Generative Engine Optimization en repositorios digitales: optimización de la visibilidad para la IA generativa
DOI:
https://doi.org/10.3145/infonomy.25.034Palabras clave:
GEO, Generative Engine Optimization, Repositorios digitales, Motores generativos, IA generativa, SEO académico, Repositorios académicos, Optimización, Visibilidad, Inteligencia artificial generativa, LLM, Reference RateResumen
El auge exponencial de la Inteligencia Artificial (IA) generativa y los modelos de lenguaje a gran escala (LLMs) ha redefinido la búsqueda y recuperación de información en la web, migrando del enfoque tradicional de coincidencias de palabras clave y listas de resultados con enlaces a sitios, a la producción de respuestas sintetizadas y conversacionales. Esta transformación está representando un desafío crítico para el tráfico de los repositorios digitales académicos y la Generative Engine Optimization (GEO) surge como una posible respuesta. Las estrategias de optimización SEO actuales resultan insuficientes para que el contenido sea referenciado de manera efectiva por los nuevos motores generativos. El objetivo de este artículo es proponer los primeros lineamientos estratégicos de GEO aplicables a repositorios institucionales para maximizar su visibilidad. La metodología establece un marco teórico que contrasta los enfoques de búsqueda y extrapola las estrategias GEO de la industria a la gestión de repositorios. Se identifican y proponen estrategias como la optimización técnica, la estructura semántica, la calidad de los metadatos, la optimización del contenido, la interoperabilidad, el multilingüismo y la exploración de nuevas métricas. Como conclusión, se plantea que la adopción equilibrada de la GEO puede fortalecer el impacto de los repositorios sin desatender su misión de servicio público centrado en las personas usuarias y de acceso abierto a la producción científica en la era de la IA.Citas
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Derechos de autor 2025 Danilo Reyes-Lillo, Alejandro Morales-Vargas, Cristòfol Rovira
Esta obra está bajo una licencia internacional Creative Commons Atribución 4.0.
