Publication: Integración de la Inteligencia Artificial y la Alfabetización de Datos en la ESO: Análisis de percepciones y condiciones de adopción
Authors
Amo Filva, Daniel ; Guàrdia Ortiz, Lourdes ; Donate Beby, Belén ; Bautista Pérez, Guillermo ; Fanni, Ludovica
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Publisher
Universidad de Murcia, Servicio de Publicaciones
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DOI
https://doi.org/10.6018/red.690641
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info:eu-repo/semantics/article
Description
Abstract
Este estudio, mediante un diseño mixto secuencial explicativo, presenta los resultados de la tercera fase del proyecto IADE, centrada en analizar la Alfabetización de Datos (AD) para el uso de Analíticas de Aprendizaje (AA) y la adopción de la Inteligencia Artificial (IA) entre docentes de educación secundaria en Cataluña. La fase cuantitativa incluyó un cuestionario validado que se administró a docentes de distintos centros educativos y del cual se recogieron 372 respuestas, cuyos resultados revelan niveles bajos y homogéneos de uso de AA, así como una adopción incipiente de herramientas de IA. La fase cualitativa, desarrollada a través de un grupo de discusión con profesionales de diferentes perfiles institucionales, permitió identificar nueve categorías que describen las barreras, necesidades y condiciones de uso de datos e IA en los centros educativos.
Los resultados muestran una gestión de datos eminentemente reactiva, ausencia de marcos institucionales claros, uso exploratorio y no sistemático de IA y una fuerte preocupación por la privacidad y la coherencia procedimental. La triangulación de datos refuerza la necesidad de políticas educativas que fortalezcan la formación docente, el liderazgo institucional y la adopción ética y contextualizada de la IA. El estudio ofrece implicaciones para el diseño de estrategias formativas, marcos normativos y acciones de acompañamiento que favorezcan una integración significativa de la IA y la AD para el uso de las AA en la educación secundaria.
The present study, through an explanatory sequential mixed-methods design, presents the results of the third phase of the IADE project, which focuses on analyzing Data Literacy (DL) for the use of Learning Analytics (LA) and the adoption of Artificial Intelligence (AI) among lower secondary education teachers in Catalonia. The quantitative phase included a validated questionnaire administered to teachers from different schools, yielding 372 responses. The results reveal low and homogeneous levels of LA use, as well as an emerging and uneven adoption of AI tools. The qualitative phase, conducted through a focus group with professionals representing diverse institutional profiles, made it possible to identify nine categories describing the barriers, needs, and conditions surrounding the use of data and AI in schools. Findings indicate a predominantly reactive approach to data management, the absence of clear institutional frameworks, an exploratory and non-systematic use of AI, and a strong concern for privacy and procedural coherence. The triangulation of data reinforces the need for educational policies that strengthen teacher training, institutional leadership, and the ethical and contextualized adoption of AI. The study provides implications for the design of training strategies, regulatory frameworks, and support actions that foster a meaningful integration of AI and DL for the use of LA in lower secondary education.
The present study, through an explanatory sequential mixed-methods design, presents the results of the third phase of the IADE project, which focuses on analyzing Data Literacy (DL) for the use of Learning Analytics (LA) and the adoption of Artificial Intelligence (AI) among lower secondary education teachers in Catalonia. The quantitative phase included a validated questionnaire administered to teachers from different schools, yielding 372 responses. The results reveal low and homogeneous levels of LA use, as well as an emerging and uneven adoption of AI tools. The qualitative phase, conducted through a focus group with professionals representing diverse institutional profiles, made it possible to identify nine categories describing the barriers, needs, and conditions surrounding the use of data and AI in schools. Findings indicate a predominantly reactive approach to data management, the absence of clear institutional frameworks, an exploratory and non-systematic use of AI, and a strong concern for privacy and procedural coherence. The triangulation of data reinforces the need for educational policies that strengthen teacher training, institutional leadership, and the ethical and contextualized adoption of AI. The study provides implications for the design of training strategies, regulatory frameworks, and support actions that foster a meaningful integration of AI and DL for the use of LA in lower secondary education.
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Citation
RED: revista de educación a distancia, N. 83, V. 26, 2026
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