Open Access

Background: The use of generative AI, as represented by ChatGPT, holds promising potential for nursing education. This manifests itself in various areas, including personalized learning, simulation training and teaching process support. However, its integration requires careful consideration of ethical implications, adaptation of curricula and a high level of digital competence on the part of teachers. Only in this way can potential risks, such as the distortion of knowledge, bias and educational inequalities, be avoided. Methods: Relevant publications were identified between 2019 and 2025 as part of a comprehensive literature search in the specialist databases PubMed, Embase, CINAHL and Scopus. The search was conducted using combined search terms that included the terms “generative AI”, “ChatGPT” and “nursing”. After removing duplicates and screening (PRISMA-guided), 140 full texts were analysed and divided into two publications. This rapid overview focuses on the topic of generative AI in nursing education. Results: As part of the analysis of the included studies, five thematic areas were identified, which were divided into the categories of nursing education, competence development and nursing skills, implementation possibilities, examination quality and ethical considerations, and evaluated. A key theme is the dual potential of this technology: it can enrich learning through features such as virtual tutors and improved exam preparation, but it also requires critical consideration of ethical issues such as plagiarism, data bias and the need for human oversight. Outlook: In this context, the conclusion emphasises the urgent need to adapt curricula and provide targeted further training for teachers so that GenAI can be used responsibly and effectively—rather than, as is often the case at present, by banning it altogether.

Durante il XV e XVI secolo il plurilinguismo diventa un tema rilevante nei manoscritti che circolano tra le corti e le botteghe di scrittura europee. Il contatto interlinguistico si articola nelle molteplici varietà italo-romanze diffuse nell’Italia e nella Francia medievale e rinascimentale. Il manoscritto Mscr.Dresd.Oc.56, conservato a Dresda e da collocare nel dibattito cinquecentesco, ospita la traduzione francese del Libro del Cortegiano (1528) di Baldassare Castiglione (1478-1529). Si tratta di una delle più antiche traduzioni francesi dell’opera, realizzata ancora prima della versione a stampa (1537). Nel manoscritto il presunto autore, Jacques Colin (scomparso nel 1547), non funge soltanto da mero traduttore, ma propone anche riflessioni sulla corretta resa di alcuni passi della traduzione, ad esempio a proposito di giochi di parole e modi di dire italiani e francesi. L’articolo, che prende in esame una selezione di passi tratti dal testo di partenza italiano e dalla sua traduzione francese, adotta una prospettiva filologica e linguistica contrastiva. Il contributo offre inoltre alcune considerazioni sulla riflessione linguistica svolta dal traduttore nell’ambito del processo di produzione del Mscr.Dresd.Oc.56.

Additive Manufacturing (AM), also known as rapid prototyping or 3D printing, is widely used across various industries, including medical products and automotive spare parts. The COVID-19 pandemic has further accelerated its adoption to address supply chain disruptions caused by shortages in production resources and logistics constraints. However, as AM integrates into supply chains, structural changes in nodes and data flows create new challenges in information sharing and data standardization. Ontologies have proven effective in enhancing data interoperability and improving information quality through semantic modeling. Despite this, a comprehensive approach that combines AM and logistics ontologies to address cross-domain challenges remains underexplored. This study develops an ontology-based supply chain model for AM by integrating existing AM and logistics ontologies. Using the Design Science Research Methodology (DSRM), the proposed ontology is constructed and instantiated with a sample dataset for validation. The results provide a foundational framework for improving data management and coordination in AM supply chains.