Keywords
Alfabetización en IA, Interdisciplinariedad, Educación Superior, STEM, HAS, Ética Tecnológica
Abstract
La creciente adopción de la Inteligencia Artificial (IA) Generativa en la educación superior exige enfoques innovadores que combinen habilidades técnicas y reflexivas. La mayoría de los programas académicos enfatizan la formación técnica, dejando de lado aspectos críticos, éticos y sociales de la IA. Este estudio busca investigar cómo la integración entre las áreas STEM (Ciencia, Tecnología, Ingeniería y Matemáticas) y HAS (Humanidades, Artes y Ciencias Sociales) puede fortalecer la alfabetización en IA, promoviendo una enseñanza más holística e interdisciplinaria.Utilizando un enfoque de métodos mixtos, realizamos un análisis bibliométrico de 100 artículos académicos (Scopus, Web of Science y Google Scholar), además de entrevistas semiestructuradas a 20 profesores e investigadores especializados en STEM, HAS e IA Generativa. Los datos fueron analizados estadísticamente y según la categoría temática, permitiendo identificar beneficios, desafíos y estrategias para la interdisciplinariedad en la enseñanza de IA. Los resultados indican que la colaboración interdisciplinaria fortalece habilidades transversales como el pensamiento crítico, la creatividad y la toma de decisiones éticas, esenciales para el desarrollo responsable de la IA. Se identificaron desafíos como la falta de estructuras curriculares integradas y la resistencia institucional para la implementación de dicho modelo educativo. En respuesta, se propone un modelo interdisciplinario de alfabetización en IA, que puede orientar a las universidades en la formación de profesionales capaces de trabajar en equipos multidisciplinarios en gobernanza y desarrollo de IA.
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Technical information
Received: 2025-01-13 | Reviewed: 2025-04-21 | Accepted: 2025-04-23 | Online First: 2025-07-21 | Published: 2025-07-24
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Prof. Sérgio Silva. (2025). Integración de STEM y HAS para la alfabetización en IA: Un modelo interdisciplinario para la educación superior. Comunicar, 33(82). 10.5281/zenodo.15993832