Lost in Translation: Chomskyan Analysis of Language Variation in AI, Post-Critical Learners, and Native Speakers

Artificial Intelligence (AI) and Large Language Models (LLMs) have revolutionized linguistic theory, raising questions about human language acquisition. Some argue AI's language generation challenges the concept of innate linguistic ability, while others contend it reinforces it. This research examines AI's "Surplus of the Stimulus" learning—relying on vast datasets—against human language acquisition, particularly within Chomskyan frameworks of Universal Grammar and the "Poverty of the Stimulus." By analyzing language production across three groups—Native English Speakers, Post-Critical Period Second Language Learners, and LLMs—this study compares linguistic complexity using the Lexical Opulence Index (LOI). Data is collected through structured prompts across four linguistic registers: literary, simplified, technical, and speculative. Findings will clarify the mechanisms of human language learning, address AI’s limitations in true linguistic acquisition, and inform second-language learning methodologies. This research underscores the fundamental distinctions between AI-generated and human language, supporting theories of innate linguistic structures.