Deliverable D3.1 presents the EU-ALMPO Documents Annotation Tool, an AI-assisted tool developed within WP3 to support experts in annotating documents on Active Labour Market Policies with structured metadata. Building on the project’s analytical and data collection work, the tool helps transform a broad collection of studies, policy reports and evaluation material into a validated, machine-interpretable knowledge base. By combining Large Language Model-based annotation with expert human validation, it makes policy evidence easier to search, analyse and reuse, while keeping experts in control of the final results. The annotated knowledge base will support the EU-ALMPO Observatory and future AI-driven services, including the Interactive ALMP Design Wizard.

Deliverable D2.2 presents recommendations for developing AI-supported tools for Active Labour Market Policies and skills-matching systems. Based on WP2 analysis, national Communities of Practice and the CoP Survey, it translates stakeholder needs and policy challenges into practical guidance for future EU-ALMPO tools. The deliverable shows how AI can support policy design, matching, monitoring and evaluation, while keeping human judgement central.

This deliverable introduces the EU-ALMPO analytical framework, a core reference model developed to support the understanding and addressing of skills mismatch through Active Labour Market Policies (ALMPs). It provides a structured approach to analysing how labour market imbalances emerge and how policies can respond more effectively. The framework organises key elements such as policy objectives, target groups, and determinants of effectiveness, while also considering broader labour market trends and institutional factors. As the conceptual foundation of the project, it supports the design, analysis, and evaluation of ALMPs and ensures coherence across subsequent work packages, including the development of AI-supported tools.

Deliverable D2.1 presents a comparative analysis of ten in-depth case studies across five countries, examining skills strategies and ALMPs in different national contexts. Developed under WP2, it applies a shared methodology combining desk research and stakeholder input. It supports Tasks 2.1–2.3 and informs Task 2.4, providing a strong analytical basis for the Communities of Practice and future policy development.

The First Ethics Report presents the EU-ALMPO project’s ethics framework and reviews how ethical principles, data protection, accountability, stakeholder engagement, and AI-related risks are addressed across the project.

Deliverable D9.2 presents the EU-ALMPO Project Management Plan, developed under WP9. It defines the project’s governance structure, coordination procedures, quality assurance mechanisms, reporting rules, financial management approach, and internal communication processes. It serves as a practical guide for partners throughout project implementation.

Deliverable D9.1 presents the EU-ALMPO Data Management Plan developed under WP9. It defines how project data will be collected, processed, stored, protected, shared, and preserved throughout the project lifecycle, in line with Horizon Europe requirements, FAIR principles, GDPR, and ethical AI standards.