AI-ASSISTED CORPORA-BASED TEACHING METHODS AND THEIR DIFFERENCES FROM TRADITIONAL CORPORA-BASED METHODS

Abstract

 The rapid development of artificial intelligence has significantly influenced contemporary language pedagogy, particularly in data-driven and technology-enhanced teaching approaches. One such approach is AI-assisted corpora-based teaching, which integrates traditional corpus-based pedagogy with AI tools to support teachers in analyzing authentic language data and designing effective instructional practices. This article aims to explore the concept of AI-assisted corpora-based teaching, clarify its theoretical foundations, and distinguish it from conventional corpus-based and AI-assisted teaching methods. The study discusses how AI tools can mediate access to large linguistic corpora by automating tasks such as frequency analysis, sense disambiguation, and contextual classification, thereby reducing teachers’ workload and enhancing pedagogical decision-making. Through illustrative classroom examples, including the corpus-based analysis of polysemous vocabulary items , the article demonstrates how AI-assisted corpora-based teaching can improve classroom management, increase learner engagement, and promote evidence-based instruction. The article also examines the advantages and limitations of this approach and highlights its potential contribution to modern language teaching in higher education contexts.