Led by Haikara (France), WP1 ensures the overall coordination and strategic management of LEAD-AI. It establishes the quality assurance framework, internal communication procedures and risk monitoring mechanisms that support efficient implementation.
This work package also oversees communication, dissemination and exploitation activities, ensuring visibility and stakeholder engagement throughout the project lifecycle. These activities are lead by Europroject – Tinexta Group (Bulgaria).
This represents a detailed project plan, with a Gantt chart and work breakdown structure. Additionally, it contains a schedule per task, responsible partner for the related tasks and sub-tasks, overview of milestones, deliverables and dependencies on tasks. This deliverable corresponds to Task 1.1 Adminsitrative and financial management.
This deliverable describes the project data management policy and specifies any data sets that may endure embargo or be kept from publication, taking into consideration any issues of ethics, privacy, confidentiality, and IPR protection. MEI will ask partners to prepare and treat datasets produced in accordance with the DMP and the standards for metadata, repositories, archiving, and presentation identified therein. The DMP will be delivered first in M6, and updated versions will be submitted in M24 and M48. This deliverable corresponds to Task 1.3 Open research Data Management.
MEI will elaborate a risk management plan, to be executed along the project. All partners will participate in the identification, assessment, registry, and follow-up of all foreseen risks and communicate the identification of unforeseen ones. Contingency and mitigation plans will be elaborated to reduce the potential impacts of the risks.
Since the beginning of the project, MEI will set up the Ethics Requirements that the project must comply with. This report will be provided at M6 and updated all along the project duration. Consent forms, information sheets and proof of compliance with EU standards will be kept on file. European ethics requirements such as Processing of Personal Data and import/export of data with non-EU countries will be the main focus of this task. This deliverable corresponds to Task 1.4 Risk management & Ethics.
Led by ESCP Business School – Berlin (Germany), WP2 builds the pedagogical foundation of the project. It defines the blended learning framework, learning outcomes, assessment strategy and learner differentiation approach. It also develops the Train-of-Trainers methodology, ensuring that educators are equipped with structured guidance to integrate Generative AI into adult learning environments effectively and responsibly.
This deliverable defines the pedagogical approaches underpinning the LEAD-AI capacity-building programme. It outlines adult learning principles, blended learning formats, and best practices to support effective, engaging, and competence-based AI learning for diverse adult target groups.
To be expected in May 2026
This deliverable sets out assessment methods to monitor learners’ progress and validate acquired skills. It includes strategies for formative and summative evaluation, as well as guidelines for the production of micro-credentials aligned with European requirements.
To be expected in May 2026
This deliverable presents the initial evaluation of the Train-the-Trainer toolkit developed for adult educators. It identifies strengths, improvement areas, and pedagogical adaptations needed to support educators in delivering AI-focused training effectively.
To be expected in September 2026
Led by Halmstad University (Sweden), WP3 develops the core training content of LEAD-AI. Based on the framework established in WP2, this work package designs practical modules covering AI literacy, hands-on Generative AI applications, ethical and regulatory considerations, and trust and technology acceptance aspects. It translates pedagogical design into concrete, practice-oriented learning materials tailored to adult learners and professionals.
This deliverable documents the internal review and iterative improvement of the generative AI training materials. It consolidates feedback from partners, trainers, and experts to ensure the content is coherent, relevant, and aligned with the defined learning objectives for adult education.
To be expected in January 2027
This deliverable provides the final set of modular training materials for adult learners and educators to enable flexible adoption of AI across diverse professional contexts.
To be expected in March 2027
Led by Haikara (France), WP4 focuses on the technical development of the multilingual online learning platform that will host the LEAD-AI programme. It includes establishment platform architecture, user experience design, integration of interactive and AI-supported tools, accessibility features and pilot testing. WP4 ensures that the training content developed in WP3 is delivered through a scalable, user-friendly and sustainable digital environment.
This deliverable defines the technical architecture, user interface, and functional specifications of the AI-enhanced e-learning platform. It ensures accessibility, inclusiveness, ease of navigation, and responsiveness, providing the foundation for hosting the LEAD-AI capacity-building programme.
To be expected in July 2027
This deliverable presents the results of the first pilot testing phase of the platform. It gathers structured feedback on usability, learning experience, and functionality to assess how well the platform supports skills development and engagement and ensure its further improvement.
To be expected in July 2027
This deliverable consolidates feedback from pilot testing and documents the final refinements of the platform. It demonstrates how user input has been integrated to improve accessibility, learning effectiveness, and overall performance, resulting in a fully functional learning environment.
To be expected in February 2028