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    Model of Characterization of Teamwork Competence Based on Three Types of Capabilities
    (Springer, 2025-07-15) Fidalgo-Blanco, Ángel; Sein-Echaluce, María Luisa; Fonseca, David; García-Peñalvo, Francisco José
    here are various teamwork models with different orientations regard-ing the conceptual model, member involvement, evidence management, and even the training process for acquiring teamwork-related skills. This research defines a hybrid model that integrates the two main theoretical models (focused on group achievements and team member involvement) and an open-box method (with continuous generation and verification of both group and individual evidence). Therefore, teamwork competence is associated with a set of capabilities of dif-ferent types, classified into three main categories: group, individual, and general (soft skills), which are related to teamwork but not exclusive to it. This paper also presents the evidence that allows for continuous and transparent training and evaluation of these three types of capabilities.
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    Integrating Individual and Collective Skills: A Rubric-Based Model for Teamwork Competence Assessment
    (Springer, 2024-06-29) Sein-Echaluce, María Luisa; Fidalgo-Blanco, Ángel; García-Peñalvo, Francisco José; Fonseca Escudero, David
    The competence of teamwork comprises a set of skills that enable the assessment of teamwork evolution (collective skills) and the involvement of each team member (individual skills). In most research works, these skills are grouped without making this distinction between collective and individual skills. In this study, collective skills are associated with the different phases that constitute the evolution of teamwork, allowing for the identification of the precise moment when such a skill should be applied. Individual skills are applied in all phases of teamwork, as they measure individual involvement and responsibility, along with the competencies necessary at an individual level to develop teamwork. This work presents a rubric that associates phases, evidence, technology, and indicators and allows educators to measure the degree of acquisition of each and collective skill. The method used for the development of teamwork has been the Comprehensive Training Model of the Teamwork Competence, which supports both the continuous and transparent creation of evidence of teamwork development by the teams and each of their members, as well as the continuous monitoring of this development by educators.
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    Enhancing Learning Assistant Quality Through Automated Feedback Analysis and Systematic Testing in the LAMB Framework
    (Springer, 2025-06-22) Alier-Forment, Marc; Pereira-Valera, Juanan; Casañ-Guerrero, María José; García-Peñalvo, Francisco José
    he Learning Assistant Manager and Builder (LAMB) is an open-source software framework that lets educators build and deploy AI learning assis-tants within institutional Learning Management Systems (LMS) without cod-ing expertise. It addresses critical challenges in educational AI by providing privacy-focused integration, controlled knowledge bases, and seamless deploy-ment through standard protocols. This paper presents major enhancements that enable systematic quality assurance and continuous improvement of these learning assistants. The new LAMB includes mechanisms for structured feedback on real-world assistant behavior, transforming it into a test suite with curated prompts and expected correct or incorrect responses. When changes are made—such as prompt engineering, retrieval-augmented generation optimization, or knowledge base expansions—this suite enables automated validation of their impact. A key innovation is using frontier large language models (LLMs) to evaluate responses automatically, generating detailed reports that reveal improvement areas and confirm performance gains. This systematic feedback-driven testing fosters continuous refinement while preserving quality standards. Validation studies show measurable boosts in reliability and consistency. In various educational contexts, the framework identifies edge cases, maintains con-sistency across iterations, and provides actionable insights. Automated testing is especially beneficial for assistants with extensive knowledge bases and complex interaction patterns. This work advances educational AI by providing a robust methodology for quality assurance and ongoing improvement of learning assistants. Its structured feedback and automated evaluations ensure alignment with educational goals while refining assistants over time. The enhanced LAMB framework offers a scalable and reliable solution for educators aiming to integrate AI-driven support into their LMS environments.
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    Refactoring User Interfaces Through a Data-Driven Framework: a Case Study in the Health Domain
    (IEEE, 2023-10-16) Vázquez-Ingelmo, Andrea; García-Holgado, Alicia; García-Peñalvo, Francisco José; Pérez-Sánchez, Pablo; Antúnez-Muiños, Pablo; Sánchez-Puente, Antonio; Vicente-Palacios, Víctor; Dorado-Díaz, Pedro Ignacio; Sánchez, Pedro Luis
    User interfaces (UIs) play a crucial role in defining user experiences and influencing the success of software products. While UI design has traditionally been subjective and iterative, data-driven approaches are becoming increasingly popular to ensure that Uis meet user needs and expectations. However, contextual factors such as the application domain can present challenges for designing Uis that are both effective and efficient. This is particularly true in the health domain, where Uis must be adapted to specific tasks and user expertise to maximize the support provided by software systems. Moreover, the urgency of delivering fully functional systems in short periods can relegate UI design to a second plane. This paper presents a framework proposal for refactoring and improving Uis using a data-driven approach, providing an efficient and systematic methodology to address not solved UI issues introduced during previous software development processes. The proposed framework has been successfully applied to two medical platforms, demonstrating the importance of data-driven approaches for UI refactoring in domains with particular necessities.
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    D-AI-COM: A DICOM Reception Node to Automate the Application of Artificial Intelligence Scripts to Medical Imaging Data
    (Springer, 2024-05-01) Vázquez-Ingelmo, Andrea; García-Holgado, Alicia; García-Peñalvo, Francisco José; Pérez-Sánchez, Pablo; Sánchez-Puente, Antonio; Vicente-Palacio, Víctor; Dorado-Díaz, Pedro Ignacio; Sánchez, Pedro Luis
    Artificial Intelligence (AI) has proven to be useful in several fields. The medical domain is one of the fields that benefits from the application of AI methods to automate and ease complex tasks including disease detection, segmentation, assessment of organ functions, etc. However, applying these kinds of methods to the variety of data formats involved in health contexts is not trivial. It is necessary to provide technologies that enable non-expert users to benefit from AI applications. This work presents a platform that acts as a DICOM reception node with the goal of automating the application of AI algorithms to medical imaging data. This platform is set to ease the process applying AI to their DICOM images by making the whole process transparent and straightforward for users without AI-related or programming skills.
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    AI-Powered DICOM Image Segmentation: A Collaborative Platform for Continuous Expert Feedback
    (Springer, 2026-03-01) Santos-Blázquez, Pablo; Vázquez-Ingelmo, Andrea; García-Holgado, Alicia; García-Peñalvo, Francisco José; Sánchez-Puente, Antonio; Sánchez, P. L.
    his work presents the development of an interactive web platform that integrates deep learning techniques for the segmentation of cardiac ultra-sound (echocardiogram) images. The platform incorporates a Picture Archiving and Communication System (PACS) to facilitate the seamless visualization, anno-tation, and automated processing of DICOM images. The web platform features an intuitive interface that allows healthcare professionals to interactively annotate medical images, providing feedback that directly informs model improvements. The system’s retraining workflow ensures that AI-driven segmentation remains adaptable to real-world clinical needs. These findings underscore the importance of iterative AI model refinement through expert feedback, paving the way for more reliable and personalized medical image analysis.
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    Management and Application of AI to DICOM Image Processing: A Systematic Mapping Literature Review
    (Springer, 2024-07-15) Fraile-Sanchón, Rubén; Vázquez-Ingelmo, Andrea; García-Peñalvo, Francisco José; García-Holgado, Alicia
    Artificial intelligence (AI) has the potential to bring unprecedented benefits to humankind. Therefore, it is worth investigating how to maximize these benefits while avoiding potential pitfalls. Given this context, the first task necessary to assess the potential of this approach is to understand the management landscape and the application of AI to DICOM image processing. In this case, the researchers employ a systematic mapping review. This paper presents this process and its main findings. 35 studies have been selected from a total of 154 analyzed. From them, in addition to obtaining a clear view of the application of AI to DICOM images, we can also conclude that pre-trained AI algorithms are used in a higher amount than non-trained algorithms in terms of DICOM image usage.
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    Una experiencia de integración segura de inteligencia artificial generativa en educación superior mediante asistentes de aprendizaje
    (Observatorio de Innovación Educativa y Cultura Digital (ODITE) y Asociación Espiral, Educación y Tecnología, 2026-04-26) Alier, Marc; Casañ, María José; Llorens, Ariadna; Pereira, Juanan; García-Peñalvo, Francisco José
    Este artículo presenta LAMB (Learning Assistant Manager and Builder), framework de código abierto que permite al profesorado crear asistentes de aprendizaje basados en inteligencia artificial Generativa (IAGen) sin necesidad de programación. A diferencia de chatbots genéricos como ChatGPT, estos asistentes están controlados por el docente, se basan en fuentes de información seleccionadas y se integran de forma segura en plataformas como Moodle. La experiencia se desarrolló con 47 estudiantes de Ingeniería Informática en la FIB y EPSEVG (UPC). Utilizaron un asistente de aprendizaje para realizar un análisis PESTLE sobre el robot Optimus de Tesla. Los resultados muestran una valoración muy positiva por parte del alumnado, destacando la utilidad para encontrar información relevante, la calidad de las respuestas y el valor añadido del acceso a fuentes verificables. Este trabajo demuestra cómo la IA puede ponerse al servicio del aprendizaje centrado en el estudiante, respetando principios de privacidad, seguridad y soberanía pedagógica.
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    Visibilizar el ecosistema de políticas abiertas para democratizar el conocimiento
    (Octaedro, 2025-10-01) Coria Tinoco, Raúl; Delgado Fabián, Mónica; García-Peñalvo, Francisco José; Glasserman-Morales, Leonardo David; González-Pérez, Laura Icela; Rodríguez Palacios, Sara María del Patrocinio; Sánchez Reyes, Miriam Guadalupe; Tenorio-Sepúlveda, Gloria Concepción; Valencia González, Gloria Clemencia; Valenzuela Arvizu, Siria Yahaira; Viñoles Cosentino, Virginia
    En una era de constante cambio, es esencial potenciar el acceso abierto al conocimiento para transformar la educación y la ciencia a nivel global. Ante la falta de transparencia y espacios que canalicen información sobre políticas de acceso abierto surge el Observatorio OPALO (Open Policies for ALl Observatory), que busca mejorar la transparencia y fomentar un ecosistema inclusivo, accesible y equitativo. Su meta es identificar brechas, buenas prácticas y oportunidades para desarrollar políticas que faciliten el acceso a recursos educativos y científicos, especialmente en regiones en desarrollo. El impacto esperado incluye: a) en educación, promo-ver el acceso igualitario a recursos abiertos mediante tecnologías emergentes y metodologías colaborativas; b) en ciencia, impulsar la colaboración global y la reproducibilidad científica a través de políticas de acceso libre y el intercambio de datos; c) en gobernanza, fomentar políticas basadas en evidencia y desarrollar infraestructuras de datos accesibles; y d) crear un modelo de madurez del conocimiento abierto que permita a las instituciones autoevaluarse y mejorar sus prácticas. El equipo, conformado por profesionales de Colombia, España y México, cuenta con experiencia interdisciplinar en ingeniería sostenible, tecnología educativa, ciencia abierta y políticas de conocimiento. Su enfoque en metodologías transformadoras y soluciones accesibles, así como su participación en iniciativas internacionales como la Cátedra UNESCO/ICDE, asegura un impacto global, inclusivo y sostenible.
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    Support for specific training plans in the Algerian university system: new teachers and online teaching
    (Editorial Comares, 2022-01-01) López-Aguado, Mercedes; Hoyuelos Álvaro, Francisco Javier; García-Peñalvo, Francisco José
    Project PAPER. Support for specific training plans in the Algerian university system: new teachers and online teaching