Animating a Knowledge Base: From Ontologies to Generative AI Models From Expert Systems and the Semantic W eb to Generative AI: Model - Driven and Data - Driven Approaches in Area Studies In a context where the social sciences and humanities are experimenting with non - anthropocentric analytical frames, this article proposes a semiotic (structural) reading of the hybridization between symbolic AI and neural (or sub - symbolic) AI based on a field of application: the design and use of a knowledge base for area studies. W e describe the LaCAS ecosystem - Open Archives in Linguistic and Cultural Studies (thesaurus; RDF/OWL ontology; LOD services; harvesting; expertise; publication), deployed at Inalco (National Institute for Oriental Languages and Civilizations) in Paris with the Okapi (Open Knowledge and Annotation Interface) software environment from Ina (National Audiovisual Institute), which now has around 160,000 documentary r esources and ten knowledge macro - domains grouping together several thousand knowledge objects. W e illustrate this approach using the knowledge domain "Languages of the world" (~540 languages) and the knowledge object "Quechua (language)". On this basis, we discuss the controlled integration of neural tools, more specifically generative tools, into the life cycle of a knowledge base: assistance with data localization/qualification, index extraction and aggregation, property suggestion and testing, dynamic file generation, and engineering of contextualized prompts (generic, contextual, explanatory, adjustment, procedural) aligned with a domain ontology. W e outline an ecosystem of specialized agents capable of animating the database while respe cting its symbolic constraints, by articulating model - driven and data - driven methods .

This chapter addresses the citizenship challenges related to AI in education, particularly concerning students, teachers, and other educational stakeholders in the context of AI integration. We first explore how to foster AI awareness and education, along with various strategies to promote a socio-critical approach to AI training, aiming to identify relevant and ethical uses to prioritise. In the second part, we discuss critical thinking and computational thinking skills that can be mobilised within certain AI-supported educational activities, depending on the degree of creative and transformative engagement those activities require.

The EU Artificial Intelligence Act (AI Act) came into force in the European Union (EU) on 1 August 2024. It is a key piece of legislation both for the citizens at the heart of AI technologies and for the industry active in the internal market. The AI Act imposes progressive compliance on organisations - both private and public - involved in the global value chain of AI systems and models marketed and used in the EU. While the Act is unprecedented on an international scale in terms of its horizontal and binding regulatory scope, its global appeal in support of trustworthy AI is one of its major challenges.

Artificial intelligence has accelerated innovations in different aspects of citizens' lives. Many contexts have already addressed technology-enhanced learning, but educators at different educational levels now need to develop AI literacy and the ability to integrate appropriate AI usage into their teaching. We take into account this objective, along with the creative learning design, to create a formative intervention that enables preservice teachers, in-service teachers, and EdTech specialists to effectively incorporate AI into their teaching practices. We developed the formative intervention with Terra Numerica and Maison de l'Intelligence Artificielle in two phases in order to enhance their understanding of AI and foster its creative application in learning design. Participants reflect on AI's potential in teaching and learning by exploring different activities that can integrate AI literacy in education, including its ethical considerations and potential for innovative pedagogy. The approach emphasises not only acculturating professionals to AI but also empowering them to collaboratively design AI-enhanced educational activities that promote learner engagement and personalised learning experiences. Through this process, participants in the workshops develop the skills and mindset necessary to effectively leverage AI while maintaining a critical awareness of its implications in education.

The rapid advancement of artificial intelligence (AI) has brought significant challenges to the education and workforce skills required to take advantage of AI for human-AI collaboration in the workplace. As AI continues to reshape industries and job markets, the need to define how AI literacy can be considered in lifelong learning has become increasingly critical (Cetindamar et al., 2022; Laupichler et al., 2022; Romero et al., 2023). Like any new technology, AI is the subject of both hopes and fears, and what it entails today presents major challenges (Cugurullo \& Acheampong, 2023; Villani et al., 2018). It also raises profound questions about our own humanity. Will the machine surpass the intelligence of the humans who designed it? What will be the relationship between so-called AI and our human intelligences? How could human-AI collaboration be regulated in a way that serves the Sustainable Development Goals (SDGs)? This paper provides a review of the challenges of lifelong learning in the era of AI from a computational thinking, critical thinking, and creative competencies perspective, highlighting the implications for management and leadership in organizations.

This study proposes an analysis of the different types of ethical approaches involved in the ethics of AI, and situates their interests and limits. First, the author introduces to the contemporary need for and meaning of ethics. He distinguishes it from other registers of normativities and underlines its inadequacy to formalization. He then presents a cartography of the landscape of ethical theories covered by moral philosophy, taking care to distinguish meta-ethics, normative ethics and applied ethics. In drawing up this overview, the author questions the relationship between ethics and artificial intelligence. The analysis focuses in particular on the main ethical currents that have imposed themselves in the ways of doing digital ethics and AI in our Western democracies. The author asks whether these practices of ethics, as they seem to crystallize today in a precise pattern, constitute a sufficient and sufficiently satisfactory response to our needs for ethics in AI. The study concludes with a reflection on the reasons why a human ethics of AI based on a pragmatic practice of contextual ethics remains necessary and irreducible to any formalization or automated treatment of the ethical questions that arise for humans.

Artificial intelligence (AI), based on deep-learning algorithm using high-quality reanalysis datasets, is showing enormous potential for weather forecasting. In this context, the European Centre for Medium-Range Weather Forecasts (ECMWF) is developing a new forecasting system based on AI. Verification results of deterministic forecast for now are promising. However, the realism of weather forecasts based on AI is often questioned. Here, different types of realism are identified and we discuss, in particular, the relationship between structural realism and predictability of weather events. Furthermore, a statistical analysis of deterministic forecasts based on AI points to a realism/performance dilemma that a probabilistic approach should help to solve. -- L'intelligence artificielle (IA) bouleverse aujourd'hui le monde de la pr\'evision m\'et\'eorologique avec l'utilisation d'algorithmes d'apprentissage profond nourris par des champs de r\'eanalyses. Dans ce contexte, le Centre Europ\'een pour les Pr\'evisions M\'et\'eorologiques \`a Moyen Terme (CEPMMT) a d\'ecid\'e de d\'evelopper un nouveau syst\`eme de pr\'evisions resposant sur l'IA. Ces pr\'evisions, pour le moment de type d\'eterministe, montrent des r\'esultats prometteurs. Toutefois, le r\'ealisme de ce type de pr\'evisions reposant sur l'IA est souvent questionn\'e. Ici, nous identifions diff\'erents types de r\'ealisme et interrogeons notamment le rapport entre r\'ealisme structurel et pr\'evisibilit\'e des \'ev\^enements m\'et\'eorologiques. Une analyse statistique de pr\'evisions d\'eterministes reposant sur l'IA laisse apparaitre un dilemme r\'ealisme/performance qu'une approche probabiliste devrait aider \`a r\'esoudre.

This study investigates the acceptability of different artificial intelligence (AI) applications in education from a multi-stakeholder perspective, including students, teachers, and parents. Acknowledging the transformative potential of AI in education, it addresses concerns related to data privacy, AI agency, transparency, explainability and the ethical deployment of AI. Through a vignette methodology, participants were presented with four scenarios where AI's agency, transparency, explainability, and privacy were manipulated. After each scenario, participants completed a survey that captured their perceptions of AI's global utility, individual usefulness, justice, confidence, risk, and intention to use each scenario's AI if available. The data collection comprising a final sample of 1198 multi-stakeholder participants was distributed through a partner institution and social media campaigns and focused on individual responses to four AI use cases. A mediation analysis of the data indicated that acceptance and trust in AI varies significantly across stakeholder groups. We found that the key mediators between high and low levels of AI's agency, transparency, and explainability, as well as the intention to use the different educational AI, included perceived global utility, justice, and confidence. The study highlights that the acceptance of AI in education is a nuanced and multifaceted issue that requires careful consideration of specific AI applications and their characteristics, in addition to the diverse stakeholders' perceptions.

Artificial intelligence (AI) is regarded as one of the most disruptive technology of the century and with countless applications. What does it mean for radiation protection? This article describes the fundamentals of machine learning (ML) based methods and presents the inaugural applications in different fields of radiation protection. It is foreseen that the usage of AI will increase in radiation protection. Consequently, this article explores some of the benefits and also the potential barriers and questions, including ethical ones, that can come out. The article proposes that collaboration between radiation protection professionals and data scientist experts can accelerate and guide the development of the algorithms for effective scientific and technological outcomes.

Artificial Intelligence is more and more pervasive in our lives. Many important decisions are delegated to AI algorithms: accessing higher education, determining prison sentences, autonomously driving vehicles... Engineers and researchers are educated to this field, while the general population has very little knowledge about AI. As a result, they are very sensitive to the (more or less accurate) ideas disseminated by the media: an AI that is unbiased, infallible, and will either save the world or lead to its demise. We therefore believe, as highlighted by UNESCO, that it is essential to provide the population with a general understanding of AI algorithms, so that they can choose wisely whether to use them (or not). To this end, we propose a serious game in the form of a civic debate aiming at selecting an AI solution to control a pandemic. This game is targeted at high school students, it was first experimented during a science fair, and is now available freely.