Generative Engine Optimization in Digital Repositories: Optimizing Visibility for Generative AI

Generative Engine Optimization in Digital Repositories: Optimizing Visibility for Generative AI

The rapid growth of generative Artificial Intelligence (AI) and large-scale language models (LLMs) has transformed the way information is searched and retrieved on the web. It has shifted from traditional keyword matching and list-based results with site links to generating synthesized, conversational responses. This change presents a significant challenge to the traffic of academic digital repositories. In response, Generative Engine Optimization (GEO) emerges as a potential solution. Current SEO strategies are inadequate for ensuring that new generative engines effectively reference content. This article aims to present the first GEO strategic guidelines for institutional repositories to enhance their visibility. The methodology develops a theoretical framework that compares different search methods and adapts industry GEO strategies to repository management. Key strategies, including technical optimization, semantic structuring, metadata quality, content enhancement, interoperability, multilingual support, and new metric exploration, are identified and proposed. Ultimately, it is argued that a balanced adoption of GEO can increase the impact of repositories while maintaining their mission as user-focused public services that provide open access to scientific knowledge in the AI era.