Linguistic Features of Selected AI-Generated Short Stories
Kata Kunci:
artificial intelligence, AI-generated text, linguistic features, short stories, stylistics, syntax, lexico-semanticsAbstrak
The increasing deployment of artificial intelligence (AI) in creative writing has generated substantial interest in the linguistic qualities of machine-produced literary texts. This study examines the linguistic features of selected AI-generated short stories, with particular attention to syntactic structures, lexico-semantic choices, figurative expressions, and graphological organisation. Adopting a qualitative descriptive design grounded in corpus stylistics, the study analyses twenty short stories generated using three open-access large language models (ChatGPT-4, Claude 2, and Google Gemini) between January and March 2025. The analysis draws on the theoretical frameworks of stylistics and Systemic Functional Grammar. Findings reveal that AI-generated stories demonstrate considerable linguistic consistency across multiple levels of textual organisation. Syntactically, the texts rely predominantly on declarative structures, paratactic sentence patterns, parallelism, and anaphoric repetition, producing a measured but formulaic narrative rhythm. At the lexico-semantic level, vocabulary is organised around recurring semantic fields including isolation, time, memory, technology, and loss, with limited lexical innovation beyond conventional literary associations. Figurative expressions, particularly metaphor, simile, and personification, are employed frequently but tend to follow established literary patterns rather than generating novel conceptual mappings. Graphologically, the stories maintain orderly paragraphing, standard punctuation, and conventional formatting, alongside occasional orthographic irregularities characteristic of machine generation. The study concludes that while AI can reproduce many conventional linguistic features of literary prose with remarkable fluency, its textual patterns remain relatively consistent, formulaic, and constrained by the probabilistic architecture of language models. These findings carry implications for stylistic theory, digital literary studies, and the pedagogy of creative writing in an era of machine-generated text. The study recommends that future research adopt comparative frameworks that directly examine human-authored and AI-generated texts using identical prompts to more precisely delineate the linguistic boundaries between human and machine creativity
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