GapFinder: Exploring Research Gaps with Artificial Intelligence

Abstract

"GapFinder" is an Artificial Intelligence (AI) solution developed to address an important challenge in the field of scientific/academic research, namely: the efficient mapping of research gaps based on existing scientific/academic literature. The exponential increase in all areas of academic publications stresses the relevance of this technological tool to help researchers identify gaps that are still unexplored in academic texts because they are undetected by traditional science mapping techniques to reveal fields for research and innovation. GapFinder applies Natural Language Processing (NLP) algorithms to identify potential research gaps in scientific documents using mining and extracting information techniques from unstructured texts in Portable Document Format (PDF). This AI solution fosters a more comprehensive understanding of the existing literature, emphasizing areas that need further investigation. The present study describes the development of GapFinder, from the conception of the idea to the practical implementation and its availability for access. The methodology used to process and analyze scientific documents in PDF format is described in the paper and followed by a simulation of GapFinder to demonstrate how it can facilitate the work of researchers from the perspective of corpus processing. The study concludes with the importance of innovation in scientific research through the implementation of technologies and methods that can act as catalysts for innovation in science, from the perspective of identifying gaps and new research strategies and also points out some implications for English language teachers and researchers.