Corpus linguistics and AI in the reconfiguration of language learning ecologies

Abstract This talk examines how corpus linguistics and artificial intelligence treasure the potential to reshape contemporary language learning ecologies. It argues that the rapid normalisation of generative AI has intensified the need for pedagogical models that combine low-friction access to language support with transparent methods grounded in attested usage. Drawing on ecological perspectives and recent empirical research, the talk shows how AI-driven environments expand opportunities for language learning while creating risks related to opacity and over-reliance. Corpus linguistics, data-driven learning and corpus literacy offer a complementary foundation by providing traceable evidence, reproducible analyses, and practices that foster learners’ critical judgement. Two convergence scenarios are proposed: AI as an extension of DDL, and corpus literacy as the operational core of critical AI literacy. Together, these scenarios illustrate how open-box pedagogies can reconcile responsiveness and accountability, ensuring that AI-mediated learning remains anchored in transparent processes and empirically grounded language knowledge.