This paper examines how AI literacy is conceptualised in Swedish upper secondary school policy documents that govern a newly introduced AI subject. Drawing on Green's 3D literacy model comprising operational, cultural and critical dimensions, the authors analyse the curriculum and support materials to explore how these dimensions are represented and what this implies for teachers, teacher educators and students. The analysis shows a strong emphasis on operational literacy, some presence of cultural literacy and a limited focus on critical literacy, particularly its agentive dimension. The authors argue that this imbalance may constrain students' opportunities to critically engage with AI as a sociotechnical phenomenon and limit teachers' ability to support such engagement. The study contributes to international discussions on AI education by highlighting the need for a more holistic and critical approach to AI literacy, with implications for curriculum design, teacher preparation and educational policy.

AI literacy commonly refers to essential skills and knowledge needed in a world where AI is ubiquitous. This work explores the literature on AI literacy, aiming to understand how AI literacy is defined and described. Our analysis teases out themes embedded in AI literacy definitions and their implications from a critical perspective. More specifically, our work highlights how current definitions of AI literacy enable individuals to engage with AI concepts, applications, and ethical implications of the wider socio-technological system. We analyze literature about AI literacy, employing thematic analysis to identify the skills, motivations, perspectives, and assumptions that underpin these conceptualizations of AI literacy. Our findings reveal that while current definitions of AI literacy acknowledge the need for understanding AI and its ethical implications, they often fall short in suggesting how this understanding can help learners critically examine AI, its impact on the socio-technological ecosystem in which it is embedded, and imagine alternatives to current AI practices. 

An emerging challenge from the rapid integration of Artificial Intelligence (AI) in society and education is how to prepare teachers for this evolving reality. Teachers need to integrate Generative AI (GenAI) into their teaching practices and educate students about AI within their specific teaching contexts. Although several frameworks have been published to guide AI literacy education, these frameworks often remain abstract and disconnected from the actual teaching environments where AI literacy needs to be applied. This disconnect makes it difficult to link the theoretical knowledge proposed in these AI literacy frameworks to the specific AI-related requirements that teachers face. Pinpointing AI literacy aspects relevant to teachers’ specific teaching contexts and prior knowledge is needed for guiding teachers to relevant Continuous Professional Development (CPD) and supporting materials. Thus, to address these challenges we have developed a method that has recently been tested and piloted with teachers. The results of our efforts suggest that the tested method effectively triggers discussions about using GenAI in teaching contexts. Distinct patterns of interaction with the GenAI application emerged, which are discussed in the paper with the long-term goal of developing tools to identify misconceptions about AI and enhance understanding.

AI, and data-driven technologies in particular, have recently drawn attention to the importance of possessing an awareness and understanding of corresponding practices to engage with and participate in society i.e. to be data or AI literate. Education is an arena for promoting literacy and therefore teachers’ ability to provide teaching regarding these technologies is a prerequisite for equal participation. As policy and curricula are often nonspecific regarding digital and AI literacy, teachers require relevant scaffolding to interpret and enact curriculum changes. As such, this paper reflects on methods for eliciting teachers’ understanding and knowledge of AI to help effective scaffolding of teachers’ practices. Grounded in our ongoing empirical research we highlight challenges encountered in collecting and analysing data using different methods to understand teachers’ sense-making of AI. We find that certain methods, such as surveys, potentially fail to capture actual knowledge, understanding and attitudes towards ill-defined concepts such as AI. We conclude by discussing the potential implications of relying on data derived using certain methods and suggest alternative methods considering these limitations.

Uncovering patterns and trends in vast, ever-increasing quantities of data has been enabled by different machine learning methods and techniques used in Artificial Intelligence (AI) systems. Permeating many aspects of our lives and influencing our choices, development in this field continues to advance and increasingly impacts us as individuals and our society. The risks and unintended effects such as bias from input data or algorithm design have recently stirred discourse about how to inform and teach AI in K-12 education. As AI is a new topic not only for pupils in K-12 but also for teachers, new skill sets are required that enable critical engagement with AI. AI literacy is trying to close the gap between research and practical knowledge transfer of AI-related skills. Teachers' AI-related technological, pedagogical and content knowledge (TPACK) are important factors for AI literacy. However, as teachers' perspectives, beliefs and views impact both the interpretation and operationalisation of curriculum. this study explores teachers' and teacher educators' understanding and preconceptions of AI to inform teacher education and professional development. To gain a comprehensive understanding of teachers’ conceptualisations regarding AI an anonymous questionnaire together with focus group discussions were employed. The qualitative content analysis underpinned by the theoretical framework Intelligent TPACK reveals that teachers' AI-related content knowledge is generally gained through incidental learning and often results in pre- and misconceptions of AI. Our analysis also revealed several potential challenges for teachers in achieving core constructs of Intelligent TPACK, examples of such challenges are vague and unclear guidelines in both policy and curriculum, a lack of understanding of AI and its limitations, as well as emotional responses related to participants' preconceptions. These insights are important to consider in designing teacher education and professional development related to AI literacy.

Recent developments in artificial intelligence (AI) and its ubiquitous presence in society have caused debate about its possible implications for individuals and society, not least in the domain of education. An ongoing discussion about what knowledge related to artificial intelligence is needed to navigate and take part in an increasingly data-driven society is currently shaping and defining the research area of AI literacy. Previous research has focused on identifying what pupils should know, but more needs to be discovered about what pupils already know about AI. To this end, this study explored the conceptualization of AI among more than 120 Swedish K-12 students and investigated the effectiveness of AI literacy interventions in enhancing their critical thinking and ethical awareness. The students displayed diverse and often inaccurate preconceptions of AI, often associating AI with physical computing and robotics. Although the pupils seemed to engage in and understand particular ML concepts at the time of intervention, it was a more difficult task for them to transfer this knowledge to different contexts in which such ML concepts are embedded.

Generative artificial intelligence is increasingly entering educational practice, raising new questions about the knowledge teachers require to engage with AI systems in pedagogically meaningful ways. While research on AI literacy and teacher knowledge frameworks has expanded, most studies focus on teachers’ self-reported competencies rather than how professional knowledge is enacted in practice. This study investigates how K–12 teachers conceptualise and enact AI-related professional knowledge during GenAI-supported lesson planning, with particular attention to the relationship between articulated knowledge and enacted practice. Data were collected through a pre-workshop questionnaire (N = 61), interaction logs documenting teachers’ lesson planning with a GenAI system (approximately 1300 prompt–response pairs), and group-based SWOT reflections following the activity. Qualitative content analysis was used to analyse questionnaire and SWOT data using the Intelligent TPACK (I-TPACK) framework, while interaction logs were analysed using a collaborative problem-solving framework to examine patterns of teacher–AI interaction. The findings show that teachers primarily articulate AI-related competence in technological and pedagogical terms, while ethical considerations appear less frequently. Interaction analysis reveals that teachers most often position GenAI as the primary generator of instructional content, with fewer instances of iterative negotiation or co-construction. The results indicate a complex relationship between teachers’ articulated knowledge and their enacted practices during interaction with GenAI. By triangulating across self-reported, interactional, and reflective data, the study demonstrates how AI-related teacher knowledge is not only conceptualised as competence but enacted through situated teacher–AI collaboration, contributing to ongoing discussions on AI literacy and teacher knowledge in educational contexts.