I’m studying for an MSc in Architectural Computation at the Bartlett, UCL – essentially computer science for architects, with a focus on geometry, simulation and computer graphics. I’m very grateful for this question, because it gives me a chance to synthesise the ideas I’ve had since I started the programme.
Even though our professors are getting worried, the institution itself hasn’t changed dramatically yet when it comes to generative AI. There is an openness from our professor to discuss the matter, but change is slow.
What does work in the current programme —and in my oppinion exactly what we need for next generations— is that we are exposed to an astonishing number of techniques and are given the freedom to interpret and implement them. The only drawback is that some students simply paste LLM outputs as their scripts, while others spend time digging deeper into the methods to gain finer control over the models. This inevitably creates a large discrepancy in skill levels later on and can damage the institution’s reputation by producing a highly non‑homogeneous cohort.
I think the way forward is to develop a solid understanding of the architecture behind each technique, be able to write clear pseudocode, and prototype quickly. Being able to anticipate what goes in and what comes out has never been more important. Writing modular, well‑segmented code is also crucial for maintainability. In my view, “vibe‑coding” is only a phase; eventually students will hit a wall and will need to dig into the fundamentals. The question is can we make them hit the wall during the studies or will that happen later in their career.
In my opinion, and the way I would love to be taught, would be to start with a complex piece of code and try to reverse‑engineer it: trace the data flow, map out the algorithm on paper, and then rebuild it step by step. This forces you to understand both the theory and the implementation, rather than relying on copy‑and‑paste shortcuts.
Hope that is of any use out there, and again, I think there is no time less exciting (and easy!) than this one to climb on the shoulders of giants.