Theoretical Disruption: AI-Driven Language Systems and the Reformulation of Linguistics

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Nuri A., SEYIDOV R., Hashimov A., Kosareva L., Ismayil Z., Alizada P., ...Daha Fazla

Journal of Ethnic and Cultural Studies, cilt.13, sa.3, ss.382-410, 2026 (Scopus)

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 13 Sayı: 3
  • Basım Tarihi: 2026
  • Doi Numarası: 10.66815/ejecs/3065
  • Dergi Adı: Journal of Ethnic and Cultural Studies
  • Derginin Tarandığı İndeksler: Scopus, MLA - Modern Language Association Database
  • Sayfa Sayıları: ss.382-410
  • Anahtar Kelimeler: Artificial intelligence, computational linguistics, distributional semantics, human-AI interaction, language modeling, large language models, pragmatic competence, probabilistic syntax
  • Açık Arşiv Koleksiyonu: AVESİS Açık Erişim Koleksiyonu
  • Atatürk Üniversitesi Adresli: Evet

Özet

This study investigates AI language systems as independent linguistic constructs, evaluating syntactic, semantic, pragmatic, and acquisition mechanisms. We conducted a comparative conceptual and empirical analysis of transformer-based language models, integrating probabilistic syntax mapping, high-dimensional embedding evaluation, and discourse-level context assessment. Data were systematically analyzed from large-scale corpora across multiple AI architectures to quantify emergent structural and semantic patterns. The results showed that syntactic regularities emerged probabilistically, with graded grammatical acceptability exceeding 85% coherence across complex sentence structures. Semantic relationships were distributional, maintaining 78–92% contextual similarity without referential grounding. Pragmatic adaptation occurred algorithmically across 1,000 discourse simulations, while acquisition was fully data-dependent, revealing alternative pathways to functional competence. AI-generated language diverged from human hierarchical grammar yet preserved operational effectiveness. These findings demonstrate that AI systems embody autonomous, non-biological linguistic competence, challenging classical assumptions of grammaticality, meaning, and acquisition. This study provides actionable insights for theoretical linguistics, computational modeling, and the design of advanced human-AI communication systems.