Scopus AI Beta: functional analysis and cases

Aguilera-Cora, Elisenda, Lopezosa, Carlos and Codina, Lluís Scopus AI Beta: functional analysis and cases., 2024 [Report]

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English abstract

Academic databases are a fundamental source for identifying relevant literature in a field of study. Scopus contains more than 90 million records and indexes around 12,000 documents per day. However, this context and the cumulative nature of science itself make it difficult to selectively identify information. In addition, academic database search tools are not very intuitive, and require an iterative and relatively slow process of searching and evaluation. In response to these challenges, Elsevier has launched Scopus AI, currently in its Beta version. As the product is still under development, the current user experience is not representative of the final product. Scopus AI is an artificial intelligence that generates short synthesis of the documents indexed in the database, based on instructions or prompts. This study examines the interface and the main functions of this tool and explores it on the basis of three case studies. The functional analysis shows that the Scopus AI Beta interface is intuitive and easy to use. Elsevier's AI tool allows the researcher to obtain an overview of a problem, as well as to identify authors and approaches, in a more agile search session than conventional search. Scopus AI Beta is not a substitute for conventional search in all cases, but it is an accelerator of academic processes. It is a valuable tool for literature reviews, construction of theoretical frameworks and verification of relationships between variables, among other applications that are actually impossible to delimit.

Item type: Report
Keywords: Scopus AI Beta, artificial intelligence, academic research, academic databases
Subjects: L. Information technology and library technology
L. Information technology and library technology > LZ. None of these, but in this section.
Depositing user: Dr Lluís Codina
Date deposited: 20 Jan 2024 19:10
Last modified: 20 Jan 2024 19:10
URI: http://hdl.handle.net/10760/45321

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