Introduction
In the Digital Decade Policy Programme 2030, the EU has set ambitious goals: ensuring that at least 80% of those aged 16-74 have at least basic digital skills, employing 20 million ICT[i] specialists, and enabling 75% of enterprises to adopt technologies such as cloud computing, big data, or AI (Artificial Intelligence) by the end of the decade. Achieving these targets, however, especially in the realm of AI literacy, requires a lot more than just deploying technology or empowering traditionally technical teams.
It requires people across society, economic sectors, and industries to understand and use AI responsibly, going beyond providing access to devices or connectivity, but access to equity of opportunity, understanding, and participation. To ensure that AI serves us all equitably, we must reimagine the AI talent pipeline to include not only data scientists and engineers but also professionals across disciplines, older adults navigating a changing workforce, and individuals historically excluded from technological spaces. This democratisation of access to AI means more than offering open-source tools or online courses, but demands a fundamental shift in how we educate, empower, and engage people with technology. It requires inclusive upskilling strategies that are as diverse and dynamic as the societies they aim to serve.
Europe’s digital future depends on widespread AI literacy, not only among tech experts, but among doctors, lawyers, bankers, journalists, and other professionals who will use AI or AI-based tools in their fields.
The EU AI ACT, in Article 4, Chapter I, highlights the importance of AI literacy among providers and deployers of AI systems, urging them to ensure an adequate level of AI literacy among staff and individuals involved in the operation and use of AI systems. But literacy must go beyond formal training. From nurses interpreting AI-assisted diagnostics, to retirees navigating smart home systems, to small business owners automating their services, responsible AI education must be accessible, contextual, and ethically grounded.
In this paper, we will explore the evolving landscape of AI education in Europe and its limitations and AI literacy gap. The paper discusses how extending the AI talent pipeline with intentional inclusivity and a commitment to responsibility can transform AI from an exclusive engine of progress into a shared infrastructure of possibility, ensuring that every professional is equipped to apply AI ethically and effectively within their field.
Evolving European AI Education: Europe’s Early Tech-Focused Approach and Its Limits
Europe’s early efforts in digital upskilling largely centred on technical expertise. According to the 2023 Report on the state of the Digital Decade, despite the numerous initiatives and EU funding programmes that play a significant role in developing, attracting, and retaining skills, under a business-as-usual scenario, the number of ICT specialists in the EU will fall short by up to 12 million by 2030. As illustrated in Figure 1: Digital Decade – EU-level trajectories (2024), this shortfall underscores the urgent need for the EU to intensify its efforts in the global race for digital talent, particularly in science, technology, engineering, and mathematics (STEM) fields.
Figure 1: Digital Decade – EU-level trajectories (2024) – ICT specialists
However, while this STEM-heavy approach addressed some technical capacity gaps, it overlooked a more foundational shift: AI is no longer confined to the tech sector. It is becoming embedded in decision-making systems across healthcare, law, finance, and education. And in these domains, it is not enough for professionals to be merely “AI-aware.” They need genuine technical understanding of how AI tools function in to use them safely, critically, and ethically.
The early, technology-centric approach had some blind spots. It often prioritised technical proficiency in isolation, without sufficient attention to interdisciplinary aspects of AI or responsible AI practices. Furthermore, many upskilling programs were generic, not tailored to how AI is applied differently in healthcare, finance, media, and other sectors. As AI systems became more entwined with daily life, new educational needs emerged, such as understanding AI bias and fairness, ensuring transparency of AI decisions, and complying with emerging AI regulations. None of those can be addressed by technical training alone, and neither will be if we continue to keep our focus solely on technical professionals.
Many initiatives and governance efforts were designed around raising general awareness or offering modular, optional training, without challenging the rigidity of professional university curricula, considering fields such as medicine or law. But when a doctor uses an AI-powered diagnostic system, or a lawyer relies on AI for legal discovery, their decisions carry real-world consequences. Professionals must understand not only how to operate such tools, but also how AI models make predictions, what data they rely on, what biases may exist, and what their limitations are. While technical disciplines have slowly adapted to include AI-related coursework, changing medical, legal or even business and journalistic education remains significantly harder, due to structural, institutional, and accreditation challenges.
Compounding these challenges is Europe’s comparative lag in AI training and confidence. Only 39% of European employees have received formal AI training versus 52% in the US. Analysts warned that “Europe’s lagging AI confidence, competence, and investment are critical challenges in an increasingly AI-driven economy” (FORRESTER).
These gaps highlight the need to rethink AI education by not only adding ethical and interdisciplinary content to technical training, but also by providing basic technical AI training to non-technical professionals like doctors and lawyers. This includes understanding how algorithms work, how predictions are made, and where system limitations lie.
The Urgency of AI Literacy: Beyond Digital Skills
A Global Consensus on Responsible AI Education
The EU’s approach to AI centers on excellence and trust, aiming to boost research and industrial capacity while ensuring safety and fundamental rights (European Commission). Europe’s direction for AI education aligns with a growing global consensus. The OECD AI Principles call on governments not only to uphold human rights and transparency, but also to build human capacity and prepare for the AI-driven labour market transition. It highlights that Governments should empower people to effectively use and interact with AI systems across the breadth of applications, including by equipping them with the necessary skills (OECD AI Principles, Principle 2.4). The UNESCO Recommendation on the Ethics of Artificial Intelligence emphasises the importance of raising awareness about AI and promoting public understanding of AI technologies and the value of data. It calls for open and accessible education, civic engagement, digital skills and AI ethics training, and media and information literacy.
These efforts should be led by governments, intergovernmental organisations, civil society, academia, the media, community leaders, and the private sector. Moreover, it emphasises the need to consider existing linguistic, social, and cultural diversity to ensure effective public participation. This way, all members of society can make informed decisions about the use of AI systems and be protected from undue influence highlighting the essential truth that responsible and equitable adoption of AI requires widespread literacy, not only among developers and engineers but among everyday professionals and the public at large.
Best Practices in Responsible AI Education
Europe is actively broadening its AI education ecosystem through a diverse set of policy frameworks, learning platforms, and national and corporate initiatives. Collectively, these efforts signal a shift from a purely technical, STEM-focused approach to one that embraces interdisciplinary AI literacy, aiming to address the needs of professionals in sectors such as healthcare, education, law, and public service.
The basic blueprint for responsible AI curriculum and training programs is being reflected at the policy level by initiatives, such as the AI Continent Action Plan, which lays the foundation for a strategy of focused deepening of AI-related skills across education levels. While it seeks to increase the number of highly skilled AI professionals, it also emphasises embedding AI literacy more broadly across society and key industry sectors. This ambition calls for responsible AI training among all those involved in deploying AI systems, not only developers, but also end users in businesses and the public sector. These programs reflect a recognition that ethical awareness, human oversight, and AI transparency must be understood by non-technical professionals whose decisions are increasingly mediated by AI systems.
The Digital Education Action Plan (Action 6) and the AILit AI Literacy Framework, co-developed by the European Commission, OECD, and Code.org, push this vision further into the school system. AILit outlines competencies in four domains, namely Engaging, Creating, Managing, and Designing AI, and provides a framework for preparing children and young adults to be active, critical users of AI from an early age. Such efforts set the stage for lifelong, society-wide AI literacy, a prerequisite for building an inclusive AI ecosystem where every citizen, not just technologists, can engage with AI meaningfully and safely.
In terms of platforms and networks, the AI4 Europe Campus and the widely acclaimed Elements of AI MOOC (University of Helsinki and MinnaLearn) exemplify democratised, accessible AI education. These offerings serve both beginners and advanced learners, providing foundational and intermediate AI knowledge in multiple languages and diverse, non-traditionally tech audiences. Over a million people have completed Elements of AI, demonstrating the public appetite for non-exclusionary AI education. Meanwhile, the EIT AI Skills Academy, supported by the European Institute of Innovation and Technology, delivers tailored training to students, SMEs, startups, and public servants, facilitating cross-sector upskilling rather than confining AI to technical silos.
At the research and professional level, the ELLIS network connects over 40 European institutions, supporting cutting-edge AI research while also cultivating talent at the PhD and postdoctoral levels. Importantly, ELLIS collaborates with non-tech disciplines, such as biology, health sciences, and environmental research, highlighting how deep technical expertise can integrate with domain knowledge to solve complex societal problems.
Several Member States have launched national strategies that embody this multidisciplinary vision. Estonia’s AI Leap initiative represents a bold step in integrating AI into general education. Beginning in 2025, all 16–17-year-olds and over 5,000 teachers will receive personal AI accounts to experiment with and learn about AI. This effort goes beyond digital literacy, it instils practical AI familiarity among educators and future professionals alike. Italy, too, is piloting AI-assisted teaching in schools, adapting content delivery and teacher training to match local readiness levels.
Sector-specific initiatives such as SUSA (Sustainable Healthcare with Digital Health Data Competence), a pan-European program involving 12 universities, demonstrate how AI literacy can be meaningfully tailored to professional contexts. With €12.4 million in funding, SUSA equips medical professionals with the digital and data literacy needed to work with AI-integrated healthcare tools, offering a strong model of technical upskilling for non-technical domains.
Private and corporate actors are also contributing. Google’s AI Opportunity Initiative for Europe pledges €25 million to expand AI training for underserved populations, nonprofits, and small enterprises, while elite European business schools (e.g., ESCP, Insead, IESE, Trinity) now offer AI modules within executive education. These programs emphasize strategic fluency, ethical design, and leadership in AI deployment, helping decision-makers across industries adapt to a rapidly changing technological landscape.
Together, these initiatives reflect an evolving understanding: that AI education must be multidisciplinary, inclusive, and technically substantial across professions. The tools of the AI age are not reserved for coders, but are being placed in the hands of doctors interpreting diagnostics, lawyers assessing automated evidence, public servants evaluating algorithmic outcomes, and teachers designing adaptive learning environments. If these professionals are to use AI responsibly, they must be equipped not only with conceptual awareness, but with domain-specific, technical understanding of how these systems work, and where they fail.
AI Education for All: Training Professionals Beyond the Tech Sector
As AI increasingly shapes society, the importance of human-centred education becomes a crucial role in preparing humans for unpredictable futures. Education should strive to offer a deeper knowledge of ethics and human rights, an understanding of AI systems, and critical thinking skills to make people resilient, adaptive and employable (Whose education? The future of learning, labour, and living in the age of AI).
Yet this vision cannot be achieved through disciplinary silos. Designing AI that is “responsible by design” requires more than technical knowledge, but demands a blend of competencies spanning data science, sociology, law, healthcare, philosophy, and domain-specific expertise (AI Education: Fostering Interdisciplinary Collaboration for Ethical and User-Centred AI Development). AI literacy now needs to become embedded in medical, legal, educational, and business curricula, empowering professionals to understand how AI affects their own fields, and how to challenge it when necessary.
The challenge, however, lies not only in making AI education interdisciplinary, but also in making it sector-specific. As recognised in the National AI Strategy Refresh 2024, it is imperative that we continue to provide the digital skills needed to support the specific needs of the labour market across all areas of the economy. Just as AI’s applications vary across sectors, so do the specific competencies needed to apply AI responsibly in those domains. A one-size-fits-all training in “AI ethics” is a start, but truly effective education will be industry-specific, co-designed with input from each sector.
Finally, effective responsible AI education also means involving industry in curriculum design so that training stays up-to-date with real-world needs. Initiatives such as the EU’s “Pact for Skills” in the digital sector encourage companies to co-create training content, identifying which skills are most lacking. This kind of industry-academia collaboration can create agile curricula that, say, teach healthcare AI to medical students or offer legal AI electives in law schools.
One ongoing initiative is the PANORAIMA (Pan-European Network for Responsible Artificial Intelligence Multisector Masters) project that seeks to address the shortage of individuals with the required digital skills to effectively apply AI. It was launched under the EU’s Digital Europe Programme (2025–2029) and it brings together 16 like-minded university, research and industry partners to develop and deliver a Master’s program in human-centred AI. The program consists of four (and later more) specialised tracks that provide in-depth knowledge on the responsible development and application of AI technology to (non-ICT) students coming from traditional industries, including:
- Healthcare & Life Sciences
- Media & Culture
- Law & Compliance
- Management & Finance
Figure 2 below illustrates the PANORAIMA framework, a structured learning pathway designed to support both ICT and non-ICT Bachelor’s graduates in developing specialised AI competencies across multiple sectors. The model shows two parallel entry points, ICT and non-ICT backgrounds, feeding into a set of modular learning events tailored to specific domains. The figure has been provided by the PANORAIMA consortium for the purposes of this paper.
The PANORAIMA initiative exemplifies an AI education model that not only addresses the digital skills gap but does so by embedding AI literacy within specific industry contexts. Yet PANORAIMA is just one example of a growing imperative across Europe: to move beyond generic training and build sector-specific AI education systems that reflect the distinct ethical, technical, and regulatory challenges of each field.
Figure 2: PANORAIMA Learning Pathway: Bridging Diverse Academic Backgrounds with AI Specialisation
As can be seen in Figure 2, the domain-specific learning modules are situated within the broader PANORAIMA ecosystem and are supported by cross-cutting HCAM (Human-Centric AI Methodologies) learning events. The outcome of this learning progression is the preparation of graduates for various AI-related professional roles, such as:
- AI Developer / Architect
- AI & Health Specialist
- AI & Media Specialist
- AI Compliance Specialist
- AI Manager / Specialist
Each track is tailored to non-ICT professionals from those industries, meaning the program is designed to upskill people who might not have a computer science background but who need to work with AI in their field.
Why Sector-Specific AI Education Matters
As AI becomes embedded in critical decision-making systems across industries, the case for sector-specific AI education grows more urgent. Generic digital literacy or one-size-fits-all AI awareness programs cannot equip professionals with the nuanced competencies needed to apply AI safely and effectively in their own domains. Bridging this gap means designing education that is deeply aligned with the specific roles, responsibilities, and ethical stakes of different sectors.
In healthcare, for example, clinicians must be able to not only use AI-assisted diagnostics but also understand the logic behind predictive algorithms to assess their reliability, challenge biased outputs, and maintain patient trust. In law, AI-driven risk assessments or evidence-filtering tools raise profound questions around due process, accountability, and legal interpretability. In finance, managers need the ability to critically engage with automated trading systems, understanding the balance between efficiency and risk, and identifying unintended consequences. Without such domain-specific AI literacy, the promise of responsible AI remains theoretical, more of a vision statement than an operational reality.
Despite growing consensus on the need for inclusive and applied AI education, the path to implementation remains complex. Industry-academia collaboration, while essential, often struggles to keep pace with the rapid evolution of AI technologies and their applications. Educational institutions, particularly in traditionally non-technical fields like medicine, law, and public administration, are frequently slow to integrate AI-relevant curricula, whether due to bureaucratic inertia, lack of expertise, or misalignment with accreditation systems. Moreover, overcoming linguistic, cultural, and regulatory diversity across Europe adds further complexity. These challenges highlight that fostering responsible AI education requires not only vision, but also sustained support, funding, and policy alignment.
Preliminary results from PANORAIMA’s market and needs analysis in Ireland reveal a strong consensus across industry and public sector stakeholders: generic AI literacy is insufficient. Focus group interviews and survey responses consistently emphasised the importance of sector-tailored training that combines technical understanding, ethical awareness, and practical application.
Across all industries engaged in the same preliminary PANORAIMA analysis mentioned above – healthcare, IT services, finance, logistics, public administration, and SMEs – participants identified clear gaps in foundational AI understanding, even among senior professionals. This includes low levels of data literacy, algorithmic transparency, AI system limitations, and compliance with emerging regulations such as the EU AI Act. Critically, these gaps were not just about coding skills; rather, they centred on how to operationalise AI responsibly within specific organisational and sectoral contexts.
In healthcare and regulated industries, participants stressed the need for AI education that integrates legal, ethical, and domain-specific risk management. SMEs involved in health tech projects highlighted their struggles with data governance across jurisdictions, the lack of multilingual AI integration support, and a shortage of professionals who could connect technical teams with legal and strategic stakeholders. Without these skills, even technically robust AI tools remained underused or misapplied.
Focus group participants also identified the need for competency-based training that includes:
- Critical thinking and prompt engineering
- Data governance and quality assurance
- Sectoral ethics and compliance interpretation
- Basic algorithmic understanding for non-technical professionals (e.g. “explainers” and “AI sustainers”)
- Applied business insight (ROI-focused evaluation of AI use).
These insights support the argument that professionals beyond the tech sector require foundational technical literacy, not simply abstract ethics courses. For example, doctors and healthcare managers must understand how models make predictions to judge when to trust or override them. Similarly, finance and legal professionals must grasp how data inputs can affect AI outcomes and how to challenge them if needed.
Another key insight from PANORAIMA is the preference for hands-on, modular, and sector-specific training formats. Across SMEs and large enterprises, stakeholders consistently favoured:
- Short, flexible courses
- Hybrid and experiential formats (e.g. case studies, simulations)
- Embedded training via mentorship and on-the-job learning
- Industry-academia collaborations to ensure content relevance and adaptation to fast-moving AI practices.
However, the findings also highlight persistent barriers to implementation, including:
- Time constraints and low training capacity in SMEs
- Resistance to change, particularly in legacy-heavy sectors
- Gaps in cross-functional communication between legal, technical, and business teams
- Lack of structured collaboration with academic institutions in many organisations.
These insights from PANORAIMA confirm the urgent need for well-funded, interdisciplinary, and adaptable AI education strategies. They reinforce that without targeted, contextual training, AI adoption risks deepening existing inequalities or introducing new forms of operational risk. As Ireland’s AI stakeholders clearly indicate, responsible AI cannot be built without the people who use it being empowered to question, adapt, and co-create it, sector by sector.
Looking Ahead
This paper is one of several outreach efforts toward cultivating greater awareness of the importance of responsible education about AI in the digital transformation of Europe. Our goal with this paper was to showcase why without inclusive, context-aware upskilling, the promise of AI will remain accessible only to a narrow group, thus deepening inequalities and exposing organisations to ethical, operational, and legal risks.
This paper has argued that AI literacy must be seen as a multidimensional competency, extending far beyond traditional digital skills. As highlighted by global frameworks, European policy instruments, and national strategies like Ireland’s AI – Here for Good, we must move from generic awareness toward sector-specific, interdisciplinary, and ethically grounded education. Doctors, lawyers, public administrators, teachers, and entrepreneurs alike will be expected to work with AI systems that influence real-world outcomes, and they must be trained to understand, question, and shape those systems.
By providing insights, structures, and cross-sectoral examples, our hope is to generate dialogue and collaboration that goes beyond academia and policy reports. The larger purpose goes beyond this individual effort: to raise a new generation of leaders in healthcare, finance, media, law, or other areas, who are not only technically adept at working with AI systems but who are also guided by ethical awareness, societal responsibility, and dedication to human dignity.
The preliminary findings from the PANORAIMA market analysis in Ireland make this need concrete. Across sectors, professionals report gaps in foundational AI understanding, ethical reasoning, and the practical ability to apply AI responsibly in their fields. What emerges is a clear message: sectoral AI education is not a luxury or a downstream concern, it is a prerequisite for responsible, sustainable innovation.
Realising this vision requires structural change. Education systems must evolve to integrate AI training into core curricula for all professions. Industry and academia must collaborate more closely to develop modular, flexible, and role-relevant learning opportunities. Policymakers must provide sustained support to ensure that no profession, and no region, is left behind in the AI transformation.
These efforts are not a final step, but rather a continuous journey towards equipping our society with the critical skills to navigate and shape the future of AI. As Europe positions itself at the forefront of trustworthy and inclusive AI development, this broader mission calls upon us all, educators, industry leaders, policymakers, and citizens to join forces in building a future where AI enriches, rather than diminishes, human potential.
If AI is to become a shared infrastructure of opportunity rather than a source of division, we must commit to a new kind of AI education, one that empowers not just a technical elite, but every professional, every sector, and every learner, to engage with technology safely, critically, and creatively. Only then can Europe lead the way toward a digital future that is not just intelligent, but inclusive.
[i] Information and Communications Technology