Generative AI in Language Education: Bridging Divide and Fostering Inclusivity
Zhenjie Weng, Yao Fu
Abstract
This systematic review analyses empirical studies on the implementation of Generative Artificial Intelligence (GenAI) in language education to highlight its impact on promoting inclusivity and bridging educational gaps. The review encompasses quantitative, qualitative, and mixed-methods research, with a focus on inclusivity and educational equity accelerated by GenAI across multiple language education contexts, ranging from K-12 to higher education. Analysis shows that GenAI has the potential to substantially increase learner motivation and confidence by providing real-time feedback and generating engaging learning materials. Such tools can adapt to individual learner needs, support students with special education requirements, and empower economically disadvantaged learners. Furthermore, GenAI facilitates intercultural competence by incorporating diverse cultural contents into language learning. By synthesizing current research, this review delineates a comprehensive understanding of GenAI’s role in creating equitable and inclusive language education landscapes, thereby guiding future research, educational practices, teacher training, and policymaking.