“Generative AI Youth Work” is based on a comprehensive strategic analysis of the KA220-YOU Voices UnMuted Project. In particular, the project aims to empower young people with fewer opportunities across Europe through lyric and music creation. Moreover, it offers a deep, evidence-grounded examination of how technology, creativity, and inclusion policy intersect. As a result, every youth worker and Erasmus+ coordinator needs to understand what is happening in this field right now.
Have you ever watched a young person struggle to find the words to describe their world? The gap between what they feel and what they can express is not a failure of imagination. Most often, it is a failure of access.
Millions of young people across Europe carry that gap with them every day. Although they have extraordinary inner lives, these are often shaped by migration, poverty, disability, and social isolation. However, the traditional tools of creative expression remain stubbornly out of reach. For example, music lessons cost money, recording studios are usually in cities, and poetry workshops often assume a particular kind of literacy. As a result, a generation of young people remains systematically unheard.
Consequences
The consequences are not abstract. When young people cannot express themselves, they disengage from democratic life. They withdraw from education. They lose the confidence that comes from being seen and understood. For youth organisations working across Europe, this is not a distant problem. It is the daily reality of the communities they serve. Furthermore, it is a problem that conventional youth work, however committed, has only partially solved.
What makes this moment different is that the technology to close this gap now exists. Generative AI can turn a simple text prompt into a melody. It can help a young person who has never written a lyric construct a verse that captures exactly what they mean. It can translate emotion across linguistic barriers and adapt to individual needs in ways that no single teacher or youth worker could manage alone.
The evidence is there. Research into NLP-based music lyric analysis found that AI-assisted creative dashboards produced gains of 28–34% in student comprehension, empathy, and engagement. That is not a marginal improvement — it is a fundamental shift in what is possible for learners who have been left behind by conventional approaches.
There is a community where this kind of knowledge is put into practice — where Erasmus+ practitioners, youth workers, and project coordinators share frameworks, tools, and real results from the field. The kind of community where knowing this changes what you do on Monday morning.
For more information, please check research resources. For further context on the wider community driving this work, explore Learning for Youth.
Key takeaways
Generative AI youth work is not about replacing human creativity — it is about removing the barriers that prevent young people from accessing their own. The Voices UnMuted project demonstrates that AI, used thoughtfully and ethically, acts as a scaffold rather than a crutch. The creative act remains entirely the young person’s own; the technology simply removes the technical obstacles that have historically favoured the privileged.
The most excluded young people — those facing economic, geographical, health-related, and cultural barriers — are also those who stand to gain the most from AI-assisted creative tools. Inclusion, moreover, is not a one-size-fits-all intervention. It requires a multi-layered, intersectional approach that the Erasmus+ programme’s own financial and mentoring tools are specifically designed to support.
National contexts matter enormously. Italy, Spain, North Macedonia, Cyprus, and Slovenia each bring different infrastructure, policy frameworks, and cultural expectations to AI in youth work. Effective Erasmus+ projects build on what already exists in each partner country, rather than imposing a uniform digital model that fits no context particularly well.
Ethical governance is not optional. The EU AI Act, UNESCO guidelines, and the European Commission’s emerging Culture Compass framework all point in the same direction — AI deployed with vulnerable young people must be transparent, culturally balanced, and human-centred in its design and operation.
Documentation and outcome measurement are the difference between a one-off project and a lasting contribution to the field. Tools like Youthpass give young people recognised, transferable evidence of what they have learned and created — and give the project the credibility it needs to influence what comes next.
What you will learn
By the end of this post, you will understand exactly how generative AI youth work functions in practice — not in theory, but through the lens of a real Erasmus+ KA220-YOU project operating across multiple European countries. You will see how the methodology is constructed, what the evidence says about its impact, and what the implementation challenges look like on the ground.
Also, you will be able to identify the specific barriers that prevent young people with fewer opportunities from accessing creative expression, and you will see how AI tools address each of those barriers in concrete, measurable ways. You will also gain a clear picture of how the EU Youth Strategy’s three pillars — Engage, Connect, and Empower — translate into programme design decisions for projects using generative AI.
Meanwhile, you will leave with a set of practical, field-tested principles for integrating AI into youth work ethically and effectively — including how to handle algorithmic bias, cultural homogenization, and the risk of cognitive dependency that comes from over-relying on AI-generated outputs.
You will also understand the national policy landscapes in Italy, Spain, North Macedonia, Cyprus, and Slovenia, and how each country’s existing infrastructure shapes the way AI-driven youth projects are designed and delivered at the local level.
Why generative AI youth work matters now
The Erasmus+ 2021–2027 programme has received approximately €26.2 billion — double the budget of the previous cycle. That investment reflects the Union’s recognition that the challenges facing young Europeans today, from pandemic recovery to digital transition, require a significant step change in approach. Moreover, the programme explicitly places young people with fewer opportunities at the heart of its priorities, establishing inclusion not as an aspiration but as a structural requirement.
Yet funding alone does not close the gap. What changes outcomes is the quality of the methodology — and increasingly, that means asking not just what youth workers can do, but what technology can enable that was not previously possible at scale.
The digital gap that leaves young people behind
Young people with fewer opportunities face a compounding set of disadvantages. Economic barriers limit access to hardware and software. Geographical remoteness means that workshops and training happen somewhere else — in cities, in well-funded institutions, in places that require travel budgets that most families simply do not have. Health-related barriers mean that physical attendance is not possible for some young people, regardless of their interest or motivation. Cultural and linguistic barriers mean that even when a young person can attend, the environment may not have been designed with them in mind.
These barriers overlap and reinforce each other in ways that individual interventions rarely address. A young person who is economically disadvantaged, lives in a rural area, and has a health condition is not facing three separate problems — they are facing one deeply entrenched condition of exclusion. Traditional youth work, however committed, often struggles to address all of these dimensions simultaneously, with the resources available at the project level.
How generative AI youth work changes the equation
Generative AI changes what is possible because it is inherently flexible and low-threshold. A text-to-music platform does not require the user to own an instrument, understand music theory, or speak a particular language with fluency. It meets the young person where they are. Furthermore, it does this at scale — one well-designed AI tool can reach participants across multiple countries, in multiple languages, at a fraction of the cost of equivalent in-person provision.
The Voices UnMuted project grasps this potential and operationalises it within a rigorous Erasmus+ accountability framework. By placing AI at the centre of a creative youth work methodology, it transforms what inclusion looks like in practice — moving from aspiration to documented, transferable results that the wider field can learn from and build on.
The Voices UnMuted project — generative AI in action
The KA220-YOU Voices UnMuted project is an Erasmus+ Cooperation Partnership in the youth field, designed to facilitate the development, transfer, and implementation of innovative practices across EU Member States and associated third countries. Its central methodology is the use of Generative AI to support lyric and music creation by young people who would otherwise have no meaningful access to these art forms — and no recognised vehicle for the self-expression they carry.
NLP and deep learning as creative tools
The project draws on Natural Language Processing and deep learning architectures to enable text-to-music generation. Convolutional Neural Networks identify rhythmic patterns and melody structure. Transformer models map song lyrics to contextual emotions, detecting nuance in ways that surface-level keyword analysis cannot. Generative Adversarial Networks add metaphorical richness to lyrics, converting personal experience into what researchers describe as “computational-affective products” — outputs that hold genuine emotional weight.
These are not abstract capabilities reserved for research labs. In practice, they mean that a young person can describe a feeling — in their own words, in their own language — and receive back a musical or lyrical expression of that feeling that they can then own, edit, and develop further. The technology acts, as researchers describe it, as a “cognitive reflector” — a tool that helps learners identify patterns in their own emotional and artistic experience that they might not otherwise have been able to articulate.
How technology turns emotion into music
NLP dashboards within these systems can visualise the emotional distribution of a set of lyrics using tools as accessible as donut charts — showing the balance of joy, anger, melancholy, and hope present in a participant’s words. This is not just a creative device; it is also a powerful pedagogical one. Youth workers can use these visualisations to open conversations that might otherwise be impossible to start, particularly with young people who have learned to be cautious about expressing vulnerability.
Research in rap and hip-hop contexts confirms this effect. Facilitators working with young people in urban environments consistently find that AI-assisted lyric work allows participants to let “parts of their lives or their way of reading reality emerge” in ways that more structured therapeutic or educational approaches do not always achieve. The algorithm cannot replicate the personal imprint that makes each verse unrepeatable — but it can, crucially, remove the barriers that prevent that verse from being written in the first place.
EU Youth Strategy and the generative AI youth work connection
The EU Youth Strategy 2019–2027 provides the policy architecture within which Voices UnMuted operates. Built around three pillars — Engage, Connect, and Empower — the strategy does not exist in abstraction. It translates directly into design decisions for AI-driven youth projects, and understanding the pillars is essential for any coordinator thinking about how to position a generative AI methodology within a funding application or evaluation framework.
Engage — democratic participation through creative expression
The Engage pillar focuses on encouraging young people to participate in society and democratic processes. Voices UnMuted contributes to this pillar by giving participants the means to articulate their experiences of issues such as human rights, social inequality, and climate change through music and lyrics that reach public audiences. Notably, European Youth Together projects — the funding strand within which this type of initiative operates — are specifically tasked with creating networks that promote inclusive democratic participation and solidarity, particularly in the face of socio-economic backlash that risks leaving the most vulnerable young people further behind.
Connect — transnational cooperation at its most practical
The Connect pillar addresses cooperation between organisations from different countries and cultures. The transnational nature of Voices UnMuted means that young people from different European contexts co-create within a shared digital space — a virtual exchange that facilitates intercultural dialogue and soft skills development. This is particularly important for participants whose geographical circumstances make physical mobility difficult or impossible, and who might otherwise be entirely excluded from the benefits of transnational Erasmus+ programming.
Empower — generative AI youth work as a skills framework
The Empower pillar is perhaps the most direct alignment. Voices UnMuted equips young people with digital literacy, media literacy, and critical thinking capabilities identified as essential in both the Digital Education Action Plan and the European Youth Goals. Furthermore, it addresses well-being explicitly and intentionally. By providing an authentic space for emotional and creative expression, the project helps young people build resilience and address challenges related to mental health and social alienation — challenges that the European Youth Goals identify as among the most pressing facing the current generation.
National approaches to AI creativity in youth work
The implementation of generative AI youth work does not happen in a policy vacuum. Each partner country brings its own infrastructure, investment frameworks, and cultural expectations — and effective project design builds on what already exists at the national level, rather than beginning from scratch.
Generative AI youth work in Italy’s digital profession labs
Italy has invested significantly through its National Recovery and Resilience Plan (PNRR), with Mission 1 specifically focused on digitalization, innovation, and culture. Digital profession labs in secondary education use coding, robotics, and virtual reality — providing an infrastructure that directly supports the kind of AI-assisted creative work at the heart of Voices UnMuted. Additionally, the Giovani 2030 platform and the National Youth Card facilitate access to digital cultural opportunities for young Italians, creating pathways that projects like Voices UnMuted can integrate with rather than duplicate.
Spain’s media literacy and skills for innovation
Spain’s Digital Spain 2026 agenda includes a Digital School Plan that guides schools in integrating AI-assisted teaching and immersive learning environments. The Youth Institute (INJUVE) runs dedicated creative programs for young artists through the Young Creation Program, providing an institutional partner with aligned objectives. Spain also places a strong emphasis on media literacy through initiatives like IS4K (Safe Internet for Kids) — a natural complement to the critical AI literacy that Voices UnMuted builds into its core methodology.
North Macedonia, Cyprus, and Slovenia — building on strong national foundations
North Macedonia’s EDUINO platform has evolved from a pandemic-era intervention into a permanent interactive learning hub, offering multimedia lessons and creative digital activities that align closely with the non-formal learning methods of AI youth work. The Fund for Innovation and Technology Development (FITD) supports student projects in AI-assisted learning and digital arts through programmes like Tech for Youth, making North Macedonia a strong environment for this type of initiative. In Cyprus, specific ethical AI guidance for classrooms — combined with student-led media production platforms like Student Internet Radio — creates a context where the creative and ethical dimensions of AI work can be addressed together. In Slovenia, the National Programme for Youth explicitly names young people with disabilities and Roma youth as priority groups, with youth centres providing the spatial conditions and equipment necessary for international, AI-assisted creative collaboration.
Ethical AI use in generative AI youth work
The EU Regulation on Artificial Intelligence — the AI Act — sets a clear framework for deploying AI systems with vulnerable populations. It demands transparency, safety, and respect for fundamental rights. For a project like Voices UnMuted, working with young people who are already at risk of marginalisation, this is not merely a compliance requirement. It is a moral imperative that shapes every design decision in the methodology.
Algorithmic bias and cultural homogenization
AI systems trained on unbalanced datasets can reflect and amplify existing biases. They may reinforce gender stereotypes in generated lyrics. They may also favour majority cultures over minority and regional voices. This risk is serious for projects serving diverse young Europeans. It especially affects young people from migrant and refugee communities. Therefore, project teams must address this risk explicitly. Overusing AI-generated content can also homogenise cultural expression. It may hide the unique voices the project aims to amplify. As a result, diversity can become a flat algorithmic average.
Safeguards that protect young creators in generative AI youth work
We should not abandon AI because of these risks. Instead, we should deploy AI responsibly. Governance should follow the EU AI Act and UNESCO frameworks. Voices UnMuted uses culturally balanced training corpora. It also frames AI as a collaborative co-creator. Young people are encouraged to challenge, question, and reinterpret AI outputs. They do this through their own creative lens. UNESCO’s human-centred vision provides ethical guidance for these choices. The European Commission’s Culture Compass framework also supports this approach. Together, they keep cultural rights and human creativity central to the methodology.
Generative AI youth work tips that actually work
The Voices UnMuted project offers something rare in the youth sector — a detailed, cross-national implementation of AI creative tools with documented outcomes and transferable lessons. Drawing on that evidence, here are the principles that matter most for practitioners considering how to integrate generative AI into their own programmes.
Prioritise pedagogy over technology
The data is clear — AI’s greatest value in youth work lies in its role as a cognitive scaffold, not as a technological spectacle. The moment a project becomes about the AI rather than about the young person, it loses its purpose. Design the learning journey first, identify where AI genuinely removes barriers or deepens engagement, then select the tools accordingly. Technology should always serve the pedagogy, never replace it.
This means investing as heavily in youth worker training and facilitation skills as in the AI platforms themselves. A youth worker who understands how to use an emotional visualisation dashboard to open a meaningful conversation is worth more than the most sophisticated platform deployed without relational competence. Technology without human skill is expensive noise.
Use AI as a cognitive reflector, not a ghost writer
AI should not create content for young people. Instead, it should help them see their own content more clearly. Emotional dashboards, lyric tools, and rhythm generators work best when they reveal personal insights. They can show participants feelings they could not previously express. Therefore, young people’s own words must stay central to the creative process. Their writing matters, even when it feels rough or unpolished.
Facilitators should structure sessions so that AI input comes after the young person has already made their own attempt. This sequencing prevents dependency and ensures that what the technology reflects is genuinely the participant’s own emotional material, shaped and sharpened by AI rather than replaced by it.
Build media literacy into every session
Young people who use AI tools without understanding them are vulnerable to manipulation, bias absorption, and cognitive dependency. Media literacy is, therefore, not an optional component — it is a core responsibility of responsible generative AI youth work. Every session should include a structured space to examine and question the AI’s outputs, to understand what the algorithm can and cannot do, and to actively strengthen the young person’s own critical and creative capacities rather than bypassing them.
This is particularly important given the speed at which AI-generated content is becoming indistinguishable from human-authored content. Young people who understand how these systems work are not just better creative collaborators — they are better citizens, better equipped to navigate a world increasingly shaped by algorithmic systems they will rarely see clearly.
Address barriers intersectionally
The Erasmus+ inclusion framework identifies six categories of barrier — economic, social, health-related, cultural, geographical, and disability-related — and notes consistently that these rarely appear in isolation. Project design must reflect this intersectional reality. Use the financial tools available through Erasmus+ (inclusion support grants, simplified procedures, preparatory visits, reinforced mentorship) not as tick-box requirements but as genuine responses to the specific, overlapping disadvantages of your target group.
Additionally, consider digital accessibility from the very first design decision rather than as a retrofit. A project that provides AI tools but does not address broadband access, device availability, or linguistic interface barriers has addressed the surface of inclusion without its substance. The most effective projects treat each barrier as both a design constraint and a design opportunity.
Document outcomes with the right tools
Rigorous outcome documentation is the difference between a good project and a lasting contribution to the field. Youthpass is the European transparency tool designed specifically to recognise the competences that young people develop through non-formal and informal learning within Erasmus+ programmes. Using it consistently — and doing so together with young people rather than simply about them — gives participants something tangible and recognised from the experience.
It also gives the project credible, transferable evidence of its impact that can inform future applications, sector publications, and the wider practitioner community. Projects that document well make the whole field stronger. Those that do not leave knowledge on the table that could have shaped the next generation of interventions.
Understanding generative AI youth work through analogy
Even the clearest technical explanation of AI can feel abstract when you are trying to understand what it means for the young person sitting in front of you. These analogies may help ground the concept in something more immediately recognisable from practice.
AI as a mirror, not a painter
Think of generative AI not as an artist who paints on behalf of the young person, but as a mirror that shows them what they are already carrying inside. When a participant enters a text prompt describing sadness, displacement, or joy, the AI reflects that back as melody, rhythm, and lyrical structure. The creative act is still entirely the young person’s. The AI simply removes the technical barrier between the feeling and its expression — the way a mirror removes the barrier between your face and your ability to see it clearly.
This is why “AI does the creative work” is both the most common misunderstanding and the most damaging one. The mirror does not paint your face. It shows it to you in a form you can work with. What you choose to do with what you see — how you edit, develop, and ultimately own the result — is entirely your own. That distinction is not merely philosophical; it is the hinge on which ethical AI youth work turns.
The scaffold that lets learners climb higher
A scaffold on a building under construction is not the building. It is a temporary structure that allows workers to reach heights they could not otherwise safely access. Once the building reaches its full form, the scaffold comes down — and what remains is the work of human hands, not scaffolding. Generative AI in youth work functions in the same way. It gives young people access to creative heights that their circumstances would otherwise deny them. Moreover, when they reach those heights, the creative output — the lyric, the melody, the emotional truth that the piece carries — belongs entirely to them.
The scaffold analogy also clarifies what good project design looks like in practice. You build the scaffold to the height you need, not higher. You remove it progressively as young people’s confidence and independent capability grow. And you never confuse the scaffold for the building — never mistake the AI’s contribution for the young person’s achievement. The best generative AI youth work projects use technology intensively at points of high barrier, and step back deliberately as participants gain creative autonomy.
Frequently asked questions about generative AI youth work
What is generative AI youth work?
Generative AI youth work uses AI tools in non-formal education and youth development. These tools can create text, music, images, and other outputs from human prompts. They help reduce barriers to participation. They also support authentic creative expression. In addition, they build young people’s digital and media competencies. This is especially important for young people with limited access to conventional creative opportunities.
The field draws on established youth work values — inclusion, participation, and empowerment — and applies them to a new technological context. Projects like Voices UnMuted represent some of the most developed and rigorously documented examples of this approach within the European Erasmus+ framework, providing a valuable evidence base for practitioners entering the field.
Why is generative AI particularly relevant for young people with fewer opportunities?
Because generative AI is inherently flexible and, crucially, low-threshold. It does not require prior knowledge of music theory, formal writing skills, or access to expensive studio equipment. A young person can engage with it using basic digital access — and in many implementations, even that barrier is addressed through project-funded hardware provision or mobile-first design. The entry point is a thought, a feeling, a few words typed or spoken in the participant’s own language.
Furthermore, the creativity enabled by AI is authentic rather than mediated. The young person’s ideas, emotions, and experiences drive the output. The technology removes the technical barriers that have historically privileged those with access to formal training and expensive resources — democratising creative production in ways that have genuine consequences for social inclusion and self-determination.
Does using AI in youth work undermine human creativity?
This is the most common concern raised — and it is a legitimate one that deserves a direct answer. However, the evidence from Voices UnMuted and comparable projects consistently suggests that the risk of undermining creativity is a design problem, not an inherent feature of the technology. When AI is positioned as a ghost writer that does the creative work for the young person, creative development can indeed be diminished. When it is positioned as a cognitive reflector or scaffold, it typically enhances creative confidence and output quality.
Educators and youth workers value the personal imprint in AI-assisted creative work. This imprint is specific, unrepeatable, and genuinely human. It comes from the participant, not the algorithm. AI can generate thousands of lyric variations. However, it cannot generate a young person’s lived experience. That lived experience makes one lyric feel true to them. Protecting and prioritising that truth is the youth worker’s primary responsibility.
How does the EU AI Act affect projects like Voices UnMuted?
The EU AI Act sets rules for transparency, safety, and fundamental rights protection. These rules apply to AI systems used across the European Union. Youth projects must explain AI tools clearly to participants. They must avoid biased or culturally unbalanced training data. They should follow ethical frameworks from UNESCO and the European Commission. Projects must also document their AI governance approach. They must show that AI protects participants’ rights and dignity. AI should enhance these rights, not undermine them.
Project coordinators must review AI platforms carefully before implementation begins. They should build media literacy and AI questioning into the core methodology. These elements should be structural features, not afterthoughts. Coordinators must also explain how the technology works. They should tell participants how AI uses their creative inputs. This transparency is not just an ethical requirement — it is a pedagogical one.
What does a well-designed generative AI youth work session look like?
A well-designed session begins with the young person’s experience — not the technology. A well-designed session begins with the young person’s experience. It does not begin with the technology. The facilitator introduces a relevant theme, emotion, or issue. This theme should connect to participants’ lives. Then, participants express their response in their own words. The AI tool comes only after this first creative step. At that point, AI helps them go further. It may suggest a melody that matches their mood. Also, it may offer a metaphor that sharpens their meaning. It may create a rhythm that gives their words new energy..
Throughout the session, the facilitator uses the AI’s outputs to open dialogue rather than close it. What does this lyric say about you? Is the AI’s interpretation of your words accurate? What would you change, and why? The session ends not with a polished AI-generated product delivered to a passive participant, but with a human-authored creative work that the participant genuinely owns — one they could explain, defend, and develop further without the technology present.
How can Erasmus+ projects measure the impact of generative AI youth work?
The most effective approach combines quantitative and qualitative methods, each of which captures dimensions of impact that the other cannot. Quantitatively, projects can track participation rates across barrier categories, session completion rates, digital competence self-assessments before and after the programme, and Youthpass uptake as a measure of recognised learning. These figures provide the accountability data that funders and evaluators require.
Reflective journals provide rich, human evidence that numbers alone cannot capture. Peer feedback sessions also reveal important learning moments. Participant-led documentation shows shifts in confidence, connection, and creative breakthroughs. These moments often happen when young people hear their experiences reflected through music. Both evidence types are necessary for a complete picture of impact. Therefore, projects should build them into the design from the outset.
What are the biggest risks in generative AI youth work, and how can they be managed?
The three most significant risks are algorithmic bias, cultural homogenization, and cognitive dependency. Algorithmic bias can be managed by choosing AI platforms with culturally balanced training data. Media literacy activities also help young people identify and challenge biased AI outputs. Cultural homogenization can be addressed by centring local, regional, and minority cultural expressions. In this way, AI amplifies diverse voices instead of smoothing them into a global average.
Cognitive dependency — the risk that young people become unable to create without AI assistance — is managed by design. Projects should structure sessions to prioritise the young person’s own creative act before any AI input is introduced, and should progressively reduce AI scaffolding as participants’ confidence and independent capability grow. The goal is always greater creative autonomy, not greater reliance. The technology is a tool, not a destination.
Conclusion
Generative AI youth work is not a trend to monitor from a distance. It is a response to a genuine and persistent failure of access — the failure to give millions of young Europeans the tools they need to express who they are, what they know, and what they want their futures to look like. The Voices UnMuted KA220-YOU project demonstrates that this failure is not inevitable. When AI is deployed with clarity of purpose, rigorous ethical governance, and a genuine commitment to the young people it serves, it changes what is possible for the most excluded communities in Europe.
The lessons from this project are clear and transferable. Lead with pedagogy. Use AI as a scaffold, not a substitute. Build media literacy into every session. Address barriers intersectionally, using the full range of Erasmus+ inclusion tools available. Document outcomes honestly and rigorously, using tools like Youthpass to give participants something lasting from the experience. These are not complicated principles — but they require the kind of intentionality and professional commitment that separates projects that make a real difference from projects that merely spend their budgets.
The knowledge, frameworks, and practitioner community to do this work well already exist. Join the Learning for Youth community. To access the tools, peer support, and real-world insights that turn this knowledge into results. And to ensure that the young people you work with are never left without a voice again.
References
Erasmus+ Programme — Πύλη Ενημέρωσης Χρηματοδοτικών Προγραμμάτων — ΓΔ Ανάπτυξης. (2024). Erasmus+ Programme information. https://www.fundingprogrammesportal.gov.cy/en/programs/erasmus-plus-en/
European Parliament. (2024, January 16). Texts adopted — Implementation of the Erasmus+ programme 2021–2027. https://www.europarl.europa.eu/doceo/document/TA-9-2024-0007_EN.html
Nica, E., & Constantin, C. (2022). Outside the bubble: Exploring youth (in)equalities in the context of the Erasmus+ programme. ResearchGate. https://www.researchgate.net/publication/359722472
National Policies Platform — European Union. (2024). 10.3 Support to youth work — Slovenia. https://national-policies.eacea.ec.europa.eu/youthwiki/chapters/slovenia/103-support-to-youth-work
European Commission. (2026). Priorities of the Erasmus+ Programme — Programme Guide Part A. https://erasmus-plus.ec.europa.eu/programme-guide/part-a/priorities-of-the-erasmus-programme
IKY — Greek State Scholarships Foundation. (2025). Guidelines for Erasmus+ coordinators on “inclusion” in Erasmus+ projects. https://www.iky.gr/
European Union Programmes Agency — Malta. (2023). A strategy plan for inclusion and diversity. SALTO Inclusion. https://saltoinclusion.eu/
Pelchat, Y., & Audette, M. (2025). AI-enabled text-to-music generation: A comprehensive review of methods, frameworks, and future directions. Electronics, 14(6), 1197. https://doi.org/10.3390/electronics14061197
Karyotakis, M. A., & Antonakaki, D. (2024). NLP-based music lyric analysis in education. ResearchGate. https://www.researchgate.net/publication/399265438
Marx, Z. (2025). Generative AI music as a disruptive force in music consumption — opportunities and challenges. TU Wien Repository. https://repositum.tuwien.at/
Abbà, M. (n.d.). Rap that sets you free. Mangrovia. https://mangrovia.info/en/rap-freedom-education/
European Parliament Research Service. (2026). EU Youth Strategy 2019–2027: Taking stock and the way forward. https://www.europarl.europa.eu/RegData/etudes/STUD/2026/774716/EPRS_STU(2026)774716_EN.pdf
References continue
EU Funding Portal. (2025). European Youth Together 2025. https://eufundingportal.eu/european-youth-together-2025/
Erasmus+. (2026). European Youth Together — Programme Guide Part B, Key Action 3. https://erasmus-plus.ec.europa.eu/programme-guide/part-b/key-action-3/european-youth-together
European Commission. (2026). What’s new in the Erasmus+ Programme Guide 2026. https://www.picxxi.com/
National Policies Platform — European Union. (2024). 8.7 Fostering the creative use of new technologies — Italy. https://national-policies.eacea.ec.europa.eu/youthwiki/chapters/italy/87-fostering-the-creative-use-of-new-technologies
National Policies Platform — European Union. (2024). 6.7 Skills for innovation — Spain. https://national-policies.eacea.ec.europa.eu/youthwiki/chapters/spain/67-skills-for-innovation
National Policies Platform — European Union. (2024). 6.8 Media literacy and safe use of new media — Spain. https://national-policies.eacea.ec.europa.eu/youthwiki/chapters/spain/68-media-literacy-and-safe-use-of-new-media
National Policies Platform — European Union. (2024). 8.7 Fostering the creative use of new technologies — North Macedonia. https://national-policies.eacea.ec.europa.eu/youthwiki/chapters/republic-of-north-macedonia/87-fostering-the-creative-use-of-new-technologies
Eurydice. (2025). National reforms in school education — Cyprus. https://eurydice.eacea.ec.europa.eu/eurypedia/cyprus/national-reforms-school-education
Horizon Europe France. (2025). Addressing the impact of artificial intelligence, cyberviolence, and deepfakes on equality, democracy and inclusive societies. https://www.horizon-europe.gouv.fr/
UNESCO. (2025). Artificial intelligence and culture — Report of the independent expert group on artificial intelligence and culture. https://www.unesco.org/
European Commission. (2025). Commission launches new Culture Compass for Europe. https://ec.europa.eu/commission/presscorner/detail/en/ip_25_2628
Danish Agency for Education and Research. (2024). National implementation report — European Solidarity Corps mid-term evaluation, Denmark. https://ufsn.dk/
European Commission. (2025). Erasmus+ programme final evaluation for 2014–2020 and interim evaluation for 2021–2027 — Estonia national report. https://ec.europa.eu/
CORDIS — European Commission. (2025). INTRACOMP — Intercultural and transcultural competence through collaborative cultural expression. https://cordis.europa.eu/project/id/101177351
