National artificial intelligence (AI) strategies have become increasingly important for countries seeking to maintain competitiveness and maximize societal benefits. Many nations have released strategic AI plans, focusing on modernizing the public sector, enhancing industry competitiveness, and responsibly managing data and algorithms (Fatima et al., 2020). Previously, van Berkel et al. (2020) analysed 25 national AI policies and found that there are significant differences between countries in terms of semantic similarity, topic prioritization, and discussion of ethical principles. On the other hand, Bareis and Katzenbach (2022) found that the narrative construction of national AI strategies is very similar across countries, establishing AI as an inevitable and disruptive technological development. Although, the AI imaginaries articulated in these strategies are different, reflecting the cultural, political, and economic differences of the countries.

For example in Nordic countries, the cultural values of trust, transparency and openness influence these documents and the themes of privacy, ethics, autonomy and democracy are prominent. Also, they emphasize the importance of citizen involvement and education for successful AI policy implementation (Robinson, 2020). Another example is Uzbekistan, where AI is seen as a key driver of economic growth, social development, and modernization. The Uzbekistan government has launched initiatives, such as establishing AI research centres, promoting entrepreneurship and innovation, and investing in digital infrastructure. However, the lack of skilled workforce, limited funding, and ethical and legal concerns are impeding implementation (Rakha, 2023). Another example is the United Kingdom, where strategy represents a shift in the country's industrial, policy, regulatory, and geo-strategic agenda, with a focus on innovation and opportunity, underpinned by a trust framework (Kazim et al., 2021). It is evident that we can see regional clustering regarding national AI strategies focus, as it is supported by Papadopoulos and Charalabidis (2020) who distinguished between a technology and innovation-driven approach (China, Japan) and a public sector, ethics and safety focused cluster (Scandinavian countries and Germany, Luxembourg). Previous research failed to focus on the Central-European region, therefore in our research we will examine the unique characteristics of national AI policies from these countries.

National AI policies and strategies increasingly focus on education as a key area for development and implementation. A topic modelling analysis of 30 AI national policies identified education as one of the primary concerns, along with technology and government. Challenges exist in responsibly implementing AI in education, with some policies marginalizing ethical algorithms and comprehensive data management approaches  (Saheb & Saheb, 2022). National AI policies often highlight personalization, teacher professional development and the importance of integrating AI literacy into educational curricula. As AI technologies become increasingly prevalent, equipping students with the necessary skills to navigate and utilize these tools is essential. AI literacy encompasses understanding how AI systems function, their societal implications, and ethical considerations surrounding their use (Karki et al., 2024).

In order to capture both the public policy interconnectedness, the specific topics related to education and the ethical and safety dimension, we used the recommendations laid out in the UNESCO’s Beijing Consensus (UNESCO, 2019) to construct our analytical framework. Therefore, the main aim of our study is to explore and analyse national AI policies from Central European countries focusing on education.

Project no. 146998 has been implemented with the support provided from the National Research, Development and Innovation Fund of Hungary, financed under the OTKA-FK funding scheme.

To explore how Central European countries integrate artificial intelligence (AI) into their national education policies, we used a qualitative research design with a focus on content analysis. Using a hybrid coding approach, combining deductive and inductive content analysis (Fereday & Muir-Cochrane, 2006), the research investigates AI-related educational priorities identified in selected national policy documents. The coding framework was grounded in the UNESCO Beijing Consensus on Artificial Intelligence and Education (2019), particularly its recommendations on (1) planning AI in education policies, (2) empowering teaching and teachers through AI, (3) AI for learning and learning assessment, (4) developing values and skills for life and work in the AI era, and (5) promoting equitable and inclusive AI use in education. Based on these elements, the following deductive codes were established before the analysis: 1.1: Multidisciplinary approach 1.2: Policy integration 2: Teacher role 3.1: Curriculum transformation 3.2: AI tools and learning processes 3.3: Adaptive learning and AI tools 3.4: Competency assessment using AI 4: AI impacts on economic and social changes 5: Equal opportunity Policy documents were sourced from the OECD AI Policy Observatory, selecting those explicitly identified as official national AI policies. The study focuses on five Central European countries – Slovenia, Hungary, Austria, Czech Republic, and Romania – where such documents were publicly available during the data gathering (November 2024). Each policy document constituted a sampling unit, with subchapters analysed as the coding units. To streamline analysis and avoid redundancy, executive summaries and introductory sections were excluded from the coding process. Two independent experts conducted the coding to ensure reliability. The first coder performed the initial analysis, followed by a two-week interval during which a second expert reviewed the coded material. This iterative process aimed to enhance accuracy and establish inter-rater reliability (Campbell et al., 2013). The study employs a mixed-methods approach to qualitative content analysis, combining the analysis of frequencies and co-occurrences of deductive codes with a qualitative analysis of inductive codes. This dual approach allows for both structured examination based on predefined categories and the identification of emergent themes, providing a comprehensive understanding of how education is addressed in AI strategies.

The analysis reveals significant variation in the thematic focus and emphasis placed on different deductive themes across countries. Austria and the Czech Republic exhibit the highest diversity, with balanced distributions across multiple themes, while Romania demonstrates limited thematic coverage. Austria and Hungary prioritizing multidisciplinary approaches, while the Czech Republic leads in curriculum transformation. Co-occurrence patterns reveal strong interconnections between policy integration and multidisciplinary approaches, particularly in Austria and Slovenia. The qualitative analysis of inductive codes reveals shared priorities. Key themes include promoting interdisciplinary collaboration, enhancing digital infrastructure, and fostering workforce adaptability through education and lifelong learning initiatives. Countries emphasize equitable access to AI, mitigating biases, and leveraging AI to drive economic competitiveness while addressing regional disparities and fostering innovation ecosystems. Focusing on education, the five countries reveals shared priorities in transforming teaching roles, curricula, and learning processes, with distinct national focuses reflecting unique contexts. Teacher training and digital competency development are emphasized across Slovenia and Hungary, which highlight the use of AI-driven tools to support personalized learning and vulnerable groups, while Romania identifies significant infrastructure gaps, particularly in rural areas, as barriers to effective integration. Curricular transformation is a priority in Slovenia and the Czech Republic, focusing on early AI exposure and interdisciplinary connections, while Austria emphasizes leveraging STEM and social sciences to address skill gaps. Hungary has a specific focus on modular training and AI-supported vocational education. Adaptive learning and AI tools, such as personal learning assistants and learning analytics, are central in Slovenia and Hungary, enabling tailored pathways and equity in access. The Czech Republic stresses automating routine teaching tasks to enhance teacher creativity. This study demonstrates the diverse thematic focuses and regional strategies of Central European countries AI policies focusing on education, reflecting shared priorities alongside distinct national approaches shaped by socio-economic context.

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