Den accelererande utvecklingen inom artificiell intelligens (AI) berör alla och har utan tvekan redan en betydande inverkan på hur utbildningspraktiker utvecklas och förändras globalt (Holmes & Tuomi 2022). Vikten av att lära sig förstå AI ur olika perspektiv lyfts fram av bland annat Europarådet (2019a) som rekommenderar alla medlemsstater att göra satsningar för att stärka medborgarnas AI-kunnande (Europarådet 2019b, rekommendation 10). I olika länders nationella strategier angående AI-kunnande har behovet i första hand fokuserat på tekniska färdigheter eller kompetenser för ett framtida arbetsliv (Penagos, Kassir & Vosloo 2020). Samtidigt har redan mängder av AI-lösningar, såsom chatbottar, rekommendationssystem och ansiktsigenkänning, blivit en del av vår vardag. AI bör därför ses som ett nutida – inte ett framtida – fenomen, och följaktligen är kunskap om olika aspekter av AI redan en viktig del av skolans innehåll och därmed centralt både för lärarstudenter och verksamma lärare (UNESCO 2021).

 Att AI är en aktuell utbildningsfråga handlar dock inte enbart om teknologiska framsteg, utan speglar också skiftande ideologier, politiska ambitioner och ekonomiska intressen (Linderoth, m. fl. 2024). Införande av AI i utbildning medför därför en diskussion där traditionella metoder utmanas i mötet med teknisk innovation och potentiell förändring av utbildning, samtidigt som gängse syn på kunskap och lärande ifrågasätts (Selwyn 2022).

I detta kapitel diskuterar vi behovet av att de verksamma i utbildningssystemet utvecklar a) förståelse för AI ur ett tekniskt perspektiv, b) förmåga att kritiskt granska och medvetet kunna välja när det är lämpligt att integrera AI i olika undervisningssammanhang och c) kompetens att använda AI utifrån ett professionellt pedagogiskt förhållningssätt. Vi inleder med att förklara vad AI är. Därefter belyses automatisering och förstärkning för att tydliggöra två huvudsakliga sätt som AI kan tänkas bidra i utbildning. Vidare beskrivs några av drivkrafterna bakom introduktionen av AI i utbildning och skola och de behov av problematisering som uppstår. Därefter diskuteras behovet av AI-litteracitet, något som kan ses som en grundkompetens alla behöver för att kunna fatta ansvarsfulla och medvetna beslut i ett samhälle där AI får allt större betydelse. Kapitlet avslutas genom att peka på vad detta specifikt kan innebära för lärare och deras arbete. 

National and international policy documents emphasize the need for AI-related competencies “for all”, but there is little clarity on what these competencies should include, and determining what non-experts need to know remains a challenge. AI literacy has become a widely discussed topic in this context, often referring to a set of skills that empower individuals to critically evaluate AI, communicate and collaborate effectively with AI systems, and utilize AI as a tool across diverse contexts, including online environments, homes, schools, and workplaces. However, what AI literacy looks like in practice depends on factors such as age, level of education, and individual background. In this article, we frame AI literacy based on a qualitative analysis of the views of 33 international experts from various disciplines on what AI literacy in K-12 education should encompass. This analysis builds on existing AI literacy frameworks, with a focus on understanding and critically evaluating AI’s role in daily life, recognizing and using AI, and designing AI solutions for everyday problems. The findings show that experts emphasize a wide range of knowledge, skills, and attitudes, highlighting the importance of multiple perspectives when exploring this emerging field.

AI literacy in school education is booming within the scientific discourse of AI in education. How AI literacy is currently being framed serves diverse educational, political, and commercial purposes influencing how we imagine postdigital classrooms today and in the future. More importantly, how AI literacy emerges in primary education notably impacts how children understand AI and their own agency in a society where AI is ubiquitous. This study reviews how scientific literature conceptualises AI literacy, focusing on middle school students. An AI-adapted literacy framework (GeST) is used in the analysis to distinguish three perspectives of AI literacy (Generic, Situated, and Transformative). Forty-four papers from 2016–2024 were included in the final descriptive and qualitative analysis, showing an exponential growth in scientific papers. While still vaguely defined and poorly theorised, AI literacy materialises into different AI curricula and technology-supported teaching activities. The GeST analysis indicates that AI literacy is primarily viewed as a set of measurable skills related to generalisable theoretical knowledge that is expected to make children more competitive in a globalised and technologised world. Although some papers consider empowering students with specific competencies to challenge the AI development, critical considerations of AI in education is less visible. The paper highlights the necessity to steer the conceptualisation of AI literacy to put a stronger emphasis on critical orientations that enable students as well as teachers to examine claims about AI, and pose ethical questions to its adoption and use in classrooms and beyond.

As a result of the increased digitalisation and its transformational impact on our society, voices have been raised globally for the importance of programming and computing for everyone. Consequently, programming has been introduced in curricula worldwide, either as part of a stand-alone computing-related subject or integrated into other subjects. While the former alternative may seem more straightforward, there are several reasons for why some countries – such as Finland – have opted for the latter approach. In this chapter, I discuss the introduction of programming in the Finnish national core curriculum for grades 1–6 (ages 7–13 years), with particular focus on the cross-curricular aspects. In addition to describing the implementation process, I present three models for introducing programming in other subjects.

In this chapter, we situate Sweden in an international context focusing on how programming and computational thinking have been introduced into primary and lower-secondary education (grades 1–9 in the Swedish system). Our review shows that the strategies used in different countries have their own pros and cons, and there is no clear evidence establishing that one method is preferable. Moreover, due to a lack of clear guidelines, decisions on how programming is taught, by whom, and when, are commonly made at school level, also in Sweden. This freedom, or burden, to locally decide on how to implement the curriculum has left teachers in a difficult position, where they are to fulfil the requirements of the curriculum without proper training, time, and competence needed. This has naturally had a negative impact on how programming and computational thinking have been and are introduced at schools. Based on the review we provide six recommendations, which posit that to succeed, a much more systematic and holistic approach is needed, addressing the needs of teachers, students, and schools.

While programming is a process covering many stages, many of the tasks K-12 students meet at school are small with little need for, e.g., analysis or design. These earlier phases are, however, important to let children meet open-ended problems, brainstorm solutions and ideate their own creative designs. In this paper, we present a model for an online, scalable and scaffolded design workshop for covering such aspects at K-12 level. Through a case study with 1200 students and 60 teachers on IoT and smart things, we describe the workshop and the resulting designs. While the students managed to design their own artifacts, more time had been needed for covering ethical aspects related to technology design. The results suggest creating separate workshops for different grade levels, and also for design and ethical aspects respectively. Moreover, additional resources could support teachers in continuing the discussion with the students after the workshop.

The technological development has raised awareness for the importance of digital competence and computational thinking (CT) to understand the digital world and has resulted in revised curricula in many countries. In Finland, a new curriculum for grades 1–9 came into force in 2016 introducing digital competence (including programming) to be integrated in other subjects. Most teachers lack prior experience in programming and there is a need for suitable instructional models. This article presents a cross-curricular teaching sequence and the results from a case study conducted in four Finnish schools. Students in grades 4–6 collaboratively worked on a project combining arts, design and CT with other subjects. The results show that students demonstrated several CT abilities while working on their projects, in particular creativity, tinkering and debugging. The findings also indicate that teachers and students learned together (co-agency) and suggest that models like the teaching sequence can help and encourage teachers to integrate programming and CT in a cross-curricular manner. Still, the teachers’ knowledge, ambition level and understanding of the task at hand, as well as the organizational support appear to play a notable role when planning and carrying out projects of this kind. While CT is commonly seen as developed through programming, the teaching sequence seems to have fostered CT abilities through the project as a whole, with programming playing the role of a tool or a glue depending on the time available, and the students’ skill and ambition level.

Previous research has shown that even though many students are aware of overarching problems with concurrency, they are less successful in addressing any issues they have found. This implies that the students have not yet developed a mental model that describes the behavior of concurrent systems with enough accuracy. One way to help students explore the non-determinism of concurrent systems and thereby develop their mental model is through the use of visualization tools. One example of such a tool is Progvis, which provides students with a detailed visualization of the program state, and allows students to single-step individual threads to explore the program’s behavior in a concurrent environment. One problem with this type of tools is that they are not able to provide feedback on whether or not a proposed solution is correct, which limits their percieved usefulness. To increase the percieved usefulness of Progvis, we extended it with a system that utilizes model-checking and peer-grading to provide automated feedback to students. Our hopes were that this would encourage students to further use Progvis to practice concurrent programming. The system was used during two years in a course on concurrency and operating systems. This made it possible to utilize the experiences from the first year to further improve the system for the second year. Overall, the students expressed that they found our additions helpful. Additionally, we observed a slight increase in usage in the second year compared to the first year, which suggests that the improvements in the second year increased students’ motivation to some extent.

The research described in this position paper aims at addressing the emerging need for scientifically grounded guidance on how tointroduce AI in K-12 education in Finland. Although there are currently no explicit requirements to teach AI in the Finnish curriculum, digital competence, including programming, is to be integrated across the curriculum. We propose that programming can be used as a gateway for introducing AI, since both programming and AI can be viewed from a technological (using, modifying, creating) and a societal perspective (questions related to ethics, security and privacy). Also, connecting programming education to a given context, such as AI, can help organize the content into a meaningful whole and anchor it in students’ reality by considering current phenomena. The proposed research builds on a design-based methdology including close collaboration with teachers, addressing questions related to curriculum design and evaluation as well as teacher training and professional development.

Previous research has shown that many students struggle with solving small concurrency problems after their first course on concurrency. A possible reason for this is that students do not have a suitable mental model of the semantics of the underlying programming language, and are therefore not able to properly reason about the program’s behavior. One way to help students learn concurrency and improve their mental model is through the use of visualization tools. Progvis is one such visualization tool that is not only aimed at concepts related to concurrency, but also provides an accurate visualization of more fundamental concepts to illustrate how they interact with concurrency. In previous work, the authors of Progvis performed a small-scale evaluation of the tool, and highlighted some areas of improvement. In this paper, we address these shortcomings by improving the memory model visualized by Progvis and implementing a model checker. We also evaluate Progvis on a larger scale by incorporating it into a course on concurrency and operating systems, which allows assessing whether using Progvis aids students in learning concurrency. The results indicate that Progvis (with our improvements) is successful in helping students realize how concurrency interacts with more fundamental concepts, and that students find it useful in helping them understand the content of the concurrency assignments.