As a technical officer in a medical school, I am often reminded that humans are at the centre of the program. From this vantage point, a 2035 in which technology dictates the curriculum and pushes people to the background does not sound realistic. The future I envision for undergraduate medical education in Switzerland is an optimistic one, because it builds on a social practice that already exists.
Today’s students already share summaries, discuss clinical cases and challenge one another in group chats. In 2035, technology will not replace how this knowledge is constructed; rather, it will connect what learners produce. I foresee social spaces hosted by the institution, where learners choose whether or not to share their notes and reflections with their peers. Artificial intelligence acts as a facilitator: it links related reflections and surfaces where they diverge, leaving the discussion, and the judgement, to the students.
What makes this plausible is the context. Medical data is sensitive, Swiss institutions value digital sovereignty, and awareness of commercial lock-in is growing. Models from external providers can be plugged into the space and swapped by the institution at any time, so the knowledge built by the community never depends on a single vendor. Selwyn (2024) reminds us that educational AI remains narrow, confined to one task within pre-defined boundaries. What he frames as a limitation, I treat as a design principle: a facilitator that does one thing, and only that, is what keeps humans in the room. It is also what leaves space for the future that Veletsianos et al. (2024) encourage us to imagine: one centred on justice and hope rather than on technological solutionism. What remains uncertain is whether learners will share enough for these connections to matter.
Transparency statement : AI assistance was used for grammar checking, phrasing, and final language polish.
References
Selwyn, N. (2024). On the limits of artificial intelligence (AI) in education. Nordisk tidsskrift for pedagogikk og kritikk, 10(1). https://doi.org/10.23865/ntpk.v10.6062
Veletsianos, G., Houlden, S., Ross, J., Alhadad, S., & Dickson-Deane, C. (2024). Higher education futures at the intersection of justice, hope, and educational technology. International Journal of Educational Technology in Higher Education, 21, Article 43. https://doi.org/10.1186/s41239-024-00475-0
lorian, I like how you took Selwyn’s (2024) point that AI is narrow and made it a design rule. A tool that does one thing leaves the judgement with the students.
Your swappable-models point matches what I’ve seen in open-source publishing. Software comes and goes, but the content and the history of who contributed what is what a community actually keeps. Who holds that decides who shapes the curriculum.
One question: if students hold back from sharing, is it the privacy of the medical material, or how they’d look next to their peers? Those need different fixes.
Hey Chris,
Thanks for your comment! Your comparison with open-source publishing captures the idea well. I believe institutions are becoming increasingly aware of the advantages of open formats and of letting students export their work, so they can keep building knowledge beyond their diploma.
Regarding your question, I think the two cases indeed call for the different fixes you suggest. For the privacy of medical material, students must form the habit of anonymizing their reflections (more precisely, removing any information that could identify patients), since Swiss law requires it from them anyway. The peer comparison case is the harder one: course instructions should strongly encourage students to share, if not require it, a bit like us in the MALAT program. The most important part is for them to feel safe sharing with their peers, so they experience how these interactions help build knowledge collectively.