Generative AI (GenAI) in the language classroom: A systematic review

Abstract

The integration of generative AI (GenAI) into language education has attracted significant attention due to its versatile capabilities. However, previous reviews rarely focused on classroom-based research with language learners, a crucial aspect for understanding GenAI's practical application. Therefore, this systematic review addresses this gap by examining 49 empirical studies published between January 2023 and December 2024, focusing on research design, research focus, and the roles and challenges of GenAI in language learning. Findings show that higher education was the dominant setting and English was the primary target language. Although the studies employed diverse research methods, they largely relied on self-reported data. They mainly investigated learner perceptions, such as attitude, self-efficacy, and motivation, with language acquisition studies focusing primarily on writing skills. GenAI served diverse roles, including feedback provider, learning tutor, cognitive stimulator, interaction facilitator, and conversation partner. However, its integration revealed technological challenges, such as content quality and querying issues, as well as educational challenges, including overreliance, pedagogical limitations, and concerns over academic integrity. The review suggests that future research should diversify its research focus and tools, extend studies into K-12 contexts, employ longitudinal designs, explore behavioral interactions with GenAI, and develop customized GenAI applications for personalized learning.

Link to publication in Taylor & Francis

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