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Item Dirección y tutorización de tesis doctorales(Grupo GRIAL, 2026-06-02) García-Peñalvo, Francisco JoséCurso impartido en el ICE de la Universidad Politécnica de Madrid (ETSI Caminos) el día 2 de junio de 2026, dentro del Plan de Formación del Profesorado del Curso 2025-2026, con una duración de 2 horas. En este seminario se plantea la dirección doctoral como una práctica de gobierno académico que combina supervisión científica, cumplimiento normativo y acompañamiento profesional. El seminario parte del marco regulador del doctorado en España, con especial atención al Real Decreto 99/2011 y sus modificaciones, así como a la normativa universitaria, la escuela de doctorado y los requisitos propios de cada programa. La idea central es que no se acepta solo un tema de tesis, sino una relación de trabajo viable, regulada y evaluable. El curso organiza el proceso de dirección en trece bloques. Comienza con el marco normativo, el tiempo de permanencia y la entrevista inicial, entendida como un filtro para valorar el encaje científico, la motivación, los recursos, la disponibilidad, las expectativas, los riesgos éticos y la compatibilidad de los estilos de trabajo. A partir de ahí, se aborda el acuerdo de supervisión, con reglas explícitas sobre reuniones, entregas, revisión de textos, autoría, datos, confidencialidad, IA y resolución de conflictos. La presentación concede un espacio específico a los roles del director/a, tutor/a, comisión académica y doctorando/a, así como al diseño estratégico de la codirección, que debe justificarse por su valor real y no por cortesía académica. El primer año aparece como un momento decisivo: debe elaborar un plan de investigación, un plan de formación personal y un documento de actividades que permitan hacer visible el progreso. Se profundiza en la construcción de la tesis desde la idea hasta la estructura: estado de la cuestión, preguntas, objetivos, hipótesis, metodología y evidencias. La revisión bibliográfica se presenta como una tarea continua, orientada a situar la tesis y justificar su aportación, mientras que las evidencias de progreso incluyen publicaciones, congresos, borradores, datos, código, estancias, formación e informes de reuniones. La presentación también incorpora internacionalización, cotutela, mención industrial, ciencia abierta, ciencia ciudadana e IA generativa. En estos apartados se insiste en la planificación temprana, la transparencia, la trazabilidad, la protección de datos y la responsabilidad humana. Finalmente, el depósito y la defensa se entienden como fases que se preparan desde el inicio: informes externos, subsanación, control de originalidad, propuesta de tribunal, depósito y defensa pública. El seminario culmina con un kit operativo de plantillas para aceptar una dirección, entrevistar al doctorando, protocolizar la codirección, hacer seguimiento anual, planificar la formación y gestionar datos de investigaciónItem AI Governance Strategies: A University Perspective(GRIAL Research Group, 2026-05-13) García-Peñalvo, Francisco JoséKeynote at the T4E Transformational Leadership Programme, held 13-14 May 2026 in the University of Alicante, Spain. This keynote argues that universities must move beyond viewing artificial intelligence as a mere technological trend and recognise it as a core challenge for institutional governance, digital transformation, and academic responsibility. The presentation begins by framing digital transformation and AI as key terms in higher education government, but immediately questions a technology-centred view. Digital transformation is presented not only as the optimisation of processes through technology, but as a change in mindset, operating models, and institutional culture. Its central element is people, not tools. The talk then defines the real challenge for universities: rethinking digital transformation from digitising processes to governing AI-enabled sociotechnical ecosystems with meaningful human oversight. AI is shown as affecting the three main university functions: teaching, administration, and research. In teaching, it enables personalised learning and engagement; in administration, automation and efficiency; and in research, data analysis and discovery acceleration. However, the presentation stresses that AI also creates risks: bias, opacity, legal non-compliance, privacy breaches, academic integrity concerns, dependence on third-party providers, and uneven access. A major section focuses on responsible AI adoption through the Safe AI in Education Manifesto, whose principles include human oversight, confidentiality, alignment with educational strategies and didactic practices, accuracy, explainability, comprehensible interfaces, ethical model training, and transparency. These principles map to university governance strategies: human-oriented, infrastructure-oriented, and a governance/assurance layer. The presentation also highlights the need for ethical AI policies, critical AI literacy, communities of practice, and shared good practices. The keynote further explores the strategic dilemma between relying on third-party proprietary tools and developing one’s own infrastructure based on open LLMs. It argues that there is no single best option: universities must evaluate privacy, cost, internal capacity, transparency, auditability, deployment speed, and strategic autonomy. In-house intelligent applications, such as learning assistants, are presented as examples of governed institutional services. The closing message is that AI governance must be strategic, participatory, and ethical. Universities should not merely adopt AI systems but build an AI-augmented academic culture grounded in values, critical engagement, institutional responsibility, and human-centred innovation.Item El papel de las instituciones para garantizar la equidad en el conocimiento y en el acceso a la inteligencia artificial(Grupo GRIAL, 2026-06-18) García-Peñalvo, Francisco JoséParticipación en la sesión “Equidad en el conocimiento e IA: ecosistemas colaborativos y soberanía informacional en la adopción de la IA generativa”, organizada por la Knowledge Equity Network (KEN), la Open Education Latin America (OELATAM) y la Universidad Nacional del Sur (UNS) de Argentina, celebrada online el 18 de junio de 2026, con una duración de una hora. Mi intervención sitúa la equidad en el conocimiento y el acceso a la inteligencia artificial como una responsabilidad institucional, no como una consecuencia automática de la disponibilidad tecnológica. El punto de partida es que las universidades deben repensar su transformación digital: ya no basta con digitalizar trámites o incorporar herramientas aisladas, sino que deben gobernar ecosistemas sociotécnicos habilitados por la Inteligencia Artificial (IA), con supervisión humana significativa, criterios de calidad académica y rendición de cuentas. Desde esta perspectiva, la IA puede contribuir a la excelencia educativa en la docencia, la administración y la investigación, pero solo si se integra con sentido pedagógico, garantías éticas y capacidad institucional. El Manifiesto para una IA segura en la educación ofrece un marco operativo basado en siete principios: supervisión humana, confidencialidad, alineación con las estrategias educativas y las prácticas didácticas, precisión y explicabilidad, interfaces comprensibles, formación ética y transparencia. Estos principios deben traducirse en políticas, procesos y decisiones de gobernanza que conecten la innovación con la equidad. Un eje central de la intervención es la alfabetización crítica en IA generativa. La formación de estudiantes, profesorado y personal de gestión no debe limitarse al manejo instrumental de herramientas, sino que debe orientarse a verificar antes de adoptar, proteger la privacidad, promover la inclusión, mantener explícita la agencia humana y documentar los procesos de toma de decisiones. Las comunidades de práctica y la compartición de buenas prácticas son claves para construir conocimiento colectivo, contextualizado y transferible. También se aborda la tensión entre la dependencia de productos externos y el desarrollo de infraestructuras propias basadas en modelos abiertos. No existe una única opción óptima: cada institución debe evaluar la privacidad, la protección de datos, los costes, la capacidad interna, la velocidad de despliegue, las necesidades de personalización, la transparencia, la auditabilidad y la autonomía estratégica. En muchos casos, las estrategias híbridas serán las más realistas, siempre que estén cuidadosamente gobernadas. Finalmente, se presentan los asistentes LAMB como ejemplo de infraestructura abierta para crear asistentes de IA integrados en sistemas de gestión del aprendizaje. Este enfoque permite desplegar asistentes vinculados a bases de conocimiento institucionales, con control docente, trazabilidad, revisión de interacciones y orientación al aprendizaje. La tesis central es que la IA generativa puede servir a la equidad si las universidades asumen la soberanía informacional, la gobernanza responsable, la formación crítica y la colaboración abierta como condiciones para una adopción con un impacto sostenible, verificable y alineado con la misión pública.Item How to Conduct a Systematic Literature Review?(GRIAL Research Group, 2026-06-22) García-Peñalvo, Francisco JoséTraining course for doctoral students organized by the ICE and the EID of the Polytechnic University of Madrid, lasting 4 hours, taught on June 22 and 23, 2026, in online format. The main purpose of this training activity is to introduce researchers to conducting systematic literature reviews (SLRs). It begins with the need to conduct literature reviews to understand the state of the art, distinguishing between the concepts of a literature review and a systematic review. Several types of systematic reviews are presented, with special emphasis on the two most commonly used approaches: systematic literature reviews and literature mapping studies. Once the basic concepts are established, the main methodological frameworks for conducting systematic reviews are introduced. The three major phases of a systematic review (planning, conducting, and reporting) are described in detail. The course concludes with a simple case study of a systematic literature mapping review. The specific objectives of the course are: 1. To know what is meant by systematic review of the literature. 2. Evaluate the effort required to conduct a systematic review of the literature. 3. Plan a systematic review of the literature. 4. Conduct a systematic review of the literature. 5. Capture the work done in a report or research article. The contents of the seminar are: 1. Introduction to systematic reviews 2. Systematic literature reviews vs. Literature mapping reviews and Scoping reviews 3. Methodological frameworks of reference for systematic literature reviews 4. Planning phase 5. Conducting the review phase 6. Reporting phase 7. Case study 8. Bibliometrix 9. Collection of workflows and tools for conducting literature reviews 10. ConclusionsItem 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 Joséhere 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.Item 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, DavidThe 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.Item 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 Joséhe 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.Item 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 LuisUser 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.Item 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 LuisArtificial 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.Item 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.