TRANSFORMATION OF KAZAKH PROVERBS AND SAYINGS IN DIGITAL DISCOURSE AND THEIR ADAPTATION FOR ARTIFICIAL INTELLIGENCE SYSTEMS

Authors

  • Ekrem Ayan Muğla Sıtkı Koçman University Author
  • Orken Kurmanaliuly Abdirov Sh. Shayakhmetov “Til-Qazyna” National Scientific and Practical Center Author

Keywords:

paremiology, paremiogenesis, Kazakh proverbs and sayings, proverb transformation, paremiological frame, expectation effect, artificial intelligence, large language model, JSON

Abstract

This article examines contemporary paremiogenesis in the Kazakh language in relation to digital communication. Using traditional proverbs and their transformed versions as material, the study describes the stability of structural frames, the pragmatic mechanism of the expectation effect, and the conflict between inherited and emerging values in network discourse. Three types of proverb transformation are distinguished: distorted, antithetical, and transient proverbs. The article also argues that proverbs and their variants should be represented as structured data for artificial intelligence systems and large language models. A JSON-based model is proposed for recording the canonical form, transformed form, logical model, semantic interpretation, and pragmatic function of a proverb. Such an approach can help distinguish original, modified, ironic, and temporary proverb forms and reduce the risk of reliable data debt in Kazakh-language digital resources.

Keywords: paremiology; paremiogenesis; Kazakh proverbs and sayings; proverb transformation; paremiological frame; expectation effect; artificial intelligence; large language model; JSON.

Author Biographies

  • Ekrem Ayan, Muğla Sıtkı Koçman University

    Prof. Dr., Faculty of Letters Department of Contemporary Turkish Dialects and Literatures Head of Department

  • Orken Kurmanaliuly Abdirov, Sh. Shayakhmetov “Til-Qazyna” National Scientific and Practical Center

    PhD, Leading Researcher

References

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Published

2026-07-23

Issue

Section

Linguistics and Artificial Intelligence