The impact of generative artificial intelligence on language teaching and learning

Abstract This paper examines the existing research on generative artificial intelligence (GenAI) in language education and highlights its potential to reshape teaching practices, learner engagement, and pedagogical design. By leveraging co-word analysis and BERTopic modeling on 908 publications from 2023 to the end of 2025, the article traces thematic patterns and conceptual developments in the GenAI field. The co-word analysis identifies key clusters at the intersection of GenAI and English language instruction, including its use in academic writing, translation, assessment, and learner-centered pedagogy. These themes demonstrate the critical role of GenAI in enabling personalized feedback, adaptive learning environments, and enhanced teacher–student interaction. The application of BERTopic modeling adds a semantic layer to this analysis and reveals diverse topics such as AI-assisted writing, teacher engagement with AI tools, emotional and motivational dynamics, and technology acceptance among learners. Results also indicate a progression from broad pedagogical experimentation toward more focused inquiries into learner psychology, ethical use, and professional development. This trend reflects the maturation of GenAI applications in both formal and informal educational contexts. The findings offer implications for language educators, curriculum designers, and policymakers by highlighting the need to integrate GenAI through pedagogically grounded, ethically responsible, and learner-centered approaches. They also suggest that future language education practices should combine AI literacy, transparent assessment policies, and teacher professional development to support effective and equitable GenAI adoption. Ultimately, the article positions GenAI as a transformative force in language education that offers technological innovation and new frameworks for inclusive, responsive, and emotionally attuned instruction.