Each spring, the first-choice application numbers give us an early signal about the next cohort of students. They are not the whole story – final admissions depend on capacity, eligibility, selection groups and final choices – but they are a useful indicator of attractiveness and long-term positioning.
SCI: a stable portfolio, with a clear upward signal in two programmes
For SCI, the overall picture is stability – paired with a very clear upward signal in two of our flagship programmes. In the latest application round (first-choice applicants), the SCI programmes show the following snapshot:
- Engineering Physics (Teknisk fysik): 534 → 672
- Vehicle Engineering (Farkostteknik): 287 → 363
- Engineering Mathematics (Teknisk matematik): 288 → 262
- Open Entry (Öppen ingång, SCI): 264 → 248
What stands out is the magnitude of the increases in Engineering Physics and Vehicle Engineering, both around +26% year-on-year. The remaining programmes show the kind of modest year-to-year movement we typically see in application cycles. Taken together, this points to a stable demand for SCI’s educational portfolio, with particularly strong momentum in programmes that offer a broad engineering foundation and multiple pathways for specialization.
One possible interpretation (offered cautiously!) is that broad programmes with flexible pathways are becoming even more attractive. In a labour market where AI is changing tasks and skills, many students may prefer degrees that keep options open: strong fundamentals, flexibility in specialization, and room to connect theory with emerging technologies over the course of the programme.
Are young people changing their study choices because of AI?
A common question right now is whether the rapid spread of AI is already reshaping how young people choose what to study.The national numbers suggest that interest in engineering is not weakening. UKÄ reports that civilingenjör programmes had 16,500 eligible first-choice applicants for autumn 2025, an 8% increase compared with autumn 2024. UHR’s admissions statistics also show broad continued demand: for autumn 2026, applications increased to both courses (+2%) and programmes (+5%).At the same time, it is clear that many students expect AI to affect their future working lives. In an OECD survey on experiences with generative AI, over 75% of respondents under 35 describe AI as useful, and many in the same age group expect AI to have a substantial impact on their careers. This matches what we hear informally as well: students increasingly ask not only “what am I interested in?” but also “what will still matter in five to ten years?”
The labour-market outlook from major international analyses points to restructuring rather than a simple job-loss narrative. The World Economic Forum’s Future of Jobs Report 2025 projects 170 million jobs created and 92 million displaced globally over 2025–2030, a net increase of 78 million jobs, while emphasizing that skills needs will shift rapidly. The European Commission similarly notes that AI and emerging digital technologies are expected to bring productivity gains and can increase employment overall, but with uneven effects and clear needs for reskilling.In that context, SCI’s stability, and the strong increase in programmes with broad foundations, fits a plausible pattern: students are not avoiding engineering because of AI. Instead, many appear to be seeking programmes that combine rigorous fundamentals with adaptability.
Numbers are not a goal in themselves. The real question is what we do with the opportunity.As we prepare for the incoming students of 2027, SCI will continue to strengthen educational quality and student experience, by focusing on two forward-looking developments: • AI in education: building the right competence among faculty and supporting students in using AI responsibly and effectively, in ways that deepen learning and uphold academic integrity. • Makerspace integration: strengthening hands-on, prototype-oriented learning environments where students can meet across programmes and connect with researchers, bringing theory closer to experimentation, iteration, and engineering practice.
