language models, speculative design, media ethics. One discussion might start with technological affordances and end with cultural power structures. The people in the room had totally different backgroundsāsome came from computing, others from fine art, journalism, philosophy. When we responded to the same question, it often felt like we were speaking different languages. At first I couldnāt quite follow. I just had this vague discomfort: something feels off here, but I canāt articulate why.
That kind of intellectual discomfort was hard to sit with. I was used to thinking in a certain wayāstructured, linear, always heading toward a clear conclusion. But in interdisciplinary spaces, that mindset quickly feels limited. Youāre forced to listen to unfamiliar frameworks, and sometimes to question your own assumptions. Not to abandon your perspective, but to understand where it beginsāand where it stops being useful.
One turning point for me came during a session on algorithmic bias and data justice. Initially, I approached the topic from a creative industry angleāhow data affects user experience or branding. But after that class, I started to realise how much ādesign decisionsā often reinforce existing hierarchies. For example, using data to evaluate someoneās creditworthiness might sound neutral, but in practice, it can punish already-marginalised groups. Algorithms aren’t neutral. And as creators, designers, communicators, neither are we.
That realisation made me rethink the kind of work we often call āsolutions.ā Are we really solving problems, or just making them more palatable? Are we asking the right questions, or just reshaping the answers to fit a familiar framework? Thatās where I think interdisciplinary learning really mattersānot because it gives you more tools, but because it makes you doubt the tools you thought you understood.
Over time, I started to embrace a different approach: one that prioritises clarity over certainty, questions over answers, and context over speed. We’re not just learning theoriesāweāre learning to interrogate the theories themselves. To ask: what kind of logic is this built on? Who does it leave out?
I donāt think this experience has made me an expert in anything. But I do think itās changed how I pay attentionāto language, to systems, to the way I move through problems. That, for me, is the core value of studying here.
Interdisciplinary learning isnāt about collecting more labels. Itās about learning which labels deserve to be questioned in the first place.
