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Author: search.filters.author.Vázquez-Ingelmo, Andrea

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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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    Más allá de las hojas de cálculo: creando flujos para la definición, validación e interoperabilidad de variables clínicas
    (AIPO, 2015-12-30) Vázquez-Ingelmo, Andrea; Nieto-Campo, Islem Román; García-Holgado, Alicia; García-Peñalvo, Francisco José; Sánchez-Puente, Antonio; Sánchez, Pedro L.
    Las hojas de cálculo siguen siendo el estándar para definir y recoger variables clínicas, pero su flexibilidad las vuelve frágiles y propensas a errores. Presentamos un rediseño centrado en la persona del flujo de definición de variables dentro de una plataforma que integra datos clínicos estructurados e imágenes médicas. La propuesta sustituye la creación manual del esquema en procesadores de hojas de cálculo por un editor web interactivo y la generación automática de plantillas validadas a partir del esquema interno de la plataforma, reduciendo la carga cognitiva y previniendo errores de formato y semántica, con retroalimentación accionable en la carga. El flujo BPMN actualizado conecta el modelado de variables con una entrada de datos guiada y validaciones, cumpliendo heurísticas clave como visibilidad del estado, prevención y recuperación de errores. Entre las limitaciones persiste la entrada de datos fuera de línea; como trabajo futuro se plantean estudios de usabilidad, interoperabilidad semántica y asistencia con inteligencia artificial para sugerir variables.
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    AI-Assisted UML Learning: Toward Ethical Integration of Generative Artificial Intelligence in Software Engineering Education
    (Servicio de Publicaciones Universidad de Zaragoza, 2025-06-11) Vázquez-Ingelmo, Andrea; Castillo-Salguero, Cristian Alejandro; García-Peñalvo, Francisco José; Conde, Miguel Ángel; García-Holgado, Alicia; Therón, Roberto
    This paper presents a web-based chatbot platform designed to support the teaching of UML domain modeling in software engineering education. Leveraging locally executed generative AI (DeepSeek-v2), the tool provides students with anonymized, interactive feedback and problem generation capabilities while preserving data privacy and promoting ethical AI use. The platform fosters autonomy, digital literacy, and critical reflection, offering a scalable and sustainable solution for integrating AI into higher education.
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    Chatbot ético y supervisado para la enseñanza de UML: Una experiencia en Ingeniería del Software
    (Universidad de Salamanca. Instituto Universitario de Ciencias de la Educación, 2025-10-31) Vázquez-Ingelmo, Andrea; García-Holgado, Alicia; García-Peñalvo, Francisco José; Therón-Sánchez, Roberto; Conde-González, Miguel Ángel
    Esta buena práctica presenta el diseño, implementación y evaluación de un sistema basado en inteligencia artificial para apoyar el aprendizaje del modelado conceptual con diagramas de clases UML. Desarrollado en dos asignaturas del Grado en Ingeniería Informática de la Universidad de Salamanca (curso 2024-2025), el sistema funciona de forma local, ética y segura, mediante un chatbot especializado supervisado por el profesorado. Su objetivo es ofrecer un entorno interactivo que facilite la comprensión del modelado UML, promoviendo el razonamiento autónomo y la reflexión crítica sobre el uso de la IA. La herramienta permite generar enunciados, subir soluciones en imagen y recibir retroalimentación asíncrona, todo de forma anónima y en servidores institucionales. Durante la fase piloto, se registraron 110 mensajes de 13 estudiantes, con resultados positivos en facilidad de uso y potencial de adopción según la escala SUS. Sin embargo, se identificaron desafíos técnicos (sobrecarga, errores en español) y una baja adhesión a los requisitos de trazabilidad en el uso de IA. La experiencia evidencia la importancia de reforzar la alfabetización digital crítica, aclarar el alcance funcional y acompañar pedagógicamente el uso de estas herramientas.
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    From spreadsheets to interfaces: redesigning clinical variable definition through interactive workflows
    (CEUR-WS.org, 2025-09-03) Vázquez-Ingelmo, Andrea; Nieto-Campo, Islem Román; García-Holgado, Alicia; García-Peñalvo, Francisco José; Sánchez-Puente, Antonio; Sánchez, Pedro L.
    Spreadsheets remain a common but fragile foundation for clinical data management, often leading to errors and inefficiencies in defining and collecting structured variables. This paper presents a user-centered redesign of the variable definition workflow in a platform for managing structured clinical data and medical images. The proposed solution replaces manual spreadsheet-based schema creation with an interactive web interface that enables users to define, categorize, and reuse variables more effectively. It also introduces automated generation of validated spreadsheet templates based on the platform’s internal schema, reducing the likelihood of formatting and semantic errors during data entry. A revised workflow illustrates the improved process, and the system addresses key usability issues previously identified through heuristic evaluations. Remaining limitations, such as continued reliance on offline data entry, are discussed, along with future work directions that include usability validation and AI-assisted variable generation.
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    AI-Assisted UML Learning: Toward Ethical Integration of Generative Artificial Intelligence in Software Engineering Education
    (Servicio de Publicaciones Universidad de Zaragoza, 2025-06-11) Vázquez-Ingelmo, Andrea; Castillo-Salguero, Cristian Alejandro; García-Peñalvo, Francisco José; Conde, Miguel Ángel; García-Holgado, Alicia; Therón, Roberto
    This paper presents a web-based chatbot platform designed to support the teaching of UML domain modeling in software engineering education. Leveraging locally executed generative AI (DeepSeek-v2), the tool provides students with anonymized, interactive feedback and problem generation capabilities while preserving data privacy and promoting ethical AI use. The platform fosters autonomy, digital literacy, and critical reflection, offering a scalable and sustainable solution for integrating AI into higher education.
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    Mediated Approach to Addressing Reading Diversity in German Classrooms
    (2025-04-27) Therón, Roberto; Vázquez-Ingelmo, Andrea; García-Holgado, Alicia; García-Peñalvo, Francisco José; Shoeibi, Nastaran
    The LATILL (Level-Adequate Texts in Language Learning) project was initiated to address these educational needs, primarily focusing on German as a Foreign Language (GFL) and Second Language (GSL) teachers. This initiative is particularly timely given the academic commitment to improving reading comprehension within German language curricula. One of the fundamental challenges in language education is the sourcing of suitable authentic texts. Educators often turn to news articles, blogs, or literary excerpts, but these sources may have complex syntactic structures, specialized jargon, or cultural references that exceed the learners’ proficiency levels. Moreover, copyright laws restrict the reproduction and distribution of many high-quality materials, limiting the diversity of texts educators can offer to their students. Recognizing these challenges, LATILL offers a personalized learning platform designed to enhance German language reading comprehension among European youth. Developed around a centralized corpus of texts sourced from public domains and open-access materials, the platform addresses the limitations of traditional teaching methods. LATILL integrates various AI technologies to streamline the creation of educational resources, especially utilizing generative AI to provide real-time translations, summaries, and visual aids. This paper explores the design, implementation, and evaluation of LATILL, with a specific focus on its use of generative AI and human-computer interaction (HCI) to address these challenges. It will highlight the design decisions, the integration of AI features, and the user feedback that informed its iterative development.