Almost every week, I come across a new initiative on artificial intelligence in research and higher education. A declaration, a set of guidelines, a working group, a conference, or recommendations on teaching, research and assessment. International organisations, national agencies, universities and departments are all trying to respond to the rapid development of AI.
Many of these efforts are valuable, but I must admit that I sometimes find the sheer volume overwhelming. Are we collectively making things clearer, or are we adding to the confusion?

As a dean, I am very much part of this process. I participate in discussions, support new initiatives and contribute to decisions about how our university should respond to AI. So the question is also directed at myself: are we, as university leaders, sometimes adding to the uncertainty rather than reducing it?

There is broad agreement on many of the challenges: AI is transforming education and research, students need AI literacy, assessment methods must evolve, and academic integrity remains essential. But translating these principles into everyday academic practice is considerably more difficult than agreeing on them.
Different courses and research environments will reasonably adopt different approaches to AI. A course teaching the foundations of AI may even require students to solve problems without AI assistance. There is nothing contradictory about that, provided the expectations and their rationale are clear. What matters is not uniformity, but a coherent framework within which students and teachers can navigate these differences.
Such a framework should not have to be developed independently by every university. Students and researchers move between institutions, and many of the questions we face are shared. Yet universities and national agencies continue to develop their own guidance in parallel. Rather than multiplying frameworks, shouldn’t we work towards a common one, developed collectively, while leaving room for disciplinary and local differences?

There is another aspect that deserves attention: stability.
AI technology is developing at extraordinary speed. But must our educational rules and academic expectations change at the same pace? I would argue that they should not. Students need clear expectations throughout their education. Doctoral students and junior faculty need guidance and stability as they develop their academic independence. Teachers need time to adapt their methods and evaluate what works. A continuous flow of new recommendations risks creating uncertainty precisely where we should be providing direction.
This does not mean resisting change. We should distinguish between principles that remain stable and practices that evolve as we learn more. We should continue experimenting and learning, while giving ourselves time to implement existing decisions and evaluate what actually works.

For our part at SCI, we will prioritize building on existing initiatives, developing our guidance in dialogue with others, and giving students and colleagues the time and stability they need to put it into practice. Follow us at https://intra.kth.se/en/sci/ai-for-utbildning.

And yes, I realise the irony of adding yet another text about AI to the already overwhelming collection. Perhaps I should take my own advice.

What should students expect from a university in the age of AI?

Artificial intelligence is forcing universities to confront some fundamental questions. If information, explanations and even personalised tutoring are available anytime and anywhere, what should students expect when they come to a university? What remains uniquely valuable about spending years on a campus?

I do not believe the answer lies in access to information, nor in the content we teach. Knowledge will remain central to what we do, but it is no longer our most distinctive contribution. Universities will increasingly be defined by the experiences they create and the communities they sustain.

Students should expect to be challenged intellectually: to encounter ideas that are unfamiliar, demanding and sometimes uncomfortable, and to learn how to analyse, question and evaluate rather than simply consume. They should develop judgement, the ability to decide what matters, what can be trusted and what should be done when there is no obvious answer. They should also expect to work with other people, especially those who think differently from themselves. Some of the most important lessons in higher education emerge from discussing a difficult problem with peers, collaborating across disciplinary boundaries, or attempting something ambitious and discovering that it does not work as planned.

This is why I believe the physical campus is becoming more important, not less. It may sound paradoxical when digital tools are becoming ever more powerful, but precisely because so much can be accessed remotely, the value of being physically together increases. Universities are among the few places where students can work closely with researchers, meet people with different backgrounds and perspectives, and belong to a community united by curiosity and a desire to understand the world.

This is one reason why I am excited about SCI MakerSpace. A makerspace lets students move between theory and practice. An idea can begin as a mathematical model, a simulation or a design sketch; it can then be built, tested and improved. Unexpected results become the starting point for deeper understanding. Theory informs practice, and practice challenges theory.

That interaction has always mattered in engineering education, but it is becoming valuable across many disciplines. In a world where technology provides answers instantly, students need opportunities to formulate questions, exercise creativity and develop the confidence to tackle problems that come without predefined solutions. The challenge for universities is therefore not simply to adapt to AI, but to rethink which learning experiences matter most when information is abundant.

My hope is that our students will graduate not only with knowledge and technical expertise, but with curiosity, initiative, judgement and the ability to work constructively with others. Those qualities will remain valuable however technology evolves.
The larger challenge, and opportunity, is perhaps to keep reimagining what a university education can be in the decades ahead.

During my first winter in Stockholm, some friends came to visit, and we decided to take a dinner cruise in the archipelago, yes in December! Quite by chance, we discovered that the entire family of that year’s Nobel laureate in Chemistry was sitting at the next table. It was many years ago, but it is a memory I will never forget.
With this week’s Nobel announcements, I find myself thinking about our students, particularly those experiencing their first autumn in Stockholm. Alongside finding their way around campus, getting to know classmates and keeping up with assignments, there is a whole Nobel season to discover. Through Nobel Calling, the city opens its doors to talks, laboratory visits and other events. What a lovely addition to the experience of studying here.

At KTH, the programme includes open lectures and opportunities to step inside Digital Futures. There is even a photo booth at KTH Entré where students and colleagues can have their picture taken as though they were receiving a Nobel Prize. A slightly quicker route than the usual one! I like that there is room for a little playfulness alongside the serious science. Nobel Week Stockholm at KTH

And the announcements are only the beginning. One of the things to look forward to in December is the tradition of public Nobel lectures at Aula Magna. There is something special about being able to hear directly from the laureates, rather than only reading about their discoveries. For our students, this is happening practically on their doorstep. Stockholm University
There is plenty to enjoy outside the lecture hall, too. Nobel Week Lights returns on 5–13 December, with free light installations inspired by Nobel Prize achievements around the city. It is another way to take part in the celebrations, and a welcome addition to the December darkness.

Students also help create the celebrations. The Students’ Nobel NightCap, the party following the Nobel Banquet, is organised by hundreds of student volunteers. This year, it is hosted by the student association at the Stockholm School of Economics. Behind the formal occasion is a very student-like undertaking: planning, building, keeping a theme secret and making a memorable night happen together.

For those of us who have lived and worked here for many years, it is easy to forget how unusual all this might feel to someone newly arrived.
I hope our students find their own memorable moments in these weeks and months.

Today the OECD released the results of PISA 2025. They make uncomfortable reading. The results point to a broader challenge for education systems, including Sweden, and raise questions about whether young people are developing the fundamental competencies in mathematics, reading and science that they will need in a rapidly changing world.

At the same time, another transformation is taking place at extraordinary speed. AI has already become part of how young people learn and how we work at universities.

I think we need to consider these two developments together.

Much of the discussion about AI in universities has concerned what AI can do. How can it improve teaching? How should students be allowed to use it? Can it accelerate research? These are important questions, and at SCI we have been working actively with them in our guidance for AI in education. But there is another question that I increasingly find more fundamental:

What does it mean to become scientifically competent in the AI generation?

A student can ask AI to explain a theorem, solve an equation, write code or summarise a scientific paper. A doctoral student can use it to search literature, suggest hypotheses, analyse data or propose steps in a proof. Researchers are already experimenting with systems that automate increasingly large parts of the scientific process.  This is an extraordinary opportunity for science. But it creates a paradox:

The easier it becomes to produce an answer, the more important it becomes to know how to judge the answer.

We learn to become researchers partly through struggle. A doctoral student searches the literature, writes code that does not work, follows an argument before finding the mistake, writes and rewrites, and discusses half-formed ideas with a supervisor. AI can remove much of this struggle, and some of it we should be delighted to remove. Science has always progressed by developing tools that allow us to stop doing things that machines can do better.

But some of this struggle may also be where scientific judgement is formed.

This concern is beginning to appear explicitly in the scientific discussion: AI may accelerate scientific production while narrowing scientific inquiry and affecting how expertise and judgement develop ( Nature comment, “The uncritical adoption of AI in science is alarming — we urgently need guard rails“)

This has consequences for both doctoral education and the role of the professor. If AI can perform some of the activities through which we traditionally learned to become researchers, we need to understand what can safely be delegated and what is essential to developing an independent scientist.

At the same time, AI is narrowing the traditional gap in access to knowledge and technical expertise between professor and doctoral student. What experience provides, however, may be something harder to reproduce. Which of ten plausible directions is worth pursuing? When should we abandon an approach? What result would actually change our understanding? When is an unexpected result a mistake, and when is it the beginning of something interesting?

Perhaps the professor of the AI generation will become somewhat less important as a provider of answers and even more important in helping young researchers learn how to ask questions and exercise judgement.

And this brings me back to PISA 2025. The students who will enter KTH in a few years are growing up in an environment where access to sophisticated answers is becoming almost unlimited, while we are seeing worrying signals about fundamental competencies in education.

The response cannot be to protect students from AI, nor simply to teach them to use it more efficiently. Generative AI can support learning, but should enrich it rather than replace cognitive effort or teachers’ professional judgement (The OECD Digital Education Outlook 2026)

Our responsibility as educators is therefore larger: it involves fostering curiosity, persistence and the confidence to question what a machine or, for that matter, a professor tells you. These are skills that have always been part of education, but have perhaps never been as fundamental as they are today.

The AI generation will need more scientific competence, not less.

Our challenge as universities is to make sure they acquire it.

Text developed in collaboration with AI.

How do we develop education, research environments and academic culture in a time of rapid technological and societal change? At the School of Engineering Sciences, SCI, we are working actively with three focus areas that are central to this question: AI in education, hands-on and project-based learning through SCI MakerSpace, and inclusion as a foundation for a strong academic environment.

To make this work more visible and easier to follow, we have now published three dedicated pages:

• AI for education
• SCI MakerSpace
• Inkludering@SCI

These pages gather information about ongoing activities, workshops, resources and contact points. They are meant to be living entry points into work that will continue to develop together with faculty, staff and students.
The three areas are different, but closely connected. AI is already changing the conditions for learning, teaching, examination, administration and research support. MakerSpace will create new opportunities for students and teachers to design, build, test and develop ideas in a practical and creative environment. Inclusion concerns the academic culture that makes it possible for people to contribute, collaborate and grow.

A central part of our approach is direct involvement. These focus areas are not being developed only through strategy documents or central decisions, but through workshops, discussions, specialized meetings and concrete activities involving faculty, staff and students. Lasting change in education and academic culture requires shared understanding, local engagement and opportunities for colleagues to bring in their expertise and experience.

A recent example was the full-day Agentathon workshop on AI agents, in which I also took part. It was an impressive source of inspiration, and a very tangible sign of both the opportunities and the risks connected to AI. We saw how AI tools can make many daily tasks faster, more effective and more structured. At the same time, the workshop raised serious questions about security, responsibility, data handling and the need for critical judgement.
This balance is important. Curiosity and experimentation are necessary if we want to understand how new tools can strengthen our work. But they must go together with awareness of risks, ethical considerations and sound academic judgement.

The publication of these pages is therefore not an endpoint, but an invitation to follow, contribute to and discuss the work as it develops. Each of these focus areas touches questions that are central to the future of universities: how we use new technologies responsibly, how we create environments for creative and practical learning, and how we build academic cultures where people can contribute fully. By making the work visible and by developing it together with faculty, staff and students, we strengthen not only individual initiatives, but also the conditions for education, research and collaboration at SCI.