The subject of the research is the phenomenon of the "artificial correlate" of folk tales – a text generated by a large language model that imitates the stylistic and plot framework of the folkloric original. The authors thoroughly examine the lexical, morphological, and syntactic features of authentic folk tales and their generated counterparts. Special attention is given to the analysis of compositional, ritual-mythological, and semiotic characteristics of the folkloric fairy tale text and the artificially generated tale. The aim of the work is to construct a theoretical model that allows for the parameterization of the "naturalness" of fairy tale discourse and to identify the ontological gaps between authentic folklore and its algorithmic imitation. The research material comprises authentic folk tales in Russian (from D.K. Zelinin's collection), as well as fairy tale texts generated by the following LLMs: GigaChat and AliceAI. The research methodology includes corpus analysis, conceptual modeling, methods of computational linguistics, as well as elements of quantitative and statistical analysis using the Python programming language. The scientific novelty of the research lies in the development of a multi-level model for assessing the naturalness of fairy tale narratives, which, unlike existing technical approaches, takes into account the mythological, ritual, and ethical constants of the genre. For the first time, a comprehensive analysis of the deficiencies of the generated text is conducted, not as technical errors, but as symptoms of the model's failure to comprehend the culturally significant code. The main conclusion is the demonstration that the artificial correlate largely successfully reproduces the superficial attributes of the genre ("the texture" of the tale), but exhibits significant deficiencies at the level of motif structure and ethical causality. It has been established that a key difference between a natural fairy tale and its digital counterpart is the presence of a rigid ritual-mythological foundation that ensures the teleology of the plot. The generated correlate, on the contrary, demonstrates a "fragmentary" nature: mechanical combinations of folkloric clichs without maintaining the internal logic of the fairy tale world, as well as euphemization of archaic motifs. The developed model of substantive parameters allows not only to diagnose the nature of the text but also raises questions about the limits of artificial intelligence's ability to reproduce culturally significant codes.
Drozhashchikh et al. (2026) studied this question.