AUGMENTOR aims to develop a novel pedagogical framework that promotes both basic skills and 21st century competencies by integrating emerging technologies. This framework will be supported by an open access AI-boosted toolkit that builds on the strengths of big data and learning analytics to provide different types of stakeholders with explainable recommendations for smart search and identification of educational resources, as well as for designing personalized learning profiles that take into account individual actors’ characteristics, needs, and preferences. The overall approach foreseen is innovative, based on appropriately structured data that capture the stakeholders’ learning paths, promote informed and well-justified interaction, and advance critical thinking. AUGMENTOR will leverage advancements in the fields of Pedagogical Design, Creative Pedagogy, Explainable Artificial Intelligence, and Knowledge Representation and Reasoning for instructional purposes. We will meaningfully integrate pedagogical approaches with AI-based software solutions to enable a rich collaboration among all chain’s actors from students to teachers and policy makers. Our goal is to provide guidelines to stakeholders on how to address potential underlying educational difficulties and disabilities, shape individual learning paths, or identify cases of gifted and talented students, to enable them to reach their full potential. AUGMENTOR will enhance trust and transparency, and trigger constructive reflection in both tutoring and pedagogical policy making. The project keeps a strategic balance between the pedagogical and technological dimensions of teaching and learning. At the same time, it contributes equally to the concepts of individual and organizational learning, considering them as interdependent processes. The foreseen solution will be thoroughly deployed and validated in a series of real pilots representing diverse educational and training settings to define best practices.

ORBIS addresses the disconnects between ambitious ideas and collective actions at a large socio-technical scale. It responds to the profound lack of dialogue between citizenship and policy making institutions by providing a theoretically sound and highly pragmatic socio-technical solution to enable the transition to a more inclusive, transparent and trustful Deliberative Democracy in Europe. The project shapes and supports new democratic models that are developed through deliberative democracy processes; it follows a socio-constructive approach in which deliberative democracy is not a theory which prescribes new democratic practices and models, but rather the process through which we can collectively imagine and realize them. ORBIS provides new ways to understand and facilitate the emergence of new participatory democracy models, together with the mechanisms to scale them up and consolidate them at institutional level. It delivers: (i) a sound methodology for deliberative participation and co-creation at scale; (ii) novel AI-enhanced tools for deliberative participation across diverse settings; (iii) a novel socio-technical approach that augments the articulation between deliberative processes and representative institutions in liberal democracies; (iv) new evidence-based democratic models that emerge from the application of citizen deliberation processes; (v) demonstrated measurable impact of such innovations in real-world settings. The project builds on cutting-edge AI tools and technologies to develop a sustainable digital solution, and bridges theories and technological solutions from the fields of political and social science, social innovation, Artificial Intelligence, argumentation and digital democracy. The achievement of the project’s goal is validated through six use cases addressing contemporary issues at different scales and settings, experimenting with different civic participation and deliberation models, and involving diverse types of stakeholders.

NEUROCLIMA aims at establishing a mindset, articulated through processes, frameworks, services & interfaces for the support and establishment of systemic transformations and citizen engagement towards climate resilience by creating a nervous system, connecting policymakers, public institutions and citizens, based on innovative truly mixed Human-AI decision support. This nervous system will be explainable, trustworthy as well as reactive and proactive both to citizens’ concerns, environmental changes, and policymakers’ new proposals, creating a bidirectional relationship between citizens and public institutions. In order to achieve this, this nervous system will engage in rigorous research, by proposing monitoring mechanisms, in the directions of the EU Adaptation Strategy & the Mission on Adaptation to Climate Change. These monitoring mechanisms will monitor needs, pain points, expectations and dispositions as well as emerging trends and relevant themes that may affect directly or indirectly EU societies and help identify social and leverage tipping points. In order to facilitate meaningful change, listening to citizens’ expression, concerns and pain points but also the reasons why policies have been proposed, the project’s nervous system will lead to the proposal of engagement and assessment frameworks and toolkits, taking into account local and national specificities. In this way, a continuous dialogue between stakeholders will take place, helping shape new decisions but also foster trust and understanding towards rule-of-law institutions. Furthermore, the project will provide concrete recommendations of operational nature towards the facilitation of systemic change, accompanied with an ecosystem of tools, activities, services & interfaces that will provide guidance towards reaching the transformative tipping points for the establishment of more resilient EU societies.

The AIXPERT project proposes a novel, comprehensive approach to developing explainable, accountable, and transparent AI systems. At its core, the project introduces an adaptable, situation-aware AI-agentic platform capable of encapsulating various AI models, regardless of their underlying architecture. This architecture-agnostic approach significantly enhances the trustworthiness of AI systems by providing a consistent framework for explainability and accountability across different model types. The project aims to address challenges in AI explainability, transparency, accountability, autonomy, and robustness by integrating multi-agent systems with multimodal foundation models and leveraging real-time human feedback. This integration enhances AI system trustworthiness and user-friendliness while maintaining flexibility in the choice of underlying AI models. The project will be implemented in a multi-layered architecture, with each layer focusing on specific aspects of AI interpretability and explainability: the Agent-World Interface Layer will define and coordinate AI agents, their situational awareness, and their interactions with real-world knowledge sources; the Dialogue Mediation Layer will control user-agent and agent-agent communications; and the Cognitive Foundation Layer will provide the base capabilities for the system based on explainable multimodal foundation models. The project will also focus on developing a framework for assessing AI trustworthiness, demonstrating the value of explainable AI through pilot demonstrations in healthcare, recruitment services, manufacturing, educational robotics, and creative industries, and ensuring the sustainability of its results. AIXPERT envisions delivering AI solutions that are not only transparent and ethical but also sustainable and adaptable to diverse user needs, ultimately fostering greater trust in AI across multiple industries and societal sectors.

PALIMPSEST takes inspiration from the original meaning of the Greek word παλίμψηστος (palimpsestos, ‘again’ + ‘scrape’), which describes the process of the writing practices over papyrus: existing text was scraped and washed off, the surface re-smoothed, and the new literary material written on the saved material. PALIMPSEST adopts this re-writing perspective and grounds it on a living heritage approach. PALIMPSEST envisages regenerating the lost “sustainability wisdom” underlying the production of heritage landscapes through the activation of co-creation processes involving creative actors, technical stakeholders and civic society. Here architecture, design and art practices will dialogue with place-specific needs and broad systemic challenges to imagine new scenarios and experiment with innovative practices connecting human actions, landscape heritage and sustainability objectives. Such experiments will envision novel Landscape Scenarios aiming at producing dedicated Landscape Services, inspired by the generation of beneficial outcomes on ecosystem functions, which the creative contribution of CCIs will empower. Human practices will arise as relevant agents of a new sustainable palimpsest process. PALIMPSEST will integrate the aforementioned Landscape Services in environmental-sensitive solutions with sustainable finance infrastructures to support the sharing and circulating of positive externalities at different levels among the landscape service actors and communities. PALIMPSEST revolves around three pilots with strong cultural identities and relevant environmental problems: Lodz (PL), a UNESCO city of films fighting the highest air pollution levels in Europe; Milan fringes (IT), traditional agricultural landscapes struggling with unsustainable water use; Jerez de la Frontera (ES), an Andalusian wine landscape and vernacular site challenged by renewable energy production facilities.