This week, I attended the last session in a series on valorisation at Uppsala University, titled “DoResearchwithImpactinMind”. This seminar series turned out to be a very inspiring event and brought lots of ideas on how research can contribute to society, not just through publications but by becoming part of real-world solutions, services, and policies. The seminar series was organized by UU Innovation, a support function at Uppsala University that offers guidance and support for researchers to explore potential ways for their research to achieve societal impact.
The seminar focused on valorisation, described as the process of translating research and knowledge into societal or economic value. This could mean anything from influencing policy and public health to developing new technologies, services, or educational approaches. Valorisation is not just about commercialization but about recognising the broader potential of research to shape the society around us. This definitely broadened my initial view of the types of research that could create societal impact.
To me, one of the most interesting points from the seminar series was the “professor’s privilege,” which means that teachers at Swedish universities own the rights to their research results. Despite the name, this applies not only to professors but to all researchers at the university and means that we have the possibility to choose if, how, and when our results might be used outside academia. That is a powerful opportunity, but also a responsibility.
The key takeaways from the seminars were to plan for impact early in the research process and that societal impact can take many shapes and forms. Overall, it was an inspiring event that made me reflect on the broader potential of research and how impact can (and perhaps should) be part of our research process from the very beginning.
Under pandemin blev videomöten en naturlig del av vården. Men nu, när det digitala mötet inte längre är ett måste, väcks en ny fråga: vill patienterna fortsätta använda videokonsultationer även på längre sikt?
Detta är kärnfrågan i en studie som jag, Åsa Cajander, varit med och författat tillsammans med Irene Muli, Helena Hvitfeldt, Lovisa Jäderlund Hagstedt, Nadia Davoody, Marina Taloyan och Maria Hägglund. Vi undersöker vilka faktorer som påverkar långsiktig användning av videomöten i primärvården – och varför vissa patienter väljer att sluta använda dem.
Majoriteten vill fortsätta – men inte alla
Av de 451 patienter som deltog i vår undersökning uppgav 76 % att de gärna skulle vilja ha fler videomöten i framtiden. Men 24 % var mer tveksamma eller helt emot det. Vad avgör vem som vill fortsätta och vem som inte vill?
Vi såg att de som vill fortsätta oftare är i åldern 35–54 år, arbetar heltid och har en positiv inställning till tekniken. De använder även videomöten i andra sammanhang – till exempel i jobbet – och ser vårdens videomöten som en bekväm lösning när tiden är knapp.
Vad får människor att sluta?
De som inte vill fortsätta med videokonsultationer angav i första hand att de helt enkelt föredrar att träffa vårdpersonal ansikte mot ansikte. Detta gällde särskilt den yngsta åldersgruppen (16–34 år) – en något oväntad slutsats som pekar på att digitala vanor inte automatiskt innebär att man föredrar digital vård.
Andra skäl var att möten via video kändes mindre personliga eller att det var svårt att förstå vårdpersonalen. Intressant nog nämndes tekniska problem och användbarhet i mindre utsträckning än väntat.
En fortsatt digital klyfta?
Studien lyfter en oroande möjlighet: att både de yngsta och äldsta grupperna i befolkningen riskerar att halka efter i den digitala vårdutvecklingen. Vi pekar på att detta kan förstärka den redan existerande digitala klyftan. Erfarenhet, positiva attityder och känslan av att tekniken är frivillig verkar spela stor roll för om man vill fortsätta använda videomöten.
Vad betyder det här för framtidens vård?
Resultaten från vår studie visar att det inte räcker med att erbjuda tekniska lösningar – det krävs också förståelse för hur människor faktiskt upplever dem. Att skapa smidiga, användbara och personliga upplevelser är avgörande om videomöten ska bli en långsiktig del av vården.
Och kanske är det just där framtidens utmaning ligger: att hitta rätt balans mellan teknikens möjligheter och människors behov av närhet, förståelse och valfrihet.
When we hear the word “handover,” we might think of a nurse passing on information at the end of a shift, or maybe a car switching from self-driving mode back to the driver. If you think more closely about it, you will see that handovers are everywhere around us — and that they are quietly shaping how we work, collaborate, and share responsibility with both people and technologies. In a recent publication, we discuss handovers with the goal to broaden how we think about them in the field of Human-Computer Interaction (HCI)—especially now that Generative AI (GenAI) is entering many workplaces. The paper is co-authored by Ece Üreten (University of Oulu), Rebecca Cort (Uppsala University), and Torkil Clemmensen (Copenhagen Business School).
Traditionally, HCI research has looked at handovers as the moment when control of a system, like a semi-autonomous car or a VR headset, passes from one person to another or from a machine to a human. However, handovers are more than just “passing the baton.” In today’s digital workplaces where AI, automation, and human collaboration blend, handovers are crucial moments of communication, coordination, and shared understanding. Accordingly, in this publication, we argue that handovers are more than technical events and should be viewed as complex socio-technical interactions involving people, technology, organizational structures, and tasks.
With GenAI tools becoming more widespread, the rules of handovers are changing. These tools can summarize data, generate content, and even adapt to different communication styles—all of which could make handovers smoother and more reliable. But GenAI also raises new questions such as: How should we design AI tools that understand the context of a handover? Can AI support empathy and human connection in teamwork? How do we make sure AI does not confuse, overwhelm, or mislead during critical transitions? To tackle these questions, we propose a research agenda focused on the following key dimensions: 1. Technology – What tools and formats make handovers effective? 2. Tasks – What exactly is being handed over, and under what conditions? 3. Actors – Who is involved? (It is not just humans anymore.) 4. Structure – How are handovers shaped by organizational rules and culture? 5. Cross-domain – How can we understand the distinctions of handovers across domains?
In this paper, we emphasize that handovers are not the same across domains and therefore, we call for cross-domain studies and more nuanced thinking about who (or what) is involved in these critical moment of communication, coordination, and shared understanding. The paper invites the HCI community to take handovers seriously, study them across different industries, and design future technologies with this human (and increasingly non-human) interaction in mind.
“Something is rotten in the state of … ” well, at least in Sweden, and at least when it comes to software systems. There are a large number of failures adding up over the years. I can understand the problem we had in the 1960s and the 1970s, and I can even understand the Y2K phenomenon (which actually seems to fade in even computer scientists’ collective memory). Before the year 2000 we didn’t understand that the software was going to be so well designed (!) that it would survive into a year, when the year “00” was not an error message, but an actual number.
However, if we go back through the very short history of computing, we find that there were a large number of failures already from the beginning. Not only with computers, but the machines that were connected to the computers. Just take the example of the first ATM machines that were introduced. For some reason people seemed to become very absent-minded all of a sudden. They started to leave their banking cards in the machine over and over again. When this issue was investigated more thoroughly, it became clear that it was necessary to change the order of two actions in order to make the system better in this respect. Instead of “get the money first” and “retrieve card after that”, the trick was to “retrieve the card first” and “then get the money”. As simple as that, right.
Now this is no longer a problem, since we have this “touch” authorisation of everything you do. Just touch your card or even the phone to a small sensor and everything is fine. But just before this became more common, there was a small period of time when the ticket machines for the car parking were exchanged to a new, safer model. Apparently, it was now a requirement from the EU that the card should remain inserted during the whole operation (it was in fact held there by some special mechanism). Guess what happened? A large number of people became amnesiac again, leaving their cards in the slot. But this was not a surprise to some of us. No, we knew that this were to happen. And of course there was a good reason for this problem to reoccur — THE GOAL of using THE MACHINE!
When you go to an ATM or go to a ticket machine for a car park, you have a goal with this – namely to get money, or pay the parking fee. The card we use to pay is not a central part (unless the account is empty or the machine is broken) of the task, it is not as important as getting the money, or getting the parking paid. We KNOW this since the 1970s. But today, it seems, it’s not the users who suffer from bad memory; it’s the software developers turn.
Today we know quite a lot about human factors. Human factors are not bad, on the contrary. We know when they can cause a problem, but we also have quite good knowledge about how we can use the human factors constructively, so that they help people to do the right thing more or less by default. This does not mean that there is a natural, or obvious way to do something, but if we take the human factors into considerations, we can in fact predict if something is going to work or not.
This sounds simple, of course, but the problem is not just to change the order of two actions. It means to understand what we are good at, and also when the human factors can lead us the wrong way.
But how can we know all this? By looking back!
It is by looking back on all the bad examples that we should be able to start to figure out what can go wrong. And the list of bad (failed) examples has not grown shorter over the years, even after the Y2K chaos (which in fact should be called the “centennium bug” rather than the “millenium bug”). In the late winter last year we had a new version of the millium bug (or a similar event, at least). Two of the major grocery chains in Sweden (and one in New Zeeland) could not get their cashiers to work. The system was not working at all. Since the Swedish government has stressed “the cashless society” so hard, it had the effect that a large number of customers was unable to do the shopping that day.
So, what was the problem? Well, from the millenium bug we know that years can be very tricky entities. They should have four numbers, otherwise there can be problems. So far so good! However, the developer of this financial system didn’t make a proper estimation of the importance of the years in the system. It turned out, in the end, that the day when the system stopped working, was a day that didn’t exist. At least it did not exist in the minds of the developers, when they designed the system. But now and then, February 29 does indeed exist. Almost (every) every 4th is a year leap year, where February has an extra day attached.
The question is how this kind of bugs can enter the development process? The thing is that we can study a large number of single anecdotical accounts without drawing any wider conclusions from the examples. But if we instead look at the failures from a human factors perspective, there are many conclusions that we can draw. Most of these are very easy to understand, but oh, so difficult they seem to be to actually make anything out of. In part 2 of this post, I will dive deeper into some of the anecdotal examples, and make an attempt to generalize the reasoning for future references. (to be continued…).
In our recent paper From Assistance to Misconduct: Unpacking the Complex Role of Generative AI in Student Learning (published at the 2024 IEEE Frontiers in Education Conference), we explore how computing students use tools like ChatGPT—and where they draw the line between help and cheating. The paper is co-authored by Andreas Axelsson, Åsa Cajander, Daniel Tomas Wallgren, Mats Daniels, Udit Verma, Anna Eckerdal (Uppsala University), and Roger McDermott (Robert Gordon University, UK).
Based on interviews with nine students, we found that GenAI is deeply integrated into their workflows: for debugging, quizzing themselves before exams, and even overcoming motivational dips. One student noted they’d started consistently getting top grades thanks to AI-assisted study strategies.
What stood out most was the students’ own ethical reasoning. Most agreed that copy-pasting AI outputs felt like misconduct—but using GenAI for inspiration, feedback, or clarification was generally seen as acceptable. Still, the boundaries were blurry and often context-dependent.
The study raises questions not just about tools, but about pedagogy and policy. As educators, we need to better support students in navigating this grey zone—through clearer guidance and learning designs that promote reflection, not just results.
Nyligen samlades TARA-projektets referensgrupp för att diskutera nästa steg i arbetet med att kartlägga och förbättra arbetsmiljön för flygplatspersonal i samband med införandet av ny teknik. Mötet bjöd på många värdefulla insikter, särskilt kring projektets slutprodukt, enkätdesign och hur vi bäst fångar olika dimensioner av arbetsmiljön.
TARA-projektet fokuserar på att öka kunskapen om hur nya tekniker – exempelvis automation, robotisering och AI – påverkar arbetsmiljön för lastare, flygplatstekniker och tankningspersonal på Sveriges flygplatser. Projektet bedrivs i nära samarbete med TYA (Transportfackens Yrkes- och Arbetsmiljönämnd) inom ramen för ett aktionsforskningsupplägg, där forskare och praktik möts för att tillsammans skapa ny kunskap och lösningar. Genom fältstudier, intervjuer och en enkätstudie syftar projektet till att både förstå nuläget och skapa stöd för framtida implementeringar av teknik, med arbetsmiljö i fokus. Projektet är finansierat av AFA.
Vid vårens referensgruppsmöte diskuterades bland annat den framtida slutprodukten – en möjlig utbildning eller checklista som stöd vid teknikinförande – samt enkätens utformning. Deltagarna betonade vikten av att anpassa materialet till olika roller inom organisationen, och att både teknikanvändningens konsekvenser och införandeprocessen behöver belysas. Diskussionen visade också tydligt att arbetsmiljön bör betraktas ur flera perspektiv: fysiskt, psykosocialt, kognitivt, organisatoriskt, digitalt och emotionellt.
Vi fick också många konkreta tips kring exempelvis hur teknikanvändning kan kategoriseras i enkäten och vad som kan underlätta en hög svarsfrekvens.
Tidigare i veckan presenterade Sofia Ouhbi pågående forskning inom projektet EDU-AI (vilken bedrivs tillsammans med Niklas Humble, Åsa Cajander, Mats Daniels och Lars-Åke Nordén) vid Teknisk-naturvetenskapliga fakultetens Universitetspedagogiska Konferens (TUK 2025) vid Uppsala universitet.
Presentation bygger på en kommande rapport från projektet där pågående forskning om generativ AI i den professionella IT-världen och dess implikationer för högre utbildning sammanfattas och diskuteras.
EDU-AI-projektet (Adapting Computing Education for an AI-Driven Future: Empirical Insights and Guidelines for Integrating Generative AI into Curriculum and Practice), syftar till att undersöka hur generativ AI används av IT-professionella och hur denna teknik påverkar högre utbildning.
Presentation, och rapporten, lyfter följande fynd från den pågående forskningen:
Adoptionsmönster: Generativ AI adopteras i olika takt inom olika sektorer. IT-professionella ligger ofta i framkant och använder AI för att lösa komplexa problem och förbättra effektiviteten.
Fördelar med generativ AI: Fördelarna med generativ AI är många. Den ökar produktiviteten, förbättrar lärande-upplevelser och uppmuntrar till innovativa tillvägagångssätt för problemlösning.
Utmaningar och etiska överväganden: Trots sina fördelar medför generativ AI också utmaningar, inklusive etiska frågor, data-bias och risken för missbruk. Att hantera dessa frågor är avgörande för en ansvarsfull användning av AI-teknologier.
Framtida AI-kompetenskrav: När arbetsmarknaden utvecklas kommer det att finnas ett växande behov av AI-kompetenser. Att förbereda studenter för denna framtid är ett centralt mål för projektet.
Sammanfattningsvis belyser EDU-AI-projektet den transformativa potentialen hos generativ AI inom både professionell IT och högre utbildning. Den fortsatta forskningen i projektet kommer att utforska studenters syn på och användning av AI-teknologier, samt vilka metoder lärare använder för att integrera tekniken i högre utbildning.
Om du har frågor eller vill veta mer, kontakta niklas.humble@it.uu.se
This year, the HTO research group has launched a seminar series on funding applications, led by Åsa Cajander, Sofia Ouhbi, and Jessica Lindblom. This initiative emerged from our annual strategic meeting, where we discuss the research group’s focus, future goals, targeted conferences, collaborations, and social events. A common theme that surfaced during these discussions was the need to strengthen our grant-writing skills, leading to the creation of this seminar series.
The series consists of six interactive seminars, where participants analyze both successful and unsuccessful funding applications. Our goal is to understand what makes an application stand out, recognize potential pitfalls, and refine our own grant-writing strategies. These sessions are designed to be inclusive and beneficial for everyone, from PhD students and postdocs to senior researchers, docents, and professors. Regardless of prior success in securing grants, there is always something new to learn. By engaging in discussions and sharing experiences, we aim to build a stronger, more confident research community equipped to navigate the competitive world of funding applications.
The seminar was attended by over 20 participants, including PhD students, postdocs, researchers, docents, and senior professors. To foster engagement, we began with an icebreaker session, where participants introduced themselves and shared their experiences with funding applications.
Led by Prof. Cajander, the session provided a comparative analysis of different funding bodies, focusing on:
Their core priorities and evaluation criteria
The types of projects they tyoically fund
Key factors that make applications successful
In addition to major grants, we also discussed travel grants for doctoral students and funding opportunities available at the university and national levels.
This was followed by an interactive discussion where attendees reflected on key takeaways. The session provided valuable insights into how to tailor applications for different funding bodies and maximize the chances of success.
Academic Credit Opportunity for PhD Students
PhD students have the possibility to earn academic credits if they attend at least five out of six seminars and actively participate in discussions. This is in discussion with their PhD supervisor. This ensures that participants engage deeply with the material while gaining practical skills in grant writing.
Second Seminar on 25 February
In our second seminar, we explored the application process for FORTE funding by analyzing four real-life applications—three successful and one unsuccessful—along with their corresponding reviews. The session was divided into two parts: an informative presentation followed by group discussions.
Understanding FORTE’s Funding Landscape
The first half of the seminar featured a presentation by Sofia Ouhbi, who outlined the key focus areas of FORTE, the different types of calls, and the various application formats available. She also provided an analysis of the common characteristics of successful applications, offering valuable insights into what FORTE typically prioritizes in its funding decisions.
We were also joined by Magdalena Stadin, a postdoctoral researcher in human-computer interaction and a recipient of FORTE’s starting grant for postdocs. Having contributed to multiple successful FORTE applications, Magdalena shared her firsthand experience of the application process. She provided practical advice on approaching FORTE applications, highlighted key strategies, and emphasized the importance of persistence for early-career researchers.
Group Discussions and Reflections
In the second half of the seminar, Prof. Cajander, a FORTE board member, discussed the organization’s future plans and funding directions. Following this, participants were divided into three groups—doctoral students, postdocs, and senior researchers—to critically analyze the previously shared applications.
Each group examined the strengths and weaknesses of both successful and unsuccessful applications, identifying key takeaways and strategies for improvement. The discussions centered around best practices, common pitfalls to avoid, and essential elements to include in a strong FORTE application.
To conclude the seminar, the groups presented their reflections, sharing insights on how to craft more competitive funding applications. The session was facilitated by Prof. Cajander, ensuring a productive and engaging discussion. Overall, this seminar provided a deeper understanding of FORTE’s application process and valuable peer learning opportunities.
The key takeaway? Successful funding applications require a good idea, strategy, persistence, a good team, and a strong understanding of what the funding body values.
What’s Next?
On 25 March, we look forward to the next seminar which will focus on The Swedish Research Council (Vetenskapsrådet, VR). We meet at Theatrum Visuale at Ångström. Please write to Prof. Cajander if you wish to join.
Teknikutvecklingen går snabbt framåt, och flygbranschen är inget undantag. Automatisering, AI och digitala verktyg förändrar arbetsmiljön för markpersonal på flygplatser. Det AFA-finansierade TARA-projektet (Teknik, Arbetsmiljö och Resiliens i Arbetslivet) undersöker hur dessa nya tekniker påverkar arbetsmiljön för lastare, flygplatstekniker och tankningspersonal. Projektet drivs av forskare från Uppsala universitet i samarbete med branschorganisationen TYA (Transportfackens Yrkes- och Arbetsmiljönämnd) och syftar till att identifiera både möjligheter och risker med digitaliseringen.
En viktig del av projektet är att ta fram konkreta rekommendationer för en säker och hållbar implementering av ny teknik. Jonathan Källbäcker har sammanställt en rad praktiska råd som nu finns publicerade på TYAs hemsida (länk här). Dessa riktar sig till flygplatschefer, operativa chefer och personalansvariga samt till fackligt engagerade och skyddsombud.
Rekommendationer för en bättre arbetsmiljö vid införande av ny teknik
För dig som är flygplatschef
Prioritera arbetsmiljö som en central del vid införandet av ny teknik – både fysisk, organisatorisk och social arbetsmiljö.
Dela erfarenheter mellan flygplatser för att lära av varandra.
Kommunicera vikten av en god arbetsmiljö till alla involverade och se till att de förstår dess betydelse.
Involvera och engagera medarbetarna i processen samt planera för relevant utbildning.
Säkerställ att tekniken är användarvänlig genom att låta markpersonalen vara delaktiga i designprocessen.
För dig som arbetar som operativt ansvarig, personalansvarig eller chef för tekniska avdelningar
Håll dig uppdaterad om ny teknik och regelverk och informera markpersonalen kontinuerligt.
Undersök noga hur tekniken används i praktiken och identifiera eventuella behov av anpassningar.
Följ upp hur tekniken påverkar arbetsuppgifter och arbetsbelastning.
Avsätt tid under arbetspassen för personalen att testa och utvärdera tekniken.
För dig som är fackligt engagerad eller skyddsombud
Se till att arbetstagarna får möjlighet att delta i tester av ny teknik.
Håll dig informerad om tekniska innovationer och nya arbetsmiljökrav.
Identifiera när och var det går att ställa arbetsmiljökrav under införandeprocessen.
För en tydlig dialog med chefer, utvecklare och externa aktörer om personalens behov.
Påpeka vikten av att avsätta tid för säker och effektiv teknikanvändning.
Vikten av en genomtänkt implementering
Teknik kan bidra till en mer effektiv och hållbar arbetsmiljö, men det är avgörande att den införs på rätt sätt. Genom att följa dessa rekommendationer kan flygplatser säkerställa att digitalisering blir en positiv förändring för både verksamheten och personalen.
The integration of artificial intelligence (AI) in the workplace is reshaping how professionals engage with their work. In a recent study, researchers Andreas Bergqvist, Tony Clear, Mats Daniels, Niklas Humble, Marta Larusdottir, Maria Normark, and Sofia Ouhbi explored how AI affects work engagement among IT professionals through the lens of Self-Determination Theory (SDT). Their research provides insights into both the opportunities and challenges of AI-driven work environments.
The Dual Impact of AI on Work Engagement
The study found that AI tools, such as UiPath and GitHub Copilot, improve efficiency by automating routine tasks. This allows IT professionals to focus on more complex and creative aspects of their work, enhancing their perceived competence—a key component of work engagement according to SDT. However, this efficiency gain comes with challenges, particularly the need for continuous learning and adaptation.
Some professionals view AI as an empowering assistant that supports them in their work, while others feel pressure to deepen their AI knowledge to keep up with industry expectations. The researchers highlight that while AI can enhance competence and autonomy, its rapid evolution demands a significant investment in skill development.
Learning and Adaptation: A Necessity in AI-Driven Work
One key theme that emerged from the interviews was continuous learning. Since formal AI training is often lacking, IT professionals rely on self-directed learning through online resources, internal communities, and hands-on experimentation. Many also use AI itself as a learning tool, leveraging it for brainstorming ideas, debugging, or validating their work.
However, the study also found that AI’s accuracy and reliability remain concerns. Professionals hesitate to fully trust AI outputs without verification, indicating that AI should complement—not replace—human expertise.
AI’s Influence on Motivation and Collaboration
The study revealed that AI both enhances and disrupts traditional work dynamics. Some participants felt that AI increased their autonomy, enabling them to solve problems independently before turning to colleagues. Others noted that AI might reduce direct collaboration, as employees increasingly rely on AI-generated insights rather than consulting team members.
The findings suggest that for AI to positively impact work engagement, organizations must balance efficiency with employee well-being. AI should be designed to support human competencies, allowing professionals to grow rather than feel displaced. Companies should also invest in structured AI training to ensure that employees are equipped to navigate this evolving landscape.
Final Thoughts
As AI continues to reshape industries, its impact on work engagement will depend on how well it is integrated into professional workflows. By understanding AI’s effects on competence, learning, and motivation, organizations can better navigate the challenges and opportunities of this transformation.
For those interested in workplace digitalization, this research underscores the importance of human-centered AI design, ensuring that technological advancements enhance—not hinder—professional engagement.
Reference
Cajander, Å., Bergqvist, A., Clear, T., Daniels, M., Humble, N., Larusdottir, M., Normark, M., & Ouhbi, S. (2024). AI and work engagement: A study of IT professionals through the lens of self-determination theory. Uppsala University.
In our research group, we study the relationships and dynamics of Human, Technology, and Organisation (HTO) to create knowledge that supports sustainable development and utilization of ICT.
