SILENCE IN AI TEXTS: INVESTIGATING THE LOSS OF INTERPERSONAL MEANING IN GENAI-PRODUCED ACADEMIC PASSAGES

A Comparative Study of Reading Essays and the Initial Output of Generative AI : Focusing on Ethos and Reflection

This study aims to compare and analyze human reading essays and the initial outputs of generative AI from the perspectives of ethos and reflection, in a context where the uniquely human domains of reading and writing are being re-examined due to the development of generative artificial intelligence. To this end, a book club was operated at a local small library, and the reading essays written by participants were compared with essays generated by generative AI (ChatGPT 5.2) after being provided with the same full literary texts. The participants’ responses during the sharing and discussion process were also analyzed. The results showed that the initial outputs of generative AI were strong in terms of logical coherence and structural completeness, but had limitations in embodying uniquely human emotional and existential qualities, such as embodied experiences rooted in real life, incomplete emotions, and the inability to read aloud. In contrast, human reading essays revealed self-identity and ethical attitudes based on lived experience and reflection, thereby forming deep empathy and trust with readers. This study is significant in that it demonstrates that, even in the age of AI, reading essays remain an important educational practice for forming uniquely human reflection and ethos, while also shedding new light on the humanistic value of reading education.

Human-AI Academic Writing, Integrity, and Language-Communicative Culture in Teacher Education: Evidence from Ukrainian Student Texts

Generative artificial intelligence has turned academic writing into a hybrid communicative process in which human authors interact with language models, automated editing systems and digital source environments. For teacher education, this shift is not only a question of academic misconduct; it is a question of language-communicative culture, authorship, source accountability and professional responsibility. The article examines how Ukrainian student academic texts reveal linguistic, compositional, terminological and integrity-related risk zones that become especially significant in AI-mediated academic writing. The empirical material consists of 26 anonymised student texts: 12 course papers, 8 master’s theses and 6 research articles. The study applies qualitative content analysis with descriptive quantification across four textual categories: lexico-stylistic normativity, logical-compositional cohesion, citation and source use, and terminological control. The findings show that only 19% of texts demonstrated a high level of language-communicative culture, while 54% were sufficient and 27% were low. Lexical and stylistic violations were identified in 65% of texts, citation and source-use problems in 38%, cohesion problems in 35%, and terminological imprecision in 19%. The article argues that these weaknesses should not be treated as isolated language errors but as indicators of preparedness for responsible human-AI academic writing. The study’s contribution is a text-centred framework that connects academic literacies, language-communicative culture, and AI-use transparency in teacher education.

Gendered asymmetries in AI stories in English and Spanish

Creative texts produced wholly or partly by large-scale generative language models are increasingly circulating within literary markets and cultural institutions. Research on AI-generated texts has largely examined bias at the level of words and images; however, small-scale statistical co-occurrences in AI-output can generate macro-scale narrative phenomena, where narrative functions intersect with stylistic texture, for example through voice, focalisation, reliability, or omission. This article argues that literary criticism, and New Formalism in particular, offers a necessary methodological framework for identifying and historicising these stylistic asymmetries. A background premise, working against the current techno-utopian ethos that improving algorithms requires more algorithms, is that close, slow reading is necessary to addressing the ethical complexities in literary form, the unsettled nature of language, and the context-specific nature of bias, harm, and oppression (Jackson and Courneya 2023, p. 62). Through a new formalist reading of English- and Spanish-language short stories generated by ChatGPT-5, I demonstrate that distributional regularities in language modelling scale upward, producing gendered and culturally normative stylistic patterns. By shifting the analysis of AI creativity from representation to form, I hope to indicate how literary methods can illuminate narrative-specific dimensions of bias in generative AI output.

When Algorithms Read Folklore: AI, Simulated Reception, and Cross-Cultural Studies of Literary Interpretation

As artificial intelligence increasingly enters literary classrooms and digital humanities research, the question is no longer whether AI can produce interpretations, but whether it can meaningfully “read” culturally embedded texts. This study addresses a critical gap in AI-literature scholarship: limited attention has been given to AI-generated interpretation as a form of simulated reception, particularly in cross-cultural folklore contexts where meaning depends on emotion, moral judgment, and cultural situatedness. Grounded in reader-response and reception theory, this qualitative comparative study examines human and AI responses to four folktales: Bawang Merah Bawang Putih, Timun Mas, Cinderella, and Jack and the Beanstalk. Data were drawn from 30 undergraduate students’ reader responses and AI-generated interpretations using the same interpretive prompts. The analysis focused on three dimensions: intellectual, emotional, and cultural interpretation. The findings show that human readers produced varied, affective, morally evaluative, and culturally grounded responses, while AI generated structurally coherent but consistently affirmative interpretations. AI responses displayed algorithmic positivity, emotional flattening, and cultural abstraction, especially when confronting moral ambiguity, gendered agency, suffering, punishment, and cross-cultural value conflict. The originality of this study lies in conceptualizing AI as a simulated reader rather than an interpretive subject and in showing how folklore exposes the cultural limits of algorithmic interpretation. The study contributes to literary education, reception theory, folklore studies, and digital humanities by positioning AI not as a replacement for human interpretation, but as a comparative artifact for developing critical AI literacy and culturally responsive literary pedagogy.

JOURNAL OF COMMUNICATION, LANGUAGE AND CULTURE

AI Translation in Poetry and Prose: Loss, Gain, and the Aesthetics of Machine Voice

Artificial Intelligence (AI) has transformed the field of literary translation by introducing new possibilities and challenges in the translation of poetry and prose. This study examines the phenomenon of AI-mediated translation through the lens of Translation Studies, focusing on the dynamics of loss, gain, and the emerging aesthetics of machine voice. It explores the long-standing tension between fidelity and creativity and investigates how AI systems influence linguistic, cultural, and aesthetic dimensions of literary texts. The study identifies significant areas of loss, including diminished prosodic patterns, weakened sensory and emotional resonance, and cultural and intertextual gaps that affect the preservation of meaning and literary expression. Conversely, AI translation also offers notable gains, such as expanded multilingual accessibility, wider global circulation of literary works, enhanced efficiency, and the development of new hybrid literary forms. Furthermore, the study analyzes the concept of machine voice as a distinct aesthetic phenomenon that reshapes reader interpretation and challenges traditional notions of authorship and originality. Through theoretical analysis and comparative perspectives on AI- and human-mediated translations, the paper argues that AI translation should not be understood solely as a technological tool but also as a transformative force redefining literary production, reception, and cross-cultural communication. The study concludes that while AI introduces inevitable losses in literary translation, it simultaneously creates new forms of creative and interpretive possibilities that demand continued scholarly engagement. Keywords: Artificial Intelligence Translation, Machine Voice Aesthetics, Literary Translation, Fidelity and Creativity, and Poetry and Prose Translation.