The rapid integration of Large Language Models (LLMs) has catalyzed a paradigm shift in applied linguistics. We explore the synergy between traditional corpus methodologies and artificial intelligence across three core dimensions: resources, practice, and pedagogy. Through a narrative review of nine recent studies presented in this volume, this paper proposes a model for GenAI–corpus integration, moving from manual linguistic labor toward augmented linguistic workflows. We examine how AI automates the compilation of specialized medical corpora, the linguistic monitoring of synthetic text, and the identification of systemic gender biases and cultural mistranslations in machine output. Finally, the literacies required for the next generation of linguists are outlined. This work is intended for researchers, practitioners, and teacher educators seeking a structured approach to AI implementation in translation and language education.
