THE ARCHITECTURE OF PREDICTABILITY: SYNTACTIC FLATTENING IN AI-MEDIATED ENGLISH
International Journal of Computer Science (IJCS) Published by SK Research Group of Companies (SKRGC)
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Abstract
While much of the contemporary anxiety surrounding artificial intelligence in text generation focuses on lexical repetition and vocabulary choices, a more profound linguistic shift is occurring at the structural level. Large Language Models (LLMs) are progressively imposing a predictable architectural blueprint onto English syntax, leading to the rapid homogenization of digital discourse. This paper examines the phenomenon of "syntactic flattening," wherein algorithmic text generation curtails syntactic complexity and dilutes regional linguistic variance. By analyzing the mechanics of algorithmic syntax—specifically the dominant "four-beat progression" of framing, expansion, concession, and resolution—this study demonstrates how AI tools are shifting from descriptive grammar aids to prescriptive syntactic authorities. Furthermore, it explores the cognitive implications of this shift, arguing that frequent exposure to AI-mediated English establishes a new, machine-driven cognitive baseline for acceptable professional and academic writing. Ultimately, this structural standardization threatens stylistic diversity, sanitizing everything from daily interpersonal digital communication to official institutional narratives.
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Keywords
Anxiety, Artificial intelligence, Large Language Models, Cognitive implications , English
