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

**Authors:** Rajesh Kumar Das, Mohammad Sharif Ul Islam  
**arXiv:** [2112.04573](https://arxiv.org/abs/2112.04573)  
**Date:** 2021-12-06  
**Summary page:** https://www.summarizepaper.com/en/paper/2112.04573v1/

## Key points

- Systematic literature review on the application of AI and ML in libraries
- Data collected from various databases including Web of Science, Scopus, LISA, and LISTA
- 32 articles selected for review and analysis
- Current state of AI and ML research in libraries focuses on theoretical works, implementation projects, and case studies
- Identified AI and ML techniques commonly used in libraries summarized in Table 2
- Inclusion criteria: English language, ML or AI applications, Library and Information Science sub-area with relevant titles, abstracts, and keywords
- Exclusion criteria: non-digital publications, unavailable for full review, non-English language without ML or AI applications in libraries
- Initial screening based on title and abstract followed by assessment using inclusion/exclusion criteria
- Panoramic view of AI and ML in libraries provided for researchers, practitioners, educators interested in technology-oriented approaches
- Highlights the need for further research and innovation pathways in this field

## Summary

This systematic literature review explores the application of artificial intelligence (AI) and machine learning (ML) in libraries. The objective of the study is to provide a synthesis of empirical studies in this area. The researchers conducted a systematic literature review based on established guidelines, collecting data from various databases including Web of Science, Scopus, LISA, and LISTA. After a rigorous selection process, 32 articles were selected for review and analysis. The findings reveal that the current state of AI and ML research in libraries mainly focuses on theoretical works, although some researchers also emphasize implementation projects or case studies. The study identifies the AI and ML techniques most often used in libraries and summarizes them in Table 2. These techniques include machine learning algorithms and artificial intelligence methods applied to different aspects of library operations. The inclusion criteria for articles in this review were that they must be written in English, discuss ML or AI applications, belong to the sub-area of Library and Information Science, and have titles, abstracts, and keywords related to libraries with the application of either machine learning or artificial intelligence. Exclusion criteria included non-digital publications, publications not available for full review, articles not in English language, and those without any application of ML or AI in libraries. The study selection process involved an initial screening based on title and abstract followed by assessment using the inclusion and exclusion criteria. A total of 379 articles were initially retrieved from various databases but after removing duplicates (16.09%), 286 papers remained. Finally, 32 articles were included in the systematic review. This study provides a panoramic view of AI and ML in libraries for researchers, practitioners, and educators interested in technology-oriented approaches. It highlights the need for further research and innovation pathways in this field. Overall, it contributes to the understanding of how cutting-edge technologies like AI and ML are being applied within library settings.