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<p>With the tremendous development of artificial intelligence and machine learning technologies, chatbots have gained commonly in many fields, including education. Chatbots can be used in education to provide continuous support, enhance interaction and participation processes, and support self-learning and collaborative learning. The current study came in response to the lack of scientific reviews that provide an integrated view of the descriptive and analytical structure of research on the use of chatbots in education. This paper presented a bibliometric analysis (BA) of a total of (313) documents during the period 2016–2023 extracted from the Web of Science database. The reason is to determine publication trends, analyze citations, identify collaborative networks, analyze relationships, and future trends, using Biblioshiny in the R environment and VOSviewer. The study also provided a systematic review (SLR) with a total of (30) documents to find the main topics of the most influential studies. This is because the most important studies focused on the learning outcomes of using chatbots, the effectiveness of chatbots in learning a second language, limitations and precautions for using chatbots, using ChatGPT models, and the roles of teachers in using chatbots.</p>

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Numerical and thematic trends and developments of chatbot research in education: bibliometric analysis and systematic review

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月3日

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  • Wael Alsayed,
  • Fahad Al-Hafdi

摘要

With the tremendous development of artificial intelligence and machine learning technologies, chatbots have gained commonly in many fields, including education. Chatbots can be used in education to provide continuous support, enhance interaction and participation processes, and support self-learning and collaborative learning. The current study came in response to the lack of scientific reviews that provide an integrated view of the descriptive and analytical structure of research on the use of chatbots in education. This paper presented a bibliometric analysis (BA) of a total of (313) documents during the period 2016–2023 extracted from the Web of Science database. The reason is to determine publication trends, analyze citations, identify collaborative networks, analyze relationships, and future trends, using Biblioshiny in the R environment and VOSviewer. The study also provided a systematic review (SLR) with a total of (30) documents to find the main topics of the most influential studies. This is because the most important studies focused on the learning outcomes of using chatbots, the effectiveness of chatbots in learning a second language, limitations and precautions for using chatbots, using ChatGPT models, and the roles of teachers in using chatbots.