Beyond Universalism: Typology & Limits of AI Language Models

This paper examines the limitations of contemporary AI language models through linguistic typology, arguing that implicit universalist assumptions constrain explanatory and applied adequacy. Drawing on Croft's typological framework, the study conceptualizes linguistic diversity not as surface variation but as systematic differences in how languages structure form-function relations. The paper critically explores how AI language models, largely trained on typologically skewed and high-resource data, tend to privilege Indo-European grammatical patterns while marginalizing alternative typological configurations. By foregrounding construction-based analysis, functional motivation, and cross-linguistic diversity, the study demonstrates how typology can serve as a theoretical compass for applied linguistics in this era. It is argued that typological approaches can enhance linguistic inclusivity, reduce typological bias, and improve the interpretability of AI-driven applications such as language education technologies, multilingual assessment tools, and automated language processing systems. The paper ultimately positions linguistic typology as a necessary theoretical resource for rethinking AI-language relations beyond universalist abstraction.

کلیدواژه ها:

Linguistic Typology ، Croft ، AI ، Universalism ، Applied Linguistics