The rapid emergence of generative artificial intelligence (GenAI) has introduced both opportunities and challenges for education systems worldwide. Educational stakeholders are grappling with fundamental questions of how to guide students on whether and when, and in what ways, they should use GenAI. In this study, a framework was developed to guide K-12 policies and guidelines on the use of GenAI. Using the Delphi technique and collective writing, expert perspectives were gathered from participants across 20 countries and six continents. The analysis identified eight key topic areas for K-12 GenAI policy and guideline development: (1) data privacy and security, (2) ethical and responsible use, (3) equitable access, (4) academic integrity, (5) human oversight, (6) GenAI literacy, (7) curriculum integration, and (8) governance and review. A complementary six-part framework was also constructed to support policy relevance and currency through multi-stakeholder governance, continuous review, ongoing training, awareness of external developments, outcome monitoring, and transparent communication. Together, these frameworks advance the scholarly and practical understanding of how GenAI policies can be designed and maintained in schools.

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.

As GenAI technologies become more pervasive in higher education (HE), scholars call for guidance on AI governance. To meet this need, a Delphi technique and collective writing was used in gathering expert perspectives from across 22 countries/locations and six continents. This resulted in the development of a HE GenAI policy/guidelines framework with eight core areas: (1) academic integrity, (2) ethical use and responsible use, (3) privacy and protection, (4) equitable access, (5) GenAI literacy, (6) integration strategy, (7) human oversight and accountability, and (8) institutional support and infrastructure. In addition, a six-part framework was developed to ensure that policies remain current and relevant: (1) creating a dedicated GenAI Committee, (2) conducting regularly scheduled policy reviews, (3) providing ongoing professional development and support, (4) communicating with all stakeholders, (5) evaluating the effectiveness and impact of GenAI, and 6) monitoring external developments. By providing a robust, eight-part framework for policy and guidelines, alongside a six-part mechanism for continued review, this study offers faculty, students, administrators, educational leaders, policymakers, and funders a responsible, adaptable, and consensus-driven blueprint for navigating the integration of GenAI in HE, ensuring that technological innovation serves pedagogical excellence.

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.

The integration of Artificial Intelligence (AI) in education has underscored the urgent need to equip educators with essential AI literacy and related competencies. This paper highlights the critical importance of advancing research on the development of teachers' AI literacy, particularly through targeted professional development (PD) programs. The study piloted a PD program involving 19 mid-career teachers in Singapore. Over six sessions, each lasting three hours, participants engaged in an intensive program designed to enhance their ability to integrate AI into educational practices. The program placed a strong emphasis on ethical considerations and the responsible use of AI. Mixed methods were employed. Data collection included pre-and post-intelligent Technological Pedagogical Content Knowledge (TPACK) surveys, teachers' perceptions on AI, in-class group discussions, and written assignments. Data analyses included content analysis and quantitative data analysis. The results showed a significant enhancement in i-TPACK, accompanied by a noteworthy shift in their overall perceptions of AI. The teachers not only acquired a good understanding of ethical frameworks but also demonstrated adept application in envisioning innovative AI in teaching, schools, and assessment. Importantly, they formulated tailored action plans for implementing AI in their respective schools. The study employed a novel analytical matrix based on the Aristotelian tripartite division of knowledge-episteme, techne, and phronesis-to compare action plans between teachers with different perceived i-TPACK levels, focusing on AI's application in teaching, schools and assessment. This study contributes valuable insights into teacher PD concerning AI in education and informs the implementation of AI in teaching practices.

As artificial intelligence (AI) becomes increasingly embedded in everyday life and educational systems, understanding how students conceptualize AI is essential for designing responsive and inclusive AI literacy curricula. This study investigates the perceptions of AI among Swedish students aged 13-16, using a qualitative survey instrument comprising background questions and five open-ended questions adapted from previous research on student conceptions of AI. Using a BERT-based topic modelling approach, we analyzed responses from 75 students, identifying nine key themes that reflect how students think about, relate to, and emotionally react to AI technologies. The identified topics, ranging from functional views of AI as a tool or helper, to more anthropomorphic notions like robots and AI being "just like"humans, to expressions of uncertainty and mixed emotions, highlight the diversity and complexity of student perceptions. We compare these findings with previous data collected in Azerbaijan to examine cultural and contextual differences in the way young learners understand AI. This research contributes to the growing body of work on AI literacy by providing evidence-based insights into student conceptions and the sociocultural shaping of AI understanding.

The increasing use and application of Artificial Intelligence (AI) across various sectors in society, combined with the growing adoption of emerging technologies, emphasizes the urgent necessity to develop digital competencies in K-12 education, encompassing both digital literacy and AI literacy. Computational thinking (CT) has been recently incorporated into the educational curricula of several countries as it is regarded as an effective approach to improving problem-solving and programming skills. AI literacy, however, has only recently started to gain attention and make its way into K-12 teaching. Few studies to date have investigated to what extent having a technical understanding of AI methods and techniques can contribute to actual AI literacy thus finding effective ways to integrate this knowledge into policy and curricula. To address this problem and contribute with empirical insights, we designed and conducted a study identifying the challenges and the opportunities teaching basic concepts of AI to a class of middle school students. During a workshop series, an entire class of 8^th graders engaged in hands-on activities related to CT and AI with a particular focus on machine learning. We identify a lack of prerequisite skills that impact students’ ability to engage in contextual AI-related activities due, at least partly, to poor programming skills. This, in turn, seems detrimental to gaining transferable skills in machine learning which are important given the interdisciplinary nature and broad application of these AI methods. We identify CT and programming as prerequisite skills necessary to achieve a deeper understanding of AI technology and its implications for individuals and society.

Recent advances in Information and Communication Technologies (ICT), especially AI-powered tools, are driving innovation and productivity across academic and industrial sectors. These technologies are also transforming education by enabling personalized learning, redefining teaching methods, and enhancing educational outcomes. Given the transformative potential of Artificial Intelligence in Education (AIEd), it is essential to examine its benefits and implications for the future of education. AI tools offer substantial benefits to various educational stakeholders by enhancing cognitive engagement, improving teaching assessments, and fostering digital literacy. Additionally, they support policy development, address ethical considerations, and increase educational efficiency and accessibility. However, despite AI’s potential in education, the rapid pace of technological change can create uncertainty and overwhelm, even among experts. As AIEd advances, it is crucial to develop strategies that help educators and institutions stay updated on new developments and integrate them effectively into educational settings. This study explores the historical development of AIEd and identifies current research trends through a dual-method approach. First, a bibliometric analysis using VOSViewer uncovers patterns in AIEd research over the past 50 years. Second, text mining techniques using term frequency analysis and topic modeling—examine over 2,204 abstracts from Scopus, covering publications from 1970 to the present. By combining these methods, the study highlights key AIEd research areas that impact stakeholders and influence future educational practices. Understanding the evolution of AIEd and current trends is essential for anticipating its future trajectory, preparing us to address emerging challenges and opportunities in education. The findings trace the progression of AIEd research, revealing shifts in focus, underlying learning theories, and application paradigms. This comprehensive overview helps identify research gaps and emerging themes, supporting the development of strategies to leverage AIEd’s potential in transforming education and meeting the complex, evolving needs of twenty-first century stakeholders.

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.

Artificial intelligence (AI) and other emergent technologies are increasingly used in different areas of our modern society thus the importance of developing digital competences among young students is a clear necessity. Efforts towards the development of digital competences in K-12 education have been made with the introduction of programming and computational thinking (CT) in different countries around the world. However, AI-related education in K-12 is still trying to find its place (Touretzky & Gardner-Mccune, 2019). In the particular case of Sweden, efforts towards developing digital competences among K-12 students have been made with the introduction in 2018 of programming education in the subject matters of mathematics and technology (Skolverket, 2018). However, both CT and AI literacy have only been implicitly included in the current study programs (Skolverket [theSwedish National Agency for Education], 2022). Developing AI literacy in young students is therefore among the urgent challenges that Swedish K-12 education must face in the coming years.

In order to better understand the challenges of introducing AI concepts to students that have no previous formal education in this field, we conducted a small exploratory study with a class of 8th graders (17 students aged 14-15) that was carried out during the autumn semester 2023/2024 in a municipal school in southern Sweden. This group of students had previous experience doing basic programming, as they had received education in this topic as part of their study programm, however they had received no previous formal education in AI technology. The study consisted of six workshop sessions for a total of 16 classroom hours. The activities carried out with the students were mainly focused on introducing basic concepts of AI and machine learning (ML), with only a few activitiesfocusing on traditional rule-based programming. These workshops were looselybased on the MIT AI Ethics Curriculum (Payne, 2019) and the AI literacy competencyframework from Long and Magerko (2020), for those activities focused on AI, and theframework for assessing development of CT (Brennan & Resnick, 2012), for thoseactivities focused on CT and rule-based programming. Regarding the pedagocical methods, for the design of the activities for this workshop series we used challenge-based learning and inquiry-based learning, both with a constructionist approach, thus these workshops had a special emphasis on hands-on activities.

The selection of tools we used was based on the specific topics that we wanted to cover for introducing the students to general concepts of AI technology while at the same time reviewing some basic concepts of programming. Some of these tools are well-known, such as Machine Learning for Kids, Scratch and Teachable Machine, which are widely used as they are web-based tools that can run on any laptop computer. However, we also used some other less known tools such as MistyRobotics, a so-called social robot which uses AI-based technology such as natural language processing, computer vision, and other functions based on ML techniques. The selection of educational tools for these workshops was made not just to cover the specific topics of CT and AI, but also to offer a fun experience that would increase students’ engagement in the activities.

The main methods we used for data collection during the workshops were: (1) screen recordings, (2) sound recordings, (3) participant-generated content, and (4) observations and field notes. The method for the data analysis was qualitative content analysis. We used the AI literacy and competencies from Long and Magerko (2020) to assess the activities regarding AI and ML, and the framework for CT assessment from Brennan and Resnick (2012) to evaluate the activities regarding rule-based programming. Our results suggest that there are several challenges when introducing young students to basic concepts of AI and ML and even regarding traditional programming we identified some relevant issues. For example, in one of the activities where students were given different challenges of programming small robots, out of five challenges, only the first one (the simplest which required the use of loops) was completed by all the students. The other four challenges which required using programming concepts such as conditionals, logic/arithmetic operators and variables, were completed by less than 50% of the students. As for the activities related to AI and ML, after the students tried different tools working with concepts such as image and sound recognition, training ML models and trying different methods such as unsupervised and supervised ML, over 90% of them managed to provide a definition of AI that is at least partly related to the main AI competencies listed in the framework for AI literacy (Long & Magerko, 2020). Nevertheless, when engaging in activities where the students had to create a working solution based on AI and ML, the rate of success was as low as 20%. Among the possible reasons for this relatively low performance of students when devising aworking solution using rule-based programming and AI methods could be their limited skills in traditional programming. In addition, although students seemed to have managed to acquire an understanding of basic AI concepts and principles, this knowledge might still be superficial and thus the students struggled when trying to transfer the knowledge they acquired on AI to other contexts different from those they had to deal with during the workshops. An integrated and complementary teaching of CT and AI is therefore necessary for developing AI literacy, increasing digital competences and fostering problem-solving skills.

In future studies we intend to continue exploring how to bring together CT and AI in K-12 education using social robots and other types of physical computing. The idea is to explore the possibilities offered by the many sensors and input methods that modern robotics have to study to what extent AI methods such as ML can help students create innovative solutions that, in combination with traditional rule-based programming, could improve their skills to solve problems and create innovative digital solutions.