In alignment with the other digital academies, the activities of the AI Skills Academy will be centred around three pillars of action.

Pillar 1: Knowledge, education, and training

Under this first pillar, the AI Skills Academy will review, develop, and implement education and training material for university and vocational training students. It will also pilot on-the-job trainings for professionals.

More specifically, the goals of this pillar will be:

1) Complement existing information on the AI skills needs in the EU labour market, the AI role profiles currently demanded across key EU industries and the public sector, and the gaps in the existing European higher education and training offer on AI and, specifically, GenAI.

2) Design educational programme(s) and trainings.

• Higher education curricula at ISCED level 6 or 7 (Bachelor’s, Master's or equivalent level) leading to a degree, and related self-standing modules leading to a certification: To be designed are full programmes and self-standing modules on GenAI model development for ICT students, and on (Gen)AI deployment and application for key industrial sectors (see the list of key sectors below), as well as deployment and application for the EU, national and regional public sectors. This higher education offer will be based on the European Credit Transfer and Accumulation System and the Blueprint for a European Degree. Joint degrees are encouraged.
• Technical education curricula (such as vocational training): To be designed are curricula that can be adapted for their adoption by technical education and vocational training institutions across EU Member States leading to a vocational qualification. These trainings should aim at enhancing employability through the acquisition of AI- and GenAI-related skills for ICT and non-ICT students. Input from industry partners and stakeholders, as well as EU Member States’ entities responsible for vocational training, should be included in to fine-tune the content of these trainings, in particular in view of the evolving job market demands.
• On-the-job trainings: To be provided are ready-made training materials for specific profiles/professions and sectors (see again the list of sectors below). Some of these materials should be used by courses directly offered by the Academy and delivered online or in person. The training materials should also be made available to companies or public administrations, which wish to implement the trainings with their own resources, while access to these on-the-job trainings should be facilitated for SMEs, start-ups, as well as the public sector that demonstrate the need to upskill their employees. Collaboration with EDIHs is encouraged, where relevant. Professional training design should take into account the European Approach to Microcredentials.

3) Implementation and delivery of the above-mentioned educational programmes and trainings. The Academy should at least implement the following:

• one certified Master’s programme specialized in GenAI,
• one vocational training scheme,
• one on-the-job training programme per profile/profession (and, where possible, per sector).

The implementation should pay particular attention to the attraction and the promotion of opportunities for women through both a scholarship and a returnship programme targeting this underrepresented population. For further activities addressing women, collaboration with the project “Advancing Girls and Women in Digital” is encouraged.

In terms of content, the AI Skills Academy will focus on a variety of topics and levels of expertise in AI, with GenAI being a core focus. This includes but is not limited to: GenAI model development, inference processes, data science for AI and GenAI sectoral deployment and adoption.

When carrying out activities envisaged under pillar 1, the Academy should create synergies with and build upon already existing efforts and offers, such as the educational programmes and trainings funded under DIGITAL-SO4 on Advanced Digital Skills, the ARISA AI Skills Academy, and other Erasmus+ Programme and possibly courses offered by the European Digital Innovation Hubs, while striving to avoid duplications.

Additionally, the AI Skills Academy will collaborate with the project European Advanced Digital Skills Competitions (once established) that will support the design, manage and run the AI competition.
Under topic 4.3 ELEVATE: European League of Advanced Digital Skills Academies of the Digital Europe Work Programme 2025-2027, an action will develop a framework for a certification mechanism of modular learning credentials to provide a seal of quality/accreditation of excellence for advanced digital skills training. The academy is expected to coordinate with this action and pilot and implement the seal of quality/accreditation of excellence for all the training modules they offer.

Proposals with high ambitions in terms of the number of students trained and the successful completion of the trainings, as well as delivering excellent training offer and all other activities are likely to be assessed more positively. Additionally, proposals delivering their activities in several Member States are likely to have more added value.

Pillar 2: Building the ecosystem

Under this second pillar, the Academy will act as a catalyst to attract and retain European and international talent in the EU labour market, including facilitating the matching of AI-skilled workers with jobs. Communication and dissemination activities will further promote career paths and increase the visibility of the Academy and its education and training offer.

More specifically, the goals of this pillar will be:

1) Build and nurture a diverse and rich ecosystem of stakeholders, including businesses from both the AI industry and other key industrial sectors, higher education, vocational and training institutions, research organisations, as well as EU national and regional public. This may include the organisation of stakeholder events, such as matchmaking, career days, and other formats.

In particular, the establishment and promotion of fellowship schemes allowing PhD students and young professionals living abroad to return to Europe and work in EU-based entities is encouraged.

2) Leverage this diverse ecosystem to offer practical professional experiences in collaboration with the industry and the public sector (e.g. “teach-the-teacher" schemes).

In this context, to be developed and implemented is also a pilot of a full-time AI apprenticeship programme (to support this programme, FSTP might be used), with the aim of preparing a pipeline of AI specialists for working in EU industries and public sector and ensuring their (re-)entry in the EU labour market. To ensure diversity, participants should be selected from various professional and academic backgrounds, as long as they demonstrate a sufficient level of competence in AI and GenAI. Gender balance and cross-Member State representation should be sought, and 15% of the available positions shall be offered to unemployed citizens and/or EU young professionals returning from abroad. After a first training period, participants will work on concrete AI projects for European companies and/or public sector entities. Companies and/or public entities involved should make available real-world data and production environments. Compensation schemes should be provided to the participants by the involved companies or entities.

To increase the impact and the long-term sustainability and to ensure the continued delivery of the designed educational and training programmes, the proposals are expected to foster the dissemination, promotion and uptake of the designed curricula as widely as possible through the main European Universities and Technological Institutes of each Member State. Furthermore, the Academy will engage with the national education ecosystems to scale up existing education and training offers (for example the higher education programmes funded under DIGITAL) and make them available throughout Europe. Active collaboration and inputs from industry must be sought for all activities.

3) Communicate about the opportunities offered by the Academy and more generally the EU AI education and job market, as well as disseminate outcomes and results. This should be done via a dedicated landing page integrated in the Digital Skills and Jobs Platform, a self-standing website titled “AI Skills Academy”, and any other relevant channels. A specific outreach campaign targeting European AI experts and professionals living abroad, aiming to inform them of the opportunities for further education, training and employment in the EU, is to be set up.

Communication should also be strongly coordinated with the umbrella action awarded under the topic “ELEVATE: European League of Advanced Digital Skills Academies” (see topic 4.3 of the DEP WP 2025-2027) that will federate the Digital Europe Programme’s Academies. The Academy will build synergies with the AI-focused initiatives part of the EU AI Innovation ecosystem, namely the AI-on-Demand Platform, the Networks of Excellence in AI, the European Digital Innovation Hubs, and the upcoming AI Factories.

Synergies and complementarities with other EU-funded programmes and existing education and training initiatives should be pursued, in particular with the Artificial Intelligence Skills Alliance (ARISA), the Digital Large-Scale Partnership under the Pact for Skills, the EIT Campus and EIT Community AI, the Next Generation Talent initiative, the Deep Tech Talent Initiative (DTTI), the European Universities Alliances, Alliances for Innovation and the Centres for Vocational Excellence.

An additional dissemination action should target high school students, who would benefit from being informed regarding the education and training opportunities offered by the Academy.

Wider dissemination via, for example, European universities, technological and training facilities as well as cultural and civic institutes and EU-funded projects (also targeting the civil society), will be important to promote new activities and scaling up existing ones, not only at EU, but also at regional and local level.

Pillar 3: Measuring progress

Under the third pillar, the Academy will monitor the progress and impact of its activities. More specifically, the goals of this pillar will be:

1) Develop and implement a robust methodology to systematically monitor the progress achieved in closing the identified AI skills gaps. For this monitoring, EU measurement frameworks such as the Reports on the Digital Decade Policy Programme should be taken into account, as well as, where relevant, data infrastructures such as the Common European Data Space for Skills, and data insights from e.g. the EU Advanced Digital Skills Project (LeADS), StepUp StartUps, the Artificial Intelligence Skills Alliance (ARISA), and the European Centre for the Development of Vocational Training (CEDEFOP). The Academy might also seek alignment with other upcoming monitoring tools of DG CNECT’s European AI Office.

2) Based on the progress monitoring, suggest and implement adjustments of the Academy’s activities. This objective should also take into consideration the feedback received from Academy’s participants.

Additionally, a sustainability plan ought to be developed that explains how the impact of the project beyond the period of EU funding can be guaranteed.

Proposals are highly encouraged to cover the following points explicitly under the RELEVANCE criterion:

• Proposals should explicitly demonstrate how they align with the three pillars of the AI Skills Academy (Knowledge, education, and training; Building the ecosystem; and Measuring progress).
• Proposals should demonstrate the extent to which the project will support the development of skills and training for AI and GenAI in specific EU industries, including healthcare and pharmaceutical, manufacturing, mobility including automotive and aerospace, agriculture, cultural and creative industries, energy, telecommunications, environment, robotics, defence and (cyber)security, finance, and public administration, including education.
• Proposals should explain how the project’s materials, courses and programmes provided are designed to take into consideration all relevant new developments and trends regarding AI and GenAI and show responsiveness to evolving labour market needs.
• Proposals should describe how the project will contribute to the EU's relevant policies (e.g., AI Innovation Package, upcoming Apply AI Strategy, Digital Decade Policy Programme), and synergies with activities at European level.
• Proposals should describe the extent to which the consortium foresees to establish synergies with other Digital Europe Programme’s initiatives and other EU projects as mentioned in the objectives.

Proposals are highly encouraged to cover the following points explicitly under the IMPLEMENTATION criterion:

• Proposals should demonstrate the maturity of the project by describing the totality of the content to be proposed in a structured manner, linking content items to specific objectives or deliverables mentioned in this call text. A granular description of deliverables is essential.
• Proposals should describe the implementation plan, including how the different content items will be designed, developed, implemented by members of the consortium, the needed resources and timeline. All the Academy’s education and training activities should be starting at the latest, at the beginning of the second year of the project.
• Proposals should explain the rationale behind the proposed work packages and tasks, and how they are linked to the objectives/activities/key tasks of the topic. Ensure that the work packages support the three pillars outlined above.
• Proposals should provide deliverables and milestones that are suited to the scope of the proposed implementation and allow for the monitoring of the project’s implementation. Ensure that outcomes and deliverables listed in this call are embedded in the implementation plan.
• If applicable, proposals should explain how students will be financially supported to participate to the programme(s), training modules (e.g. via scholarships, fee waivers or others), and especially to acquire practical experience either with members of the consortium or with companies and organisations outside the consortium. This support can take the form of Financial Support to Third Parties, in particular for the funding of scholarships, fellowships, returnship programmes and apprenticeships.
• Proposals should explain the feasibility of the proposed implementation timeline and budget/use of resources taking into account the proposed work packages.
• Proposals should demonstrate the capacity of the applicants and the consortium as a whole to carry out the proposed work, with particular emphasis on the role to be played by industrial participants.
• Proposals should present how the members of the consortium complement each other, their expertise and their contributions to the project. Make sure to clearly indicate the role each partner will play in the proposal. Clarify how content will be (co-)designed by members of the consortium, with particular emphasis on the role to be played by industrial participants.

Proposals are highly encouraged to cover the following points explicitly under the IMPACT criterion:

• Proposals should describe the expected outcomes and deliverables referred to in the call for proposals and, where relevant, the plans to disseminate and communicate project achievements. The added value of the proposed deliverables compared to the existing offer should be highlighted. The legacy and long-term impact of the Academy beyond the end of the funded project should be highlighted.
• Proposals should ensure that all outcomes and deliverables listed below are addressed by the proposal (and are included in the implementation plan). The added value of the proposed deliverables compared to the existing offer should be highlighted. The legacy and long-term impact of the Academy beyond the end of the funded project should be highlighted.
• Proposals should present all KPIs to be used to for the proposal (incl. Targets). Ensure to cover the obligatory KPIs mentioned below. Propose ambitious KPIs to be achieved by your proposal.
• Proposals should demonstrate the potential of the project to have a multiplier effect, reaching a large number of beneficiaries and creating a lasting impact on the EU labour market. This should include plans for scaling up successful initiatives, creating a network of stakeholders, and developing tools and resources that can be widely adopted.
• Proposals should demonstrate the extent to which the project will close the AI skills gap, improve access to and allocation of workforce in the labour market and strengthen competitiveness across EU strategic industries and the public sector.
• Proposals should explain how the project will align with EU values. Firstly, the project should consider how it will encourage students and professionals to design, develop, apply and use AI in line with ethical standards. Secondly, the project should demonstrate the extent to which it will consider benefits for the society and the environment. Finally, the project should explain how the project will be inclusive, addressing the specific challenge of increasing the proportion of female AI specialists and encourage a diverse workforce in the field of AI.

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By the end of the project, the selected proposal will be required to have achieved the following:

• Detailed description and their related skills, competences and education/training paths, in line with the previously gathered information on the skills gaps and educational/training offer. The description should be linked to the European Classification of Skills, Competences and Occupations (ESCO) and take inspiration from the existing European Cybersecurity Skills Framework (ECSF). Adaptation of the roles to different sectors should be considered.
• Development and implementation of comprehensive academic (Bachelor’s and/or Master’s) curricula to train the identified and needed AI profiles, including in specific sectors of application of AI. At least one certified Bachelor’s or Master’s programme specialized in GenAI should be implemented, with a focus on enabling students from different backgrounds and genders to become AI specialists. Minimum number of students enrolled in a single programme per cycle: 120 for a 3-year Bachelor (minimum number of women: 42), 80 for a two-year Master (minimum number of women: 28), 40 for a 1-year Master (minimum number of women: 14).
• Development and implementation of vocational training curricula to train the identified and needed AI profiles, including in specific sectors of application of AI. At least one vocational training scheme should be implemented, with a focus on enabling students from different backgrounds and genders to become AI specialists. Minimum number of students enrolled in an single institute’s scheme per cycle: 100 per 2-year scheme (minimum number of women: 35), 200 per 3-year scheme (minimum number of women: 70).
• Number of professionals working in SMEs, start-ups, as well as the public sector, who are benefiting from on-the-job training programmes. At least one pilot per profile/profession and possibly per sector. Minimum number of professionals completing a single training: 100 of which minimum number of women: 35.
• Design, development, and support for the implementation of actions to increase the proportion of female AI specialists and encouraging a diverse workforce, starting with a scholarships and returnship scheme for women. Minimum number of women supported (per single scheme): 40 per year (total for two schemes: 80).
• Development and implementation of a pilot EU AI apprenticeship programme to develop a pipeline of AI specialists for the EU industries and public sector, with exposure to real-world data, use cases, and production environments of existing companies/entities. Minimum number of apprentices completing the programme (per cycle of 6 to 9 months): 300 of which minimum number of women: 105.
• Development and implementation of a sound communication and dissemination strategy including different communication and awareness-raising activities, such as social media campaigns and events, and a specific outreach campaign to AI experts and specialists living abroad.
• Development and implementation of a network and synergy strategy between the Academy and other EU projects and initiatives, as well as with industrial and public sector actors, including the promotion of fellowships in EU-based entities.
• Development of a robust methodology to recurrently monitor the evolution of the progress (e.g. via KPIs, users’ feedback) achieved in closing the respective AI skills gaps via the offered activities.
• Development of a long-term exploitation strategy which outlines the long-term impact of the Academy beyond the end of the funded project.
• Curation of a self-standing website and a dedicated landing page integrated in the Digital Skills and Jobs Platform, where all activities, events, and initiatives of the academy are disseminated.

KPIs to measures outcomes and deliverables

The KPIs to measure outcomes and deliverables should be provided by the consortium. KPIs should address the points under the ‘Scope’ section. Priority will be given to consortia who provide realistic, relevant, qualitative and quantitative KPIs that best reflect the proposal’s main outcomes and deliverables. The following KPIs should be included as a minimum (i.e. to be expanded and detailed as needed):

• Number of higher education curricula developed and implemented.
• Number of vocational training schemes developed and implemented.
• Number of on-the-job trainings per AI profile developed and implemented.
• Number of participants in the offered higher education, vocational training and on-the-job trainings offered (including percentage of full completion).
• Number of scholarships and returnship schemes implemented, their value, and the number of women benefitting from the scheme.
• Number of participants in the different Academy’s activities successfully joining the labour market as AI specialists, breaking down per demographics, gender, profession.
• Number of places in the apprenticeship programme offered to unemployed citizens.
• Number of website visitors to the self-standing website and followers on social media.
• Number of communications and awareness-raising activities conducted to promote the project's outcomes and deliverables and number of participants
• Number of collaborations with industry (in particular for the sectors of interest) and the public sector.

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