A bibliometric analysis of artificial intelligence in language teaching and learning (1990–2023): evolution, trends and future directions

The advancement and application of Artificial Intelligence (AI) has introduced innovative changes in language learning and teaching. In particular, the widespread utilization of various chatbots as foreign language learning partners showcases their remarkable potential contribution to the field. Nev...

Full description

Bibliographic Details
Main Authors: Ma, Huiling, Ismail, Lilliati, Han, Weijing
Format: Article
Language:English
Published: Springer 2024
Online Access:http://psasir.upm.edu.my/id/eprint/115315/
http://psasir.upm.edu.my/id/eprint/115315/1/115315.pdf
_version_ 1848866744735629312
author Ma, Huiling
Ismail, Lilliati
Han, Weijing
author_facet Ma, Huiling
Ismail, Lilliati
Han, Weijing
author_sort Ma, Huiling
building UPM Institutional Repository
collection Online Access
description The advancement and application of Artificial Intelligence (AI) has introduced innovative changes in language learning and teaching. In particular, the widespread utilization of various chatbots as foreign language learning partners showcases their remarkable potential contribution to the field. Nevertheless, there are currently few studies that encompass extensive and holistic reviews and analyses of the relevant literature during this period. The study employs bibliometric analysis and a systematic review of representative research to present trends, the current status and future directions of AI research in language teaching and learning, providing language educators, policymakers, and research scholars with visually accessible and comprehensive insights. Results indicate that the field is in its early stages of development, growing rapidly with significant research potential. The study identified the most productive and influential sources, institutions, authors and countries and provided a summary for the most representative papers in the research field. Through keyword analysis, the study delineates the evolutionary progression of AI in the domain of language teaching and learning across different time periods, identifies prevailing research trends and proposes future research directions. Results indicate that influential research in this realm predominantly focuses on refining technological solutions and conducting empirical studies on AI applications in language teaching and learning. This highlights significant interest in the effectiveness of AI in language education and its implementation methods. However, research on the application of AI in language education is still in its infancy. Therefore, the study advocates for increased empirical research on AI’s specific applications in language listening, speaking, reading, and writing, as well as the development of more effective pedagogical approaches. Furthermore, the findings reveal a lack of attention given to various concerns and challenges associated with AI utilization in language teaching and learning, such as concerns regarding academic integrity, content authenticity, potential bias, privacy and security issues, and environmental concerns. At present, there is a lack of suitable solutions or regulatory frameworks proposed to address these concerns adequately.
first_indexed 2025-11-15T14:25:28Z
format Article
id upm-115315
institution Universiti Putra Malaysia
institution_category Local University
language English
last_indexed 2025-11-15T14:25:28Z
publishDate 2024
publisher Springer
recordtype eprints
repository_type Digital Repository
spelling upm-1153152025-03-10T08:01:27Z http://psasir.upm.edu.my/id/eprint/115315/ A bibliometric analysis of artificial intelligence in language teaching and learning (1990–2023): evolution, trends and future directions Ma, Huiling Ismail, Lilliati Han, Weijing The advancement and application of Artificial Intelligence (AI) has introduced innovative changes in language learning and teaching. In particular, the widespread utilization of various chatbots as foreign language learning partners showcases their remarkable potential contribution to the field. Nevertheless, there are currently few studies that encompass extensive and holistic reviews and analyses of the relevant literature during this period. The study employs bibliometric analysis and a systematic review of representative research to present trends, the current status and future directions of AI research in language teaching and learning, providing language educators, policymakers, and research scholars with visually accessible and comprehensive insights. Results indicate that the field is in its early stages of development, growing rapidly with significant research potential. The study identified the most productive and influential sources, institutions, authors and countries and provided a summary for the most representative papers in the research field. Through keyword analysis, the study delineates the evolutionary progression of AI in the domain of language teaching and learning across different time periods, identifies prevailing research trends and proposes future research directions. Results indicate that influential research in this realm predominantly focuses on refining technological solutions and conducting empirical studies on AI applications in language teaching and learning. This highlights significant interest in the effectiveness of AI in language education and its implementation methods. However, research on the application of AI in language education is still in its infancy. Therefore, the study advocates for increased empirical research on AI’s specific applications in language listening, speaking, reading, and writing, as well as the development of more effective pedagogical approaches. Furthermore, the findings reveal a lack of attention given to various concerns and challenges associated with AI utilization in language teaching and learning, such as concerns regarding academic integrity, content authenticity, potential bias, privacy and security issues, and environmental concerns. At present, there is a lack of suitable solutions or regulatory frameworks proposed to address these concerns adequately. Springer 2024-06-22 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/115315/1/115315.pdf Ma, Huiling and Ismail, Lilliati and Han, Weijing (2024) A bibliometric analysis of artificial intelligence in language teaching and learning (1990–2023): evolution, trends and future directions. Education and Information Technologies, 29 (18). pp. 25211-25235. ISSN 1360-2357; eISSN: 1573-7608 https://link.springer.com/article/10.1007/s10639-024-12848-z?error=cookies_not_supported&code=aa5aa342-825b-4aa7-880f-cf5fecf0df25 10.1007/s10639-024-12848-z
spellingShingle Ma, Huiling
Ismail, Lilliati
Han, Weijing
A bibliometric analysis of artificial intelligence in language teaching and learning (1990–2023): evolution, trends and future directions
title A bibliometric analysis of artificial intelligence in language teaching and learning (1990–2023): evolution, trends and future directions
title_full A bibliometric analysis of artificial intelligence in language teaching and learning (1990–2023): evolution, trends and future directions
title_fullStr A bibliometric analysis of artificial intelligence in language teaching and learning (1990–2023): evolution, trends and future directions
title_full_unstemmed A bibliometric analysis of artificial intelligence in language teaching and learning (1990–2023): evolution, trends and future directions
title_short A bibliometric analysis of artificial intelligence in language teaching and learning (1990–2023): evolution, trends and future directions
title_sort bibliometric analysis of artificial intelligence in language teaching and learning (1990–2023): evolution, trends and future directions
url http://psasir.upm.edu.my/id/eprint/115315/
http://psasir.upm.edu.my/id/eprint/115315/
http://psasir.upm.edu.my/id/eprint/115315/
http://psasir.upm.edu.my/id/eprint/115315/1/115315.pdf