This paper presents ESQAD (Educational Spanish Question-Answer Dataset), an open-access resource for question-answer generation (QAG) in Spanish aligned with national curricula. ESQAD comprises 6,980 expert-curated exam questions, 340 literary comprehension items, 47 legal FAQs, and 913 LLM-generated pairs validated in academic settings. The dataset covers diverse linguistic and cognitive features, including subject classification, question typologies, and Bloom-level complexity. We analyze lexical diversity, cognitive intent, and structure across subsets. The automatically generated portion was created with Bloom-guided prompts and large language models (LLMs), and evaluated through both computational metrics and classroom validation. Results show that cognitive-level control is reliable for factual recall but only partial for higher-order reasoning. All resources, including dataset, code, and validation tool, are publicly available. ESQAD provides a structured benchmark for curriculum-aware QAG in under-resourced languages, with applications in educational NLP, question generation, and adaptive learning.

This work proposes an approach based on Large Language Models (LLMs) for creating digital collections from free-text medical reports. The approach uses instruct LLMs to extract relevant clinical terms from these reports, as well as to catalog them using the extracted terms. The cataloged reports are then integrated into a digital collection management platform, enabling further curation by clinical experts. To confirm the feasibility of the approach, we used various models associated with DeepSeek, as well as a coding model developed by Alibaba, and the experimental Clavy reconfigurable collection management platform to handle the resulting collections. The preliminary evaluation results demonstrate the feasibility of the approach, even without relying on external services that could compromise data privacy in real-world scenarios, or on particularly expensive dedicated hardware.

This research-to-practice full paper describes a case study conducted at the Escuela Técnica Superior de Ingeniería de Sistemas Informáticos at the Universidad Politécnica de Madrid, within the subject of Software Engineering Fundamentals. In this study, physical rewards, called MeriCOINs, were introduced as a way to gamify the subject. These rewards were progressively awarded to incentivize students throughout the course. These MeriCOINs were given as prizes within educational games and multiplechoice exam simulations presented as contests through the Wooclap tool. Students could use these MeriCOINs as part of the course process for queue advancement and late submissions. A notable improvement in student motivation and performance was achieved according to the perceptions of both students and teachers. A process for manufacturing and editing MeriCOINs using sustainable and low-cost processes was designed, which not only allow for easy and inexpensive implementation of the rewards but also establish them as a valid educational approach that can be replicated in different courses and contexts.

The information produced during clinical practice is of unquestionable value from a didactic point of view. Thus, there is a need for methods to generate educational content from this information in an agile, effective, and automatable way. This paper proposes a method that transforms clinical cases coded in the HL7 clinical interoperability standard FHIR into educational content compliant with the IMS CP standard. To demonstrate the feasibility of the method, we describe an implementation using the collaborative clinical case design platform Mosaico as the FHIR case provider and the Clavy digital collection management platform to transform these FHIR cases into IMS CP e-Learning content, ready to be deployed in a Learning Management System (LMS).

In this paper, we describe how to transform unstructured free-text clinical corpora, made from reports written in natural language and complementary assets (e.g., medical images, laboratory results, etc.), into collections of digital objects compatible with Clavy, a tool for managing reconfigurable digital collections. It will allow healthcare experts to subsequently reorganize the resulting collections to adapt them to their specific needs. The transformation will be achieved through the use of MetaMap, a robust tool for mapping clinical texts into the UMLS (Unified Medical Language System) thesaurus. Thus, by processing reports with MetaMap, we will be able to extract a significant set of corpus-specific UMLS terms, grouped according to relevant semantic types, which will be used to support a preliminary organization of the resources in the Clavy collection. We illustrate the viability of the approach with the generation of a reconfigurable Clavy collection from the Indiana Chest X-ray corpus of radiology reports and images. On the basis of this case study, we also discuss the strengths and weaknesses of the approach proposed.