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Introduction The way we understand language—how it is acquired, processed, and produced—has shifted considerably over the past few decades. In language teaching, Michael Lewis’s Lexical Approach challenged traditional grammar-first models by emphasizing the importance of word combinations, or collocations , in language use. In the field of artificial intelligence, large language models (LLMs) like GPT-4 or Claude have also abandoned rule-based systems in favor of pattern recognition. Though born in different disciplines, both approaches rest on a similar foundation: that fluency and meaning emerge not from abstract rules, but from repeated exposure to patterned language. This article explores the surprising alignment between Lewis’s pedagogical model and the operational logic of LLMs. By examining the underlying principles of the Lexical Approach, and mapping them onto the behavior of AI text generators, we can better understand not only how machines learn to write, but also how h...

Introduction Language has long fascinated philosophers, linguists, and teachers alike. In his book The Lexical Approach , Michael Lewis presents key principles that challenge conventional views of language learning and teaching. Among these principles are the assertions that " Language consists of grammaticalized lexis, not lexicalized grammar " and " The grammar/vocabulary dichotomy is invalid; much language consists of multi-word 'chunks' " (Lewis, 2002). These principles, central to the Lexical Approach, prompt a reevaluation of traditional language pedagogy and draw intriguing parallels with the linguistic theories of Ferdinand de Saussure. Exploring the Lexical Approach In his  elucidation of the Lexical Approach, Lewis asserts that " Language consists of grammaticalized lexis, not lexicalized grammar ," emphasizing the central role of vocabulary in language acquisition and usage. This approach shifts the focus from isolated grammar r...