DEVELOPMENT AND VALIDATION OF SIRSL: GENERATIVE ARTIFICIAL INTELLIGENCE-BASED SYSTEM FOR AUTOMATING SYSTEMATIC LITERATURE REVIEWS
TL;DR
It is concluded that SIRSL is functionally viable and has the potential to make systematic reviews more integrated, organized, and traceable, however, further validation across different research fields and documentary datasets is still required.
Abstract
The expansion of scientific production and the fragmentation of tools used in Systematic Literature Reviews hinder the organization, traceability, and integration of methodological stages. This study aimed to develop and functionally validate the Intelligent System for Systematic Literature Reviews (SIRSL), based on Generative Artificial Intelligence. This applied technological research adopted a descriptive approach and was conducted through iterative stages involving requirements assessment, system modeling, implementation, testing, and refinement. The system was developed using React, TypeScript, Node.js, and Firebase, integrating the Gemini model. Functional validation was carried out by applying SIRSL to a systematic review associated with a doctoral research project within the PPGADT. The initial corpus comprised 470 records, of which 58 were excluded after duplicate identification and application of the selection criteria, leaving 412 documents. The results demonstrated that SIRSL integrated functionalities for developing search strategies, importing files, detecting duplicates, screening studies, extracting data, producing indicators, and generating reports. Artificial Intelligence was employed as an assistive resource, while methodological decisions remained under the researchers’ responsibility. It is concluded that SIRSL is functionally viable and has the potential to make systematic reviews more integrated, organized, and traceable. However, further validation across different research fields and documentary datasets is still required.