Public Organisations' Talk on AI Education and Skills Development in Finland

Research Paper Recommended citation: Asplund, M., Ihantola, P., & Malmi, L. (2025). Public Organisations' Talk on AI Education and Skills Development in Finland. In Kangaslampi, R., Langie, G., Järvinen, H.-M., & Nagy, B. (Eds.), SEFI 53rd Annual Conference. European Society for Engineering Education (SEFI), Tampere, Finland. DOI: 10.5281/zenodo.17631638. This Conference Paper is brought to you for open access by the 53rd Annual Conference of the European Society for Engineering Education (SEFI) at Tampere University in Tampere, Finland. This work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 4.0 International License. PUBLIC ORGANISATIONS' TALK ON AI EDUCATION AND SKILLS DEVELOPMENT IN FINLAND M. Asplund a, 1 , P. Ihantola b, L. Malmi c a LAB University of Applied Sciences, Lahti, Finland b University of Jyväskylä, Jyväskylä, Finland c Aalto University, Espoo, Finland Conference Key Areas: 1. Dialogue between engineering and society - effects on education Keywords: AI education framework, AI competency education ABSTRACT In this study, we explore what perspectives of public organisations in Finland, especially labour market organisations and public administration, take on AI skills education. This kind of research on public views related to AI skills education is important as it helps identify societal expectations. Moreover, the results inform education policy planning, curricula development, and the practical implementation of education programmes. The specific research questions of the study focus on objectives and key competencies emerging from public documents. The results indicate that public organisations set distinct expectations for different stakeholders. Students need practical AI skills for future employment, lifelong learners require motivation and curiosity to engage with AI, and educators need to understand AI deeply to teach it effectively and ethically. Key competencies identified include general AI knowledge, data literacy, ethics, and practical AI usage, while technical competencies like algorithm development were underrepresented. 1 Corresponding Author M Asplund [email protected] 1 INTRODUCTION The world is becoming increasingly digitalized and automated at an unprecedented rate. At the heart of this transformation is artificial intelligence (AI), which is revolutionizing both our personal lives and the labour market (Lane & Saint-Martin, 2021). The opportunities offered by AI are vast, but their utilization requires new skills. The success of any nation in this new, AI-driven economy requires a skilled workforce capable of developing and utilizing AI technologies. New educational needs can be identified in many ways. Squicciarini and Nachtigall (2021), for example, studied job postings and observed that AI-related skills were required increasingly often in online job advertisements. One of the problems with the previous kind of needs analysis is that it focuses on needs for today – not on what might be needed in the future. Thus, curriculum design often includes feedback from various stakeholders. To illustrate, design of the ACM and IEEE CC2020 curriculum (CC2020 Task Force, 2020) involved representatives from eleven international professional societies and multiple industry-government members. AI-driven changes in the labour market also involve many perspectives and stakeholders. At a very high level, the roles include employers, employees, educational institutions, and government. Each stakeholder contributes different insights on business expertise, workforce readiness, and skill development. Moreover, in many countries, government plays a central role by organizing and allocating educational resources. Understanding the interests and needs of education consumers, e.g., employers and employees, is increasingly important as this information provides valuable input on how education should be implemented. In this paper, we report our investigation of the goals, competencies, and content presented in the public online communication of labour market organisations and public administration regarding AI skills education, with a particular focus on the Finnish context. We selected these target groups as our focus, because their role in developing the society, especially workforce, industries and business life, is central. The results of the research can be utilized, for example, in planning education policy at different educational levels, in the development of higher education curricula, and the implementation of education programs. However, the goal is not only to develop new curricula. Instead, we want to understand the public discussion, especially the role of various labour market organisations in discussing why AI-related skills or competencies should be taught and what these skills and competencies should be. Moreover, labour market organisations are often central in Scandinavian societies and this kind of case analysis from Finland might help various public organizations in positioning themselves, for example, regarding the level at which the new competencies could be discussed. Finally, the research provides valuable information for companies and other organisations that need AI professionals. 2 BACKGROUND AI skills are identified in various policy documents and educational frameworks. Such documents guide the definition of educational objectives, curriculum planning, teaching, and competence assessment. As public discussion on AI skills is often at very high level, we will start by introducing three high level educational frameworks, namely Digital Competence Framework for Citizens framework (DigComp) (Vuorikari et al., 2022), and UNESCO’s AI Competency Framework for Teachers (AI CFT) (Miao & Cukurova, 2024) and Students (AI-CFS) (Miao & Shiohira, 2024). DigComp focuses on citizens' general awareness and interaction with AI, while UNESCO’s frameworks address the needs of teachers and students in educational settings. Both frameworks highlight ethical considerations, a human-centered approach, and the responsible use of AI. DigComp is a European Commission project started in 2010. The latest version (v2.2) addresses citizens' interaction with AI systems, focusing on knowledge, skills, and attitudes. The framework is divided into five dimensions: information and data literacy, communication and collaboration, digital content creation, safety, and problem-solving. Additionally, proficiency levels are considered, with each competence divided into eight levels, ranging from Foundation to Advanced and Highly Specialized. For example, at the Foundation level and with guidance, individuals can recognize the credibility and reliability of common information sources and their digital content. AI CFT is designed to support the development of AI competencies among teachers, enabling them to use these technological tools safely, effectively, and ethically in their teaching practices. It is based on a human-centered approach and points out that meaningful interactions between teachers should remain at the center of the educational experience. The framework comprises human-centered thinking, AI ethics, AI fundamentals and applications, AI pedagogy, and AI in professional development. AI CFS, on the other hand, focuses on what AI skills should be taught and outlines 12 competencies across dimensions such as human-centred thinking, AI ethics, AI technologies and applications, and the design of AI systems. These competencies span three progression levels: understand, apply, and create. As Rigley et al. (2024) state in their systematic literature review on the assessment of AI skills policies in seven countries, the growing and disruptive impact of AI across various industries and professions means that economic growth and stability depend on maintaining a leading position in AI-driven sectors. The challenges of recruiting AI professionals and the discrepancy between the skills of new graduates and employer expectations point to a skills gap or mismatch. Their findings suggest that comprehensive, national strategies are associated with higher AI readiness indices than more focused, specialist-led strategies. (Rigley et al., 2024) The growing interest towards AI has sparked public discussion on AI literacy, and future skills. Grey literature and public discussion forums have been used as research data in multiple studies exploring the impact of AI on work life. Merikko et al. (2022) have analyzed the discussion forums and observed how Finnish teachers were not worried about being substituted by new technologies. Ouchchy et al. (2020) have analyzed how ethical issues of AI have been visible in public media and argue that by examining this, we can better understand the possible effects of these public conversations. Along these lines, this study focuses on how labour market organizations and public administration in Finland talk about AI skills education. 3 RESEARCH QUESTIONS AND METHODS We set our goal to answer the following questions in the context of public online communication of labour market organisations and public administration in Finland: RQ1: What kind of objectives are set for AI skills education? RQ2: What are the key AI competencies and skills mentioned in the documents? To answer these questions, we systematically searched AI related articles from the websites of the biggest Finnish employer and employee organisations, ministries and other governmental organisations; for example, ministries, trade unions, employers' associations, research centers, specialist organisations, funding organisations, and European Union. These included key stakeholders from the labour market, education, research and politics. Searches were carried out in Finnish using Google by searching for keyword “tekoäly” (artificial intelligence). From 25 initial call results, 16 discussing AI competence education were selected. AI education did not need to be the main topic of the article, however. Google searches were conducted between February and March 2025. The selected articles were analysed thematically. The aim of the analysis was to identify key themes related to the objectives (RQ1) and competencies (RQ2).Using grey literature in research is often justifiable, especially when academic literature is limited or lacks certain viewpoints. It provides diverse perspectives and practical approaches for measuring or approaching outcomes. This is particularly useful when peer-reviewed literature on a topic is scarce. Including grey literature in a review helps ensure comprehensiveness and offers a more holistic understanding of a phenomenon, which in turn supports conclusions drawn from available information (Benzies et al., 2006). Furthermore, incorporating grey literature in research can broaden the scope of a review with more relevant studies by giving a more complete picture of the available evidence, as it can offer rich, contextual details that might not be found in peer-reviewed academic publications (Mahood et al., 2014). 4 PUBLIC DISCUSSION ON EDUCATION OF AI SKILLS IN FINLAND 4.1 Objectives of AI skills education (RQ1) Artificial intelligence (AI) education in Finland addresses diverse needs, benefiting both individuals and society. The key target groups of Finland's AI education are students, teachers, and employees whose competence development and the broader benefit of society AI training aims to address. Key objectives identified in grey literature sources reveal a focus on enhancing work readiness in higher education. Universities of Applied Sciences 1 (UAS) are expected to equip students with AI tools to strengthen their preparedness for the workforce and ensure graduates possess practical AI skills 2 (Arene 2024). Education providers in general are tasked to increase AI literacy and foster safe, ethical AI use in teaching and learning, 1 A University of Applied Sciences is a tertiary level institute that bridges the gap between academia and the professional world. Unlike traditional universities, which often underscore theoretical knowledge and research, a UAS places a strong emphasis on practical, hands-on learning and the direct application of knowledge to real-world situations. (Study.eu, n.d.) 2 AI literacy can be understood as a multidimensional skill set that includes comprehending AI basics, applying it practically, evaluating it critically, using it creatively, and acknowledging its ethical implications (Ng et al., 2021). supported by materials from the Finnish National Agency for Education (Finnish National Agency for Education, 2024a). Advancing AI integration in teaching is essential. Teachers should understand AI's potential and adapt their methods. UAS' should train students and staff in basic AI tool usage (Arene, 2024). This is not possible without adequate resources, as pointed out by the European Commission (2020). Related, the Finnish National Board of Education promotes use of open educational materials and encourages the pedagogical use of the digital environment (Ministry of Education and Culture, 2023). As pointed out in the next section, strengthening basic digital skills through AI training is also a priority. The Central Organisation of Finnish Trade Unions states that developing training for these competencies is vital (SAK, 2024b). Finally, workplace learning cultures must be strengthened to facilitate technology adoption (Nousiainen, 2024). Enabling continuous skill development for employees is also vital. Employees should cultivate AI curiosity and understand its impact on their roles (Ylinen, 2023). Education should facilitate skill enhancement and reduce repetitive tasks (Ylinen, 2023), with a focus on flexible, on-the-job development (SAK, 2024b). The labour union points out (SAK, 2024b) that adult learning is often attended by those who already have a high level of education, while those who would most need additional training are least likely to participate. Therefore, it is important to create easy and motivating ways to acquire new skills. Moreover, employees need to understand the ethical dimensions of AI and automation (Ylinen, 2023). Moreover, continuous learning requires that learners first identify potential skill gaps. Related to this, the Technology Industries of Finland, an organization that represents the Finnish technology industry, aims to use AI to map skills gaps and connect education with employment (Teknologiateollisuus, 2024). European Digital Skills Certificate (EDSC) is another initiative with the same goal of helping citizens demonstrate their level of digital skills (European Commission, 2020). 4.2 AI-related competencies (RQ2) The studied publications contain limited perspectives on technical competencies, focusing instead on general AI knowledge (based on 6 sources), AI ethics and responsibility (2 sources), and the utilization of AI applications (6 sources). While there is also a need to conceptualise competencies from the perspectives of AI application developer education (2 sources) and education on AI-related algorithm and method research (no sources), these areas were rare. General knowledge of AI is essential for users, who must develop AI literacy skills, understanding that AI reflects its training data and can produce biased or harmful information. Therefore, students must critically evaluate AI products, as authors remain responsible for their work (Arene, 2024). Data literacy, encompassing data collection, processing, analysis, interpretation, and presentation, is becoming particularly important. This literacy extends beyond technical and informational dimensions to include legal and ethical principles guiding data use (Ylikoski, 2024). As publication of Central Organisation of Finnish Trade Unions (SAK 2024b, p. 4) states "Technological innovations, including artificial intelligence, will revolutionize human life and the labor market. It has been noted that technological development creates new jobs but also eliminates old ones, and the new jobs require different skills". AI-related education and guidance are crucial, yet only a third of employees have received such education (Nousiainen, 2024). Workplaces need a culture of learning that encourages experimentation and continuous development of one's work (Nousiainen, 2024). Understanding and communicating one's skills are vital for employment (Ylinen, 2023). Employees should be curious and sensitive to changes and skill needs in their field (Ylinen, 2023). Lecturers at UAS' must understand the possibilities of AI in teaching and learning (Arene, 2024). Teaching must consider AI's ethical principles and ensure that AI tools' operating principles are openly presented (Arene, 2024). Educational institutions should evaluate AI's impact on learning processes and theses (Arene, 2024). Learners must develop the ability to critically approach, filter, and evaluate information (European Commission, 2020). AI ethics and responsibility are crucial for employees, who must understand the ethical dimensions related to AI and automation (Ylinen, 2023). The use of AI must comply with general ethical principles such as fairness, equality, and respect. Creators must always consider the ethical implications of AI-generated content, which can be biased and harmful. UAS' should regularly update their ethical and operational guidelines to reflect the latest trends and best practices (Arene, 2024). Utilizing AI applications is essential for investing in employees' digital skills, which is essential for Finland's competitiveness (SAK, 2024b). It is important to ensure everyone is equipped to function in the digital society and labour market (SAK, 2024b). Workplaces need structures and practices that foster curiosity for learning new things, encourage experimentation, and inspire continuous development of one's work (Nousiainen, 2024). Employees should actively engage with development suggestions and participate early in the technology change process (SAK, 2024a). Generative AI may reduce differences between workers by providing weaker workers access to the tacit knowledge of more experienced workers (Ylikoski, 2024). Students must understand AI's possibilities in their studies and develop their skills (Arene, 2024). Educational institutions must be aware of AI's opportunities and challenges (European Commission, 2020). Education systems must adapt to digital transformation (European Commission, 2020). Teaching staff should be given opportunities to use innovative digital methods (European Commission, 2020). Special knowledge and application theme includes programming, data management, analysis, and problem-solving skills that are all important in the future (Finnish National Agency for Education). In the future, these skills, which are traditionally seen as core ICT skills, will be needed in various disciplines. For example, the importance of artificial intelligence skills was explicitly mentioned in the context of chemical production (Finnish National Agency for Education, n.d.). Additionally, Business Finland's publication (2025) highlights that "companies maintain up-to-date expertise, enabling agile market responses". Similarly, Expertise Foresight Forum (OEF) underscores utilizing Big Data and AI, alongside robotics and coding (Finnish National Agency for Education, n.d.). 5 DISCUSSION AND CONCLUSIONS When looking at the objectives of AI education (RQ1), we found that students not yet in working life, lifelong learners (i.e., those already in working life) and educators face different expectations. First, students should be able to gain practical AI skills necessary for their future work life. Second, lifelong learners should acquire the curiosity to study the same topics. And third, teachers should understand AI's potential and adapt it to teach AI literacy and ethical AI use. AI education exists at multiple levels, in formal education on secondary and tertiary levels, as well as in continuing education. On the other hand, education is targeted to the general audience on a national level, as well as for specialists and management. Basic AI literacy, comparable to general literacy and numeracy, is becoming essential in working life. Understanding how AI operates, critically evaluating its outputs, and considering its ethical implications are vital skills, as AI does not distinguish between true and false information. Higher education institutions, workplaces, and national policies all play a role in ensuring that individuals and organisations can effectively utilize AI. However, participation in AI education is often higher among those who are already well-educated, while those who would benefit the most are less likely to engage in further learning. This highlights the need for accessible and motivating learning opportunities, such as online courses and training vouchers. For RQ2, the key competencies identified for educational needs were general knowledge of AI, data literacy, AI ethics and responsibility, and the utilization of AI applications. However, technical competency perspectives, such as education for AI application developers and AI-related algorithm and method research, were weakly represented. The observations illustrate how in Finland, both government administration and labour market organisations have recognized the importance of AI skills and have presented their views on its education in their online publications. Labour market organisations highlight the importance of AI skills in improving employees' competitiveness and employability. Governmental administration and related organisations are also active in promoting AI skills education. Their communication highlights the integration of AI into curricula and support for lifelong learning. Findings are primarily in alignment with the educational frameworks presented in Section 2. General knowledge of AI, AI ethics, and the utilization of AI applications were part of the educational frameworks and visible in the data. In addition, the ability to build AI applications was in the UNESCO framework and our data. Although AI hype is based on research, neither frameworks nor public documents explicitly mention the need to educate future AI researchers. See the comparison in Table 1. The analysis of public documents reveals that strategic-level texts often address AI at an abstract level, focusing primarily on its end use. Notably, the technical contents of AI, including machine learning, are almost entirely absent, providing little guidance for educational planning at different levels. Another key perspective that emerges is the educational viewpoint. Defining the skills required of AI developers is fundamental, given AI's significant role in the economy and product development of companies. However, this skill definition has largely been left to universities and UASs. It is also important to distinguish between AI research conducted by universities and the development of professional skills for AI developers that align with the profile of UAS'. The Rectors' Conference of Finnish Universities of Applied Sciences (Arene) promotes the quality of education in UAS' and develops new practices in collaboration with higher education institutions, as seen in Arene's recommendations on the use of AI (Arene, 2024). Universities operate more independently, coordinating their own recommendations to ensure effective and comprehensive AI education. Table 1. AI Educational Framework based on public documentary and its comparison with DigComp and UNESCO frameworks General knowledge of AI AI ethics and responsibility Utilizing AI applications AI application development AI Algorithm and Methodology Research Public documents yes yes yes yes no Frameworks: EU/DigComp 2.2 yes yes yes no no Frameworks: UNESCO yes yes yes yes no In addition to the above, the roles of different actors must be clearly identified: researchers, developers, and end-users. Who is responsible for education at each level? Who oversees detailed planning of AI education? The Ministry of Education and Culture (OKM) could clearly define in its guidelines that universities are responsible for AI and AI methodology research and UAS' for application development, ensuring that the desired different profiles of these institutions are visible, for example, by developing funding models that support the quality of teaching, research and strategic work in these higher education institutions. However, it is important to avoid overly detailed guidelines that would define the subjects to be taught "at the algorithm level." Additionally, civic education, which could be part of upper secondary and vocational education, must be considered. The grey literature also shows that the target groups for mentions related to AI education vary (e.g., SAK). This underscores the need to identify potential target groups and design tailored AI communication for them. Considering different perspectives is essential to ensure that AI education and communication are as effective and relevant as possible for different stakeholders. Finally, this study has some limitations that need to be considered. For example, despite the best effort to select public organizations that would be representative, some relevant stakeholders may be left out. Even in this case, the qualitative observations provide ground for future research. Another problem is that the study is conducted in a Scandinavian context that can be different from other countries. However, previous research suggests that attitudes toward how AI will change work can be very similar between Scandinavia and the Far East (Persson et al., 2021). This supports the view that our results can be valuable for many others. Statement on the Use of Generative AI In the preparation of this article, generative AI was utilized to enhance the quality of the English language. Copilot and Grammarly were employed to correct grammatical errors and improve the overall structure and clarity of the text.