In the post-digital world, educators are tasked with navigating a plethora of resources to build effective interventions for ever-changing skill requirements. Intelligent systems can assist them by recommending educational content, providing personalized learning experiences and automating time-consuming tasks, allowing educators to focus on creating engaging and interactive learning experiences. However, it is also important that educators are equipped to fruitfully engage with such systems in a way that enhances their pedagogy and teaching practices as far as help them to deal with societal challenges. The project ENCORE aims to support this shift by developing an intelligent system that selects quality Open Educational Resources (OERs) to support the pedagogical design and teaching around key skills affected by societal macro-trends, i.e. Green, Digital and Entrepreneurial (GDE) skills. The project has two phases: a data-driven phase using Natural Language Processing to match skills with relevant OERs and a qualitative/expert-driven phase for developing pedagogical guidelines and testing the system in real educational contexts. This paper describes the main tenets of the ENCORE project and the research actions taken to support learners and educators in using OERs and targeting skills most relevant to the new macro-trends of digitalization, climate change, and post-COVID economic recovery challenges.

This Work in Progress paper introduces the design of an innovative educational system that leverages Artificial Intelligence (AI) to address challenges in physics education. The primary objective is to create a system that dynamically adapts to the individual needs and preferences of students while maintaining user-friendliness for teachers, allowing them to tailor their teaching methods. The emphasis is on fostering motivation and engagement, achieved through the implementation of a gamified virtual environment and a strong focus on personalization. Our aim is to develop a system capable of autonomously generating learning activities and constructing effective learning paths, all under the supervision and interaction of teachers. The generation of learning activities is guided by educational taxonomies that delineate and categorize the cognitive processes involved in these activities. The proposed educational system seeks to address challenges identified by Physics Education Research (PER), which offers valuable insights into how individuals learn physics and provides strategies to enhance the overall quality of physics education. Our specific focus revolves around two crucial aspects: concentrating on the conceptual understanding of physics concepts and processes, and fostering knowledge integration and coherence across various physics topics. These aspects are deemed essential for cultivating enduring knowledge and facilitating practical applications in the field of physics.

The fast pace of technological evolution, pushed by the Industry 4.0 and accelerated by the COVID-19, is in contrast with the slow change of the education institutions. Indeed, they struggle to promptly update their programs to be aligned with the labour market needs. In such a dynamic environment, a rethinking of the education design process is fundamental to support schools, universities and learning centres through a faster digitalization of their services. The emerging perspectives in curriculum design propose the use of data to develop and deliver better courses to students taking advantage of technological evolution. In this paper, we attempt to present a framework for using data as a support of education design, with a focus on textual data. The aim is to delineate a framework to exploit Natural Language Processing (NLP) and Named Entity Recognition (NER) techniques in data-driven strategies for designing courses. To support these techniques, we disclose a map of the most relevant concepts that can aid education design and can be mined with NLP.