Developing Pre-Service English Teachers’ Communicative Competence Through Ai-Integrated Learning

This article examines the potential of AI-integrated learning for developing communicative competence among pre-service English teachers. In contemporary teacher education, communicative competence remains a core professional outcome because future teachers are expected not only to use English accurately, but also to communicate meaningfully, contextually, and pedagogically in diverse academic and classroom settings. At the same time, rapid advances in artificial intelligence have created new opportunities for language learning through adaptive feedback, interactive dialogue, automated scaffolding, simulation, and personalized practice. This paper adopts a conceptual and integrative review approach to synthesize foundational theories of communicative competence with recent scholarship on artificial intelligence in language education and teacher training. The analysis draws on classical works by Hymes, Canale and Swain, Bachman, Celce-Murcia et al., and Richards, together with recent studies by UNESCO, Salas-Pilco et al., Ng et al., Mishra et al., Du and Daniel, Tai and Chen, Wang et al., and others. The study argues that AI-integrated learning can significantly support the development of grammatical, discourse, sociolinguistic, and strategic dimensions of communicative competence when it is used in a human-centered, ethically guided, and pedagogically purposeful manner. The article concludes that pre-service English teacher education should move beyond occasional technology use and instead embed AI literacy, reflective pedagogy, dialogic interaction, and critical evaluation into the curriculum in order to prepare future teachers for AI-enhanced language education.

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