Publications
Permanent URI for this collectionhttps://repositorio.grial.eu/handle/123456789/34
Browse
208 results
Search Results
Item Filling the gap in K-12 data literacy competence assessment: Design and initial validation of a questionnaire(Elsevier, 2025-03-01) Donate-Beby, Belén; García-Peñalvo, Francisco José; Amo-Filva, Daniel; Aguayo-Mauri, SofíaAs the integration of AI-powered technologies in education grows, data literacy has become a key competence for educators, shaping their ability to navigate and utilize vast amounts of educational data. This study details the development of the Educators Data Literacy Self-Assessment (EDLSA), a questionnaire designed to assess perceived data literacy among K-12 teachers, focusing on its behavioural implications. The development of the EDLSA was rigorous. It involved an exhaustive qualitative review of frameworks and a pilot test in a teachers' Spanish sample (n = 66) provided relevant insights for refining the instrument. Finally, we conducted a comprehensive statistical analysis, which confirmed the instrument's robust reliability (α = 0.976) in measuring teachers' data management competence. The results of the factorial analysis in piloting primary and secondary education samples led to the readjustment of the proposed dimensions into three categories: comprehensive educational analytics, educational problem-solving through data, and promoting meta-learning students through data and ethical implications. Stemmed from the assessed competencies, the EDLSA instrument provides a comprehensive understanding of the human-computer interaction over data in educational settings. Overall, this self-assessment tool presents robust psychometric properties and a framework definition that paves the way for further development among teachers and researchers.Item Asistentes de aprendizaje basados en inteligencia artificial: Principios de seguridad y experiencias de implementación en educación superior(Dykinson, 2024-12-30) Casañ, M. J.; Alier, M.; Pereira, J.; García-Peñalvo, F. J.El capítulo presenta el impacto y las aplicaciones de la Inteligencia Artificial Generativa (IAGen) en educación superior, centrándose en principios de seguridad y experiencias prácticas. Desde finales de 2022, herramientas como ChatGPT y Dall-E han revolucionado los métodos de enseñanza, promoviendo la personalización del aprendizaje y la automatización de procesos educativos. Sin embargo, estas tecnologías también plantean desafíos, como la privacidad de datos, las "alucinaciones" en las respuestas de los modelos, los sesgos inherentes y la dependencia tecnológica. Para garantizar una implementación segura y ética de la IAGen, los autores proponen siete principios clave: confidencialidad, alineación con estrategias educativas, prácticas didácticas, precisión, comprensión, supervisión humana y entrenamiento ético. Estos principios buscan integrar herramientas de IA de manera alineada con los valores institucionales y las normativas de privacidad. El capítulo también introduce LAMB (Learning Assistant Manager and Builder), un marco de software diseñado para crear asistentes de aprendizaje seguros y personalizados. Estos asistentes, interoperables con sistemas como Moodle, emplean recuperación aumentada por generación (RAG) para combinar datos específicos con la capacidad de los modelos de lenguaje. Un ejemplo práctico de LAMB se ilustra en un curso de negocios donde se utilizó un asistente para realizar análisis PESTLE y DAFO, mostrando una recepción positiva por parte de los estudiantes. Finalmente, se concluye que integrar la IAGen en la educación no solo debe enfocarse en su potencial innovador, sino en asegurar una aplicación ética y responsable, alineada con los objetivos educativos. Herramientas como LAMB ejemplifican cómo la IA puede ser una pieza valiosa y segura en los ecosistemas educativos.Item Safe AI in Education Manifesto. Version 0.4.0(2024-10-08) Alier-Forment, Marc; García-Peñalvo, Francisco José; Casañ, María José; Pereira, Juanan; Llorens-Largo, FaraónThe Safe AI in Education Manifesto outlines ethical principles for integrating AI into educational environments. It emphasizes the need for human oversight, ensuring AI complements rather than replaces educators. Decision-making must remain transparent and appealable, protecting the educational process's integrity. Confidentiality is paramount; institutions must safeguard student data and ensure AI systems comply with stringent privacy standards. AI tools should align with educational strategies, supporting learning objectives without enabling unethical practices or adding complexity. The manifesto calls for AI systems to respect didactic practices, adapting seamlessly to instructional designs without burdening educators or students. It stresses accuracy and explainability, requiring AI outputs to be reliable, transparent, and verifiable. Interfaces must be intuitive, communicating their limitations to foster trust and critical engagement. Ethical training and transparency in AI model development are essential, including minimizing biases and disclosing data sources. The manifesto commits to advancing AI’s potential in education while prioritizing privacy, fairness, and educational integrity, providing a living framework adaptable to technological evolution. It can be signed at: https://manifesto.safeaieducation.org/Item Workshop about developing educative scenarios with GenAI tools(Zenodo, 2024-06-12) García-Carrasco, J.The document outlines a workshop designed for Master’s students in ICT applied to education at the University of Salamanca. Led by Francisco José García-Peñalvo, the workshop aims to explore the application of generative AI (GenAI) tools like ChatGPT in education. The objectives include learning to integrate GenAI in teaching, reflecting on its potential and risks, and designing educational scenarios collaboratively. The eight-hour session is part of a course on "Design and Assessment of Digital Resources." Students, mostly with educational backgrounds, engage in a structured process involving an introduction, AI-focused discussions, and hands-on sessions with ChatGPT. Teams of three work to develop and present educational scenarios using GenAI. Examples of tasks include creating stories for primary school, designing gamified learning activities, or developing subject-specific assessments. The emphasis is on the process over the final product. Teams document prompts and workflows and present findings to facilitate peer discussion on lessons learned, focusing on benefits and challenges. Key takeaways stress the importance of an initial introduction to GenAI, collaborative work, and reflection. The workshop highlights the transformative potential of GenAI in education while advocating for critical engagement with its ethical and practical implications.Item Embracing GenAI literacy in education: A roadmap for empowerment(Zenodo, 2024-06-12) García-Peñalvo, Francisco JoséThe paper discusses the emergence of Generative Artificial Intelligence (GenAI) as a transformative force in education and the necessity of GenAI literacy for both educators and students. GenAI literacy involves understanding generative AI systems, their societal impacts, and ethical implications. It encompasses skills ranging from basic knowledge of how these systems work to critical evaluation and innovative application. For teachers, fostering GenAI literacy requires integrating GenAI concepts into existing curricula without overhauling them, organizing professional development workshops with hands-on training, and forming collaborative learning communities to share best practices. For students, the focus should be on developing critical thinking and ethical reasoning skills, engaging in active-based learning using GenAI tools, and promoting interdisciplinary approaches that span STEM, humanities, and social sciences. The paper argues that GenAI literacy is not limited to mastering tools but also involves cultivating a critical perspective on technology’s role in society. By emphasizing complex thinking competencies, it aims to prepare future generations for AI-augmented environments. This literacy is positioned as a cornerstone for responsibly harnessing AI’s potential and addressing challenges like bias, privacy, and intellectual property. Ultimately, the paper presents a roadmap for empowering individuals and institutions to navigate and shape the evolving AI landscape responsibly and innovatively. It underscores the importance of equipping society with the knowledge and skills necessary to engage meaningfully with one of the most influential technologies of the 21st century.Item Using ChatGPT for discovering conceptual classes in object-oriented modeling(Zenodo, 2023-07-31) García-Peñalvo, Francisco JoséUsing ChatGPT to discover conceptual classes in UML diagram classItem ModelViz: A Model-Driven Engineering Approach for Visual Analytics System Design(IEEE, 2024-03-29) Khakpour, A.; Vázquez-Ingelmo, A.; Colomo-Palacios, R.; García-Peñalvo, F. J.; Martini, A.Visual analytics systems should be able to consolidate data from disparate sources, conduct exploratory analysis, create visualizations that suit different users, and integrate seamlessly with decision-making activities to support data-driven decision-making. However, current mainstream visual analytics solutions often lack support for all these requirements. To address this gap, we propose the use of model-driven engineering to design visual analytics systems. To demonstrate the feasibility of this approach, we developed a Domain-Specific Modeling Language (DSML) named ModelViz to design visual analytics systems for consumer goods supply chain applications. Furthermore, we present the work of our DSML, using data from a manufacturing company as a case study. Finally, we evaluated ModelViz quantitatively by comparing it with other similar works from the literature. Our results demonstrate that this approach meets the requirements and provides a promising direction for designing visual analytics systems by considering domain-specific aspects to help achieve business goals.Item Generative Artificial Intelligence in Education: From Deceptive to Disruptive(Universidad Internacional de la Rioja, 2024-03-12) Alier, M.; García-Peñalvo, F. J.; Camba, J. D.Generative Artificial Intelligence (GenAI) has emerged as a promising technology that can create original content, such as text, images, and sound. The use of GenAI in educational settings is becoming increasingly popular and offers a range of opportunities and challenges. This special issue explores the management and integration of GenAI in educational settings, including the ethical considerations, best practices, and opportunities. The potential of GenAI in education is vast. By using algorithms and data, GenAI can create original content that can be used to augment traditional teaching methods, creating a more interactive and personalized learning experience. In addition, GenAI can be utilized as an assessment tool and for providing feedback to students using generated content. For instance, it can be used to create custom quizzes, generate essay prompts, or even grade essays. The use of GenAI as an assessment tool can reduce the workload of teachers and help students receive prompt feedback on their work. Incorporating GenAI in educational settings also poses challenges related to academic integrity. With availability of GenAI models, students can use them to study or complete their homework assignments, which can raise concerns about the authenticity and authorship of the delivered work. Therefore, it is important to ensure that academic standards are maintained, and the originality of the student's work is preserved. This issue highlights the need for implementing ethical practices in the use of GenAI models and ensuring that the technology is used to support and not replace the student's learning experience.Item Evaluating the Effectiveness of Human-Centered AI Systems in Education(Departamento de Informática y Automática. Universidad de Salamanca, 2024-03-01) Shoeibi, N.; Therón, R.; García-Peñalvo, F. J.This thesis examines how AI can improve human-computer interaction (HCI) and user experience in education. A systematic litera-ture review (SLR) and LATILL case study show how AI can be used in education. The SLR examines existing literature to determine how AI af-fects user experience and HCI in education, highlighting personalization and adaptability of learning experiences, improved task performance, and improved user experience for teachers and students. AI implementation in education faces obstacles. Using CEFR levels and linguistic traits, the LATILL project uses a user-centered design to give students personali-zed guidance and support. It transforms language instruction and fosters engaging and successful learning by encouraging educator collaboration and resource sharing. This study emphasizes the importance of user ex-perience and HCI principles in designing AI-driven educational systems. AI and user-centered design can improve learning, student engagement, and educational outcomes.Item Questionário de opinião com universitários/as sobre os estudos superiores em ciência, tecnologia, engenharia e matemática (QSTEMHE)(2024-02) Verdugo-Castro, Sonia; García-Holgado, Alicia; Sánchez-Gómez, Mª Cruz; Milfont Shzu, Maura AngélicaEm alguns países existe segregação por razão de gênero em algumas áreas de estudos, um exemplo disto são os setores da Ciência, da Tecnologia, da Engenharia e da Matemática, conhecidas pelo acrônimo STEM. Por esse motivo, foi elaborado o Questionário de opinião com universitários/as sobre os estudos superiores em ciência, tecnologia, engenharia e matemática (QSTEMHE), que foi validado empiricamente (Verdugo-Castro et al., 2022b) e faz parte da tese de doutorado de Sonia Verdugo-Castro, na Universidade de Salamanca (Verdugo-Castro, 2022). O objetivo perseguido com a aplicação do questionário QSTEMHE é descobrir os estereótipos de gênero que os estudantes universitários têm sobre os estudos superiores em STEM, uma vez que os fatores que afetam essa diferença de gênero tenham sido identificados (Verdugo-Castro, García-Holgado, et al., 2023). O questionário é composto de quatro blocos, dois dos quais contêm perguntas sociodemográficas e contextuais, outro contém perguntas abertas (Verdugo-Castro, Sánchez-Gómez, et al., 2023) e outro bloco contém itens nos quais são feitas afirmações às quais se deve responder com o grau de concordância ou discordância sobre a opinião que se tem como estudante universitário em relação a estudos superiores relacionados a ciência, tecnologia, engenharia e matemática (Verdugo-Castro et al., 2020, 2022a). Esta iniciativa faz parte de um projeto de pesquisa da Universidade de Brasília em parceria com a Universidade de Salamanca - Espanha com o objetivo de analisar a percepção dos estudantes universitários a respeito das áreas STEM para a detecção do problema e para a condução na elaboração de estratégias para a redução da lacuna de gênero nestas áreas. O questionário foi construído sob as dimensões associadas ao estudo de gênero (D3); atitudes (D4) e interesses (D1); percepções e autopercepções (D2), e expectativas sobre as Ciências (D5) (Verdugo-Castro et al., 2022b). No que diz respeito a algumas perguntas vinculadas a gênero, o estudo trata desde o respeito e a inclusão de diferentes identidades de gênero, sabendo que o gênero não se limita a classificação binária de homem e mulher, no entanto algumas perguntas fazem referência a homens e mulheres devido a tendência histórica de segregação entre ambos os gêneros no âmbito do estudo. A versão em espanhol do questionário, desenvolvida no âmbito da tese de doutorado de Sonia Verdugo-Castro (Verdugo-Castro, 2022), obteve um parecer favorável do Comitê de Bioética (CBE) da Universidade de Salamanca, com o número de registro 557. Além disso, é importante dizer que, apesar desta iniciativa ter respaldo no artigo 1, parágrafo único, inciso I e V da resolução CNS N˚. 510, de 7 de abril de 2016, o presente instrumento obteve a aprovação do Comitê de Ética da Universidade de Brasília (CAAE: 58603420.8.0000.5540; n.do parecer consubstanciado do CEP: 5.908.089, data: 23/02/2023). Por último, como o instrumento foi submetido a procedimentos de validação, alguns itens foram removidos. É por essa razão que, nesta versão final apresentada, ao lado dos códigos identificadores de cada item, em alguns deles, há uma referência em itálico e em cinza, entre parênteses, que está associada aos valores identificadores do questionário em sua primeira versão.