Inteligencia artificial.
Un diálogo sobre oportunidades y desafíos para la sociedad española
Bajo la dirección académica de Senén Barro y Ofelia Tejerina
Fundación FIDE (https://thinkfide.com/)

https://thinkfide.com/wp-content/uploads/2025/03/IA-Un-dialogo-sobre-oportunidades-y-desafios-para-la-sociedad-espanola-Foro-de-Debate-AI-Connect-Fide-Google.pdf

Este documento recoge las conclusiones alcanzadas por los expertos externos que participaron en las sesiones de debate celebradas dentro del Foro AI Connect, iniciativa organizada por Google y FIDE con el objetivo de facilitar un diálogo abierto en torno a la IA.
Dirigido por Senén Barro Ameneiro, Director del CiTIUS-Centro Singular de Investigación en Tecnologías Inteligentes de la Universidad de Santiago de Compostela, y Ofelia Tejerina Rodríguez, Presidenta de la Asociación de Internautas, en este foro se han abordado cuestiones fundamentales para la sociedad como son el futuro del trabajo, la democracia, la ciencia, la salud, la ciberseguridad y el medioambiente.

IA en las universidades: ¿un paso más en la automatización o una transformación?
Faraón Llorens

Jornada Interuniversitaria de IA en la Administración Universitaria
https://ice.ua.es/es/formacion/2024/jornada-interuniversitaria-ia/jornada-info.html

Grabación de la jornada:
Parte 1. Presentación y ponencias de Faraón Llorens y Rubén Martínez.
https://vertice.cpd.ua.es/301241

Self explainable graph convolutional recurrent network for spatio-temporal forecasting
Javier García-Sigüenza, Manuel Curado, Faraón Llorens-Largo & José F. Vicent
Machine Learning
Volume 114, article 2 (2025)
doi: doi.org/10.1007/s10994-024-06725-6
Published 14 January 2025
https://link.springer.com/article/10.1007/s10994-024-06725-6

Abstract
Artificial intelligence (AI) is transforming industries and decision-making processes, but concerns about transparency and fairness have increased. Explainable artificial intelligence (XAI) is crucial to address these concerns, providing transparency in AI decision making, alleviating the effect of biases and fostering trust. However, the application of XAI in conjunction with problems with spatio-temporal components represents a challenge due to the small number of options, which when implemented penalize performance in exchange for the explainability obtained. This paper proposes self explainable graph convolutional recurrent network (SEGCRN), a model that seeks to integrate explainability into the architecture itself, seeking to increase the ability to infer the relationship and dependence between the different nodes, proposing an alternative to explainability techniques, which are applied as a second layer. The proposed model has been able to show in different data sets the ability to reduce the amount of information needed to make a prediction, while reducing the impact on the prediction caused by applying an explainability technique, having managed to reduce the use of information without loss of accuracy. Thus, SEGCRN is proposed as a gray box, which allows a better understanding of its behavior than black box models, having validated the model with traffic data, combining both spatial and temporal components, achieving promising results.

Keywords: Graph neural networks, Deep learning, Data analysis, Explainability, Spatio-temporal forecasting

Se ha publicado el 3º Informe del Observatorio de Inteligencia Artificial en Educación Superior de la Universidad Europea.

Web del observatorio: https://universidadeuropea.com/conocenos/observatorio-inteligencia-artificial-educacion-superior

Informe OIAES#3 (8 enero 2025).
El impacto de la IA en planes de estudio y resultados de aprendizaje; competencias y contenidos de las asignaturas actuales y/o futuras
(pdf)

IA y Educación: Creando Nuevas Fronteras de Innovación
Faraón Llorens, Universidad de Alicante.
Ismael Gómez, director Estrategia Digital Global de la OEI.
Segundo Piriz, rector de UNIE Universidad, Grupo Planeta.
Moderación: Juan Miguel Sucunza, vicepresidente CEAPI y vicepresidente ejecutivo del Grupo Eulen

Presentación Nº 12 de la revista PÓDIUM. Iberoamérica 4.0- Innovación y productividad en la era de la inteligencia artificial
https://www.ucv.es/oferta-academica/congresos-y-jornadas/congresos-y-jornadas-2024/ii-congreso-internacional-de-politicas-educativas-entre-europa-e-iberoamerica/presentacion

Organización de Estados Iberoamericanos para la Educación, la Ciencia y la Cultura (OEI)

Programa completo:
https://oei.int/oficinas/secretaria-general/eventos/presentacion-no-12-de-la-revista-podium-iberoamerica-4-0-innovacion-y-productividad-en-la-era-de-la-inteligencia-artificial

Noticia:
https://oei.int/oficinas/secretaria-general/noticias/una-publicacion-de-la-oei-analiza-el-impacto-de-la-ia-en-la-educacion-y-la-productividad-de-iberoamerica

Podium: revista iberoamericana de educación e innovación para la productividad. Nº 12, junio de 2024
https://oei.int/oficinas/secretaria-general/publicaciones/podium-revista-iberoamericana-de-educacion-e-innovacion-para-la-productividad-n-12-junio-de-2024
Iberoamérica 4.0: innovación y productividad en la era de la inteligencia artificial
Acceso al pdf

Página 12
IA o AI, esta es la cuestión
Faraón Llorens Largo
https://blogs.ua.es/faraonllorens/2024/06/03/ia-o-ai-esta-es-la-cuestion

Multiagent Systems for Educational Transformation: A Swarm of Intelligent Personal Assistants
Sergio Arjona-Giner and Faraón Llorens-Largo
University of Alicante, Alicante, Spain

Technological Ecosystems for Enhancing Multiculturality (TEEM 2024)
23-25 October 2024
Alicante, Spain
2024.teemconference.eu

Proceedings
Lecture Notes in Educational Technology

Abstract.
The arrival of LLMs in November 2022, marked by the release of the
GPT-3.5 model, represents a milestone in the field of AI. These advanced mod-
els have demonstrated an exceptional ability to comprehend and generate hu-
man-like text, among other skills. The versatility and proficiency of LLMs have
paved the way for the development of various applications, including intelligent
agents. When these agents are combined synergistically in a MAS, they utilize
the trained or pre-trained LLMs as their core AI engine. In the educational sec-
tor, this AI-driven transformation has reinforced the concept of Smart Learning,
leveraging advanced technologies to create interactive, personalized, and effi-
cient learning experiences. One tangible manifestation of Smart Learning is the
development of SLApps which harness AI for robust educational support. In
this paper, we explore the introduction of a SLApp that employs a swarm of in-
telligent agents coordinated within a MAS. By leveraging the capabilities of
LLMs and the coordinated efforts of intelligent agents, the SLApp aims to im-
prove the educational process. Key contributions include the preliminary archi-
tecture of the SLApp, integration with the LTI 1.3 standard for compatibility
with LMSs and implementation of RAG technology to minimize hallucinations
and enhance knowledge sources.

Keywords:
Multiagent System, RAG, Smart Learning.