Texts Generated by Artificial Intelligence: Structure and Semantics

Abstract

The article describes texts generated by artificial intelligence, with an emphasis on Ukrainian-language material, which remains understudied. The aim of the study was to identify specific identifying characteristics of AI texts by comparing their semantic, structural, and stylistic parameters with texts written by humans. The study combined systematic analysis and synthesis, a comparative approach, content analysis, and quantitative linguistic methods (Type–Token Ratio, syntactic complexity analysis by T-unit), as well as semantic modeling, fact verification, and semantic-stylistic analysis. It has been proven that Ukrainian-language AI texts formally meet the basic criteria of textuality (cohesion, coherence, articulation), which are implemented through the probabilistic combination of templates rather than the author’s cognitive and communicative activity. Typical markers of machine generation have been identified: template composition (introduction – main part (3–5 subtopics) – conclusion), homogeneous paragraphs of “average” length, predominance of direct word order, presence of passive constructions, excessive frequency of formal connectors, structural and lexical monotony, errors in word usage. Semantic analysis revealed a combination of formal correctness with factual “hallucinations,” low information density, a predominance of neutral style, superficial, statistically determined expressiveness, and emotional masking. It was concluded that texts generated by artificial intelligence constitute a separate linguistic phenomenon with its own set of characteristics, which requires a special typology and a flexible, updatable analysis methodology. The risks to linguistic norms, speech culture, and information security are emphasized, as well as the need to develop critical competence in users regarding the perception of AI content.