Scopus AI Beta: functional analysis and cases
Scopus AI Beta: functional analysis and cases
By the OCM team
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.
Keywords
Scopus AI Beta, artificial intelligence, academic research, academic databases.
Introduction
The term artificial intelligence (hereinafter AI) is attributed to John McCarthy, founder of this field of study. In 1955 the scientist defined artificial intelligence as “the science and engineering of creating intelligent machines” (McCarthy, 1955). In its beginnings, scientific studies related to AI were focused on the field of physical sciences, but over time it has expanded to cover other disciplines. In fact, the number of scientific publications related to AI has increased exponentially. In 1960 only 14% of the subject areas of the Scopus All Science Journal Classification (ASJC) system featured AI-related publications. However, currently, this figure is higher than 98% (Hajkowicz et al., 2023). In the field of social sciences, recent research has studied how AI constitutes a valuable resource, both for the design of systematized literature reviews and in university teaching (Lopezosa, Codina and FerránFerrer, 2023; Codina and Garde, 2023).
The academic environment is characterized by large amounts of published research and diverse databases. These peculiarities make it difficult for researchers to discover valuable information, despite being a fundamental part of their work. This is especially true for young researchers, as the advancement of science is a cumulative process. Current tools are limited, as they do not present direct results, but rather lists of documents, which require significant amount of time to navigate. Furthermore, it is necessary to apply various inclusion and exclusion criteria before even approaching a document bank that can respond to a specific information need. This search approach is necessary in some contexts, but there are others in which an AI solution using direct responses may be a better solution, as it can help accelerate some processes
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Citation
Aguilera-Cora E, Lopezosa C, Codina L. Scopus AI Beta: functional analysis and cases. Barcelona: Universitat Pompeu Fabra, Departament de Comunicació, 2023. 46 p. (Serie Editorial DigiDoc. DigiDoc Reports).