Transversal Applications of Generative AI in Research and Innovation

This document offers a concise, practice-oriented overview of how generative artificial intelligence can support the entire research and innovation lifecycle. It outlines the main stages of research where these tools add value—formulating research questions, building conceptual frameworks, drafting project proposals, searching and synthesising scholarly literature, extracting information from scientific texts and figures, and preparing outputs for dissemination and knowledge transfer.

The text reviews several families of tools, including AI-assisted scientific writing and editing, literature-aware search engines, platforms for text mining and data analysis, systems for optical character recognition and table extraction, and generators of images and other complementary research materials. Particular attention is given to prompt design as a key competence for obtaining accurate, reproducible and well-documented results.

Finally, the document stresses the need for critical and responsible use of generative AI in research. It highlights issues such as cognitive over-reliance, opacity of models, verification of outputs against primary sources, data governance and transparency, as well as emerging editorial and institutional policies regarding the declaration of AI use in scientific work.