A Bibliometric Analysis on Chatbot Application in Education

A Bibliometric Analysis on Chatbot Application in Education

Автор: Lily Edinam Bensah, Fati Tahiru, Carlos Ankora, Noble Arden Kuadey

Журнал: International Journal of Education and Management Engineering @ijeme

Статья в выпуске: 6 vol.13, 2023 года.

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A bibliometric analysis study investigating chatbots' current state and developments in education research has not been adequately addressed in literature. Thus, this study highlights the current state, emerging research trends and directions of chatbots in education using bibliometric analysis. The significance of the study is to provide insights into the most recent developments of chatbots application in education and future research directions for academics and practitioners. A bibliometric analysis of publications on chatbots in education published between 2012 and 2022 was conducted. A total of 759 publications were collected from the Scopus database for the bibliometric analysis. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) was adopted to identify and screen the dataset. VOSviewer (version 1.6.18) was used to perform the following network analysis: co-authorship and co-occurrence. Also, Bibliometrix was used for the descriptive statistics of the bibliometric data and the publication trend. The study’s findings showed that fewer studies have been conducted from the African region on chatbot education research. Researchers from the United Kingdom, United States, Australia, China, India, Greece, Japan, Vietnam, Malaysia, and United Arab Emirates have collaborated significantly in chatbot education research. Also, the main keyword occurrences in research on chatbots in education are chatbots, students, artificial intelligence, natural language processing, education, and learning. The trends indicate a steady increase in research on chatbots over the past decade.

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Chatbots, Bibliometric analysis, Education, Co-authorship, Co-occurrence, Artificial intelligence

Короткий адрес: https://sciup.org/15018680

IDR: 15018680   |   DOI: 10.5815/ijeme.2023.06.01

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