Application of Artificial Intelligence and Machine Learning in Libraries: A Systematic Review

TL;DR

This systematic review synthesizes empirical research on how artificial intelligence and machine learning are applied in libraries, highlighting current focus areas, techniques used, and future directions for technology adoption.

Contribution

It provides a comprehensive overview of AI and ML applications in libraries, summarizing existing empirical studies and identifying research gaps and future innovation pathways.

Findings

Research mainly focuses on theoretical works.

Some studies emphasize implementation projects or case studies.

AI and ML techniques are increasingly used in library contexts.

Abstract

As the concept and implementation of cutting-edge technologies like artificial intelligence and machine learning has become relevant, academics, researchers and information professionals involve research in this area. The objective of this systematic literature review is to provide a synthesis of empirical studies exploring application of artificial intelligence and machine learning in libraries. To achieve the objectives of the study, a systematic literature review was conducted based on the original guidelines proposed by Kitchenham et al. (2009). Data was collected from Web of Science, Scopus, LISA and LISTA databases. Following the rigorous/ established selection process, a total of thirty-two articles were finally selected, reviewed and analyzed to summarize on the application of AI and ML domain and techniques which are most often used in libraries. Findings show that the current state of the AI and ML research that is relevant with the LIS domain mainly focuses on theoretical works. However, some researchers also emphasized on implementation projects or case studies. This study will provide a panoramic view of AI and ML in libraries for researchers, practitioners and educators for furthering the more technology-oriented approaches, and anticipating future innovation pathways.