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dc.contributor.author	Liashenko, Roman
dc.contributor.author	Семеріков, Сергій Олексійович
dc.date.accessioned	2024-07-11T17:23:36Z
dc.date.available	2024-07-11T17:23:36Z
dc.date.issued	2024-06-28
dc.identifier.citation	Liashenko R. Bibliometric analysis of chatbot training research: key concepts and trends / Roman Liashenko, Serhiy Semerikov // Information Technologies and Learning Tools. – 2024. – Vol. 101. – Iss. 3. – P. 181-199. – DOI : https://doi.org/10.33407/itlt.v101i3.5622	uk
dc.identifier.issn	2076-8184
dc.identifier.uri	https://journal.iitta.gov.ua/index.php/itlt/article/view/5622
dc.identifier.uri	https://doi.org/10.33407/itlt.v101i3.5622
dc.identifier.uri	http://elibrary.kdpu.edu.ua/xmlui/handle/123456789/10380
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dc.description.abstract	This bibliometric analysis aims to identify current research directions and priorities in the field of chatbot training – software agents capable of natural language dialogue. The study is based on the analysis of 549 scientific sources from the Scopus database on this topic. The analysis revealed a steady increase in relevant publications starting from 2018, indicating a growing relevance of this subject area in recent years. Based on a cluster analysis of keywords, four main research areas were identified: natural language processing, application of relevant technologies in various spheres of society, use of machine learning methods for natural language processing, and application of chatbots in education and services. In the field of natural language processing, the focus of current research is on computational linguistics, language modeling and machine comprehension, particularly speech recognition tasks. Leading research on artificial intelligence applications in this area concerns the responsible and ethical use of modern large language models and conversational agents, such as ChatGPT, in education and healthcare. Machine learning methods are actively being developed for creating virtual intelligent assistants, natural language user interfaces, and other natural language processing systems, including for diagnostic tasks in medicine. Key applications of chatbots are identified in adaptive learning systems, knowledge management, and customer service. Based on the analysis, the most significant concepts in each of the studied areas are defined to outline priorities for further research in the field of chatbot training. Future work involves conducting a systematic literature review with the automation of certain stages using large language models. In particular, these models will be employed to automatically classify study abstracts according to inclusion/exclusion criteria during the screening phase. Automating systematic review stages using artificial intelligence opens up significant prospects for accelerating scientific research, particularly in the education field based on an evidence-based approach.	uk
dc.language.iso	uk	uk
dc.publisher	Institute for Digitalisation of Education of NAES of Ukraine	uk
dc.subject	chatbot training	uk
dc.subject	natural language processing	uk
dc.subject	machine learning	uk
dc.subject	bibliometric analysis	uk
dc.subject	systematic literature review	uk
dc.subject	large language models	uk
dc.title	Bibliometric analysis of chatbot training research: key concepts and trends	uk
dc.title.alternative	Бібліометричний аналіз досліджень з навчання чат-ботів: ключові поняття та тенденції	uk
dc.type	Article	uk