Blockchain technologies are increasingly embedded in everyday digital experiences, yet their underlying mechanisms remain opaque to young learners. We present Blockchain@School, an unplugged board-game-based toolkit designed to make concepts such as distributed consensus, immutability, and cryptographic security tangible via collaborative play. Developed through an iterative design process with educators, Blockchain@School combines physical blocks and cards with a lightweight web application for real-time validation. We evaluated the toolkit in a series of 90-minute workshops with more than 300 primary and middle school learners (ages 9–13), using pre/post questionnaires, structured observations, and teacher feedback. Findings indicate significant improvements in learners’ understanding of blockchain principles, alongside high engagement and effective collaboration. Teachers reported that the activities can be easily integrated into existing curricula and replicated in autonomy. Our work demonstrates how unplugged, game-based learning can lower entry barriers to emerging technologies, foster digital citizenship, and support scalable approaches to teaching complex computational ideas in K–12 education.

Storytelling is a widely explored educational practice. Within Human–Robot Interaction (HRI), robots are extensively employed for storytelling. In parallel, non-robot phygital artefacts — physical objects augmented with digital technologies — have been considered for supporting interaction and collaboration. However, systematic comparisons between these approaches remain limited. This paper presents a systematic review of 135 out of 1,040 studies (2014–2025) involving participants under 18. The review compares robots and non-robot phygital artefacts used in storytelling. Studies were coded across analytic lenses on media, participation, collaboration, learning goals, and Artificial Intelligent support, enabling age-stratified comparison. Findings reveal that non-robot phygital artefacts more consistently foster peer collaboration and creative ownership, while robots primarily contribute social presence and novelty, often structuring participation sequentially. The review highlights implications for future HRI design, suggesting that robots should complement alternative phygital artefacts by promoting openness, adaptability, and collaboration in storytelling

While the FAIR principles (Findability, Accessibility, Interoperability, and Reusability) and data quality dimensions are widely used to evaluate Linked Data, their interdependencies remain largely unexplored.This paper is grounded on a systematic integration of these two frameworks by mapping data quality dimensions to FAIR sub-principles, revealing how individual features-such as endpoint availability, metadata richness, or use of standard vocabularies-can simultaneously contribute to multiple FAIR goals.Building on this mapping, this paper reports a large-scale, datadriven, longitudinal study of 1, 445 datasets from the LOD Cloud extending KGHeartBeat, an open-source quality assessment framework.This paper quantifies FAIRness at the sub-principle level and computes correlation patterns across five temporal snapshots and nine topical domains.The reported findings reveal that most correlations are positive and statistically significant but vary across time and domain, with only a few stable or persistent relationships.Strong inter-principle correlations-such as those linking metadata standards and security transparency-emerge over time, while intraprinciple coherence is often weak.These insights offer concrete guidance for improving FAIR compliance, highlight the importance of domain-aware evaluation, and support the development of more holistic and reproducible FAIR assessment strategies for Linked Data ecosystems.

Abstract The huge and ever-increasing amount of publicly available data is shaping the data-driven society that citizens are encouraged to tame. It requires future generations, i.e., current learners, to acquire data literacy skills to make informed decisions. Towards this direction, we present a data literacy workshop involving more than $$\varvec{150}$$ 150 high school learners focused on co-creating Open Data via a digital environment and authoring data stories while evaluating their engagement and learning. Results show that participants are, on average, engaged during the in-person stages of the workshops, independently by gender, and they are mainly interested in collaborative activities, hands-on, and public presentations. This experiment confirm that learning is positively correlated with engagement, which aligns with the literature. However, further efforts should be invested in letting learners master data literacy skills while increasing their interest.

Storytelling is an effective way of communicating information and knowledge, and it is widely adopted in heterogeneous contexts, from education by improving critical thinking and enhancing learning practice, to journalism by encouraging coherent stories of news supported by graphical representations. However, storytelling platforms seem to be targeted to a specific audience without showing how they can be adapted to heterogeneous needs, from class support in education to mechanisms to overcome the syndrome of the white page. In this article, we propose Novelette, a digital storytelling environment, and we show how it can be applied in heterogeneous contexts and by the different target audience. We present Novelette operating mechanisms, its architecture, and we overview different use cases, from tales creation Rodari style to data- and media-stories. By use-cases, we desire to make evident that the same platform can generate stories engaging for any target audience.

Querying Linked (Open) Data (LOD) by directly using SPARQL could be a painful task for most potential users of semantic data. Several approaches have been proposed to help users in query formulation. They succeed in hiding the underlying complexity but exploit only the monological - individual - approach. Information seeking and retrieval is not merely an individual effort, but it inherently involves various collaborative activities. For this reason, our proposal is to facilitate the exploitation of LODs by wrapping the querying and visualization tool in a social platform environment. In this way, we enable the dialogical approach. Moreover, since the users can collaboratively create datasets and visualizations, and reuse them also out of the social platform, we reach the trialogical learning. In this paper, we present our design approach, our tool, and related tests.