The evolution of chatbots in libraries over the past decade through bibliometric analysis

Maisyatus Su'adaa  Irfana; Nove E Variant Anna; Yollanda Nundy Alshafa; Parveen  Babbar

doi:10.22146/bip.v22i2.26776

Authors

Maisyatus Su'adaa Irfana STIE Mahardika, Surabaya, Indonesia
Nove E Variant Anna Faculty of Vocational Studies, Universitas Airlangga, Indonesia
Yollanda Nundy Alshafa Faculty of Vocational Studies, Universitas Airlangga, Indonesia
Parveen Babbar Dr. B. R. Ambedkar Central Library, Jawaharlal Nehru University, India

DOI:

https://doi.org/10.22146/bip.v22i2.26776

Keywords:

chatbot, Bibliometrics, Artificial Intelligence, Academic Libraries, Library Services, Developing Countries

Abstract

Introduction. This study examines the development of chatbot implementation in libraries over the past decade (2014–2025) using a bibliometric approach.

Research Methods. Bibliographic data was retrieved from the Scopus database using the keywords chatbot and library.

Data Analysis. The analysis was conducted using Scopus analytics and VOSviewer to map publication trends, sources, authors, countries, document types, subject areas, and keyword relationships, supported by chronological interpretation.

Results. The results show a significant increase in research on library chatbots, with rapid growth occurring in the last four years following advances in artificial intelligence and large language models such as ChatGPT. Library Hi Tech News is the most consistent publication outlet, while India is the most productive country. Adeleke University and JAIN Deemed-to-be University were the leading institutions, and Adetayo, A.J. and De Sarkar, T. were the most productive authors. Conference papers dominate publication types, and the major subject areas include computer science, engineering, and social sciences. Keyword analysis highlights strong links to artificial intelligence, human interaction, and natural language processing.

Conclusion. This study contributes a dedicated bibliometric mapping and a three-phase evolutionary model of chatbot development in libraries. However, the reliance on Scopus and bibliometric indicators may limit insights into qualitative user experience and implementation outcomes.

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