Abstract:

Artificial intelligence (AI) has rapidly influenced almost every aspect of contemporary life, transforming the ways people work, communicate and access information. Education has not remained unaffected by these developments, as AI-driven technologies are increasingly integrated into teaching and learning processes. In the field of language teaching and learning in particular, AI-driven tools enable personalized learning experiences, immediate feedback and adaptive instructional support that can respond to learners’ individual needs and progress. Moreover, AI offers new possibilities for personalised learning, automated feedback and more adaptive approaches to language acquisition.

Our study utilises a bibliometric analysis and science mapping approach to explore the landscape of artificial intelligence in language teaching and learning. The methodology involved searching the Web of Science database for AI and language education-related terms in the title and keyword fields. The results were limited to journal articles and conference papers published through 2026. The data were analysed using Bibliometrix/Biblioshiny and VOSviewer. The most influential papers, as well as the productivity and influence of authors, sources and institutions were examined. The most frequently used keywords were identified, and terms from titles and abstracts were studied using a co-word analysis to identify important research topics and trends.

The number of documents increased the most after 2021. The most cited papers discuss the use of ChatGPT in language teaching and learning, its impact on motivation and learning achievements, and collaboration with human teachers. The most prolific journals are System, European Journal of Education, Computer Assisted Language Learning and Education and Information Technologies, each with more than 50 articles. Articles published in Computer Assisted Language Learning and Education and Information Technologies are the most influential, with over 1,900 citations. The most productive authors are Di Zou from Hong Kong Polytechnic University (China) and Bin Zou from Xi'an Jiaotong-Liverpool University (China), each with 16 documents. Ali Derakhshan from Golestan University (Iran) is the most influential, with more than 300 local citations. The Chinese University of Hong Kong and the Education University of Hong Kong are the most prolific and most cited among institutions. The most frequently used keywords are artificial intelligence, generative AI, ChatGPT, technology and language learning.

Science mapping of terms from titles and abstracts revealed four thematic clusters related to:
(1) AI chatbots, learner engagement, foreign/second language learning, reviews;
(2) EFL teachers, teaching methods, qualitative studies;
(3) technology acceptance, reliability, self-efficacy, intention, performance, structural equation modelling;
(4) EFL learners, motivation, perception, enjoyment, quantitative and mixed methods.

Recent research topics from titles and abstracts include terms such as informal digital learning, Chinese EFL learner, emotional response, gain and classroom setting, while the most influential are foreign language enjoyment, informal digital learning, personalised learning, Chinese EFL context, teacher educator and writing skills.

This study helps scholars better focus their research efforts by highlighting significant research trends to help them prioritise future work.

Keywords:

Artificial intelligence, language teaching, language learning, language education, bibliometric analysis.