AI in research methodology
This paper explores the transformative impact of Generative Artificial Intelligence (GenAI) on scientific research methodology. It contrasts the traditional linear research approach with emerging AI-driven paradigms, highlighting the closed-loop automation demonstrated by frameworks like DOLPHIN. We...
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2026 |
| País: | España |
| Institución: | Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya) |
| Repositorio: | Recercat. Dipósit de la Recerca de Catalunya |
| OAI Identifier: | oai:dnet:recercat____::6c09b202c4fae400c2a76f01578b5ad9 |
| Acceso en línea: | https://hdl.handle.net/20.500.14342/6214 |
| Access Level: | acceso abierto |
| Palabra clave: | Generative AI Research methodology 00 004 |
| Sumario: | This paper explores the transformative impact of Generative Artificial Intelligence (GenAI) on scientific research methodology. It contrasts the traditional linear research approach with emerging AI-driven paradigms, highlighting the closed-loop automation demonstrated by frameworks like DOLPHIN. We identify five primary use cases for GenAI in science—Literature Review, Gap Finding, Hypothesis Generation, Research Question Refinement, and the Socratic Opponent—and analyze four key tools (Elicit, ResearchRabbit, Scite, Consensus) facilitating these tasks. Finally, we address critical risks such as hallucinations and methodological monoculture, alongside the strategic perspective of the European Commission regarding AI in science. |
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