Aug 2026· International Journal of Advanced Research· 0 citations
TL;DR
It is revealed that GenAI texts underrepresent the subtle interpersonal cues that give writing its persuasive, dialogic, and ethical texture, though they excel in both grammatical accuracy, and lexical variety.
Abstract
The growing use of Generative Artificial Intelligence (GenAI) in English for Academic Purposes (EAP) teaching has profoundly changed the way teachers and students write and read academic texts. Nevertheless, behind the fluency and accuracy of the passages generated by AI-machines, lies a subtle silence viz. the disappearance of the human voice that gives writing its warmth, stance, and dialogue. The present study examines how GenAI-produces academic passages, expresses, or fails to express, interpersonal meaning. Grounded in the appraisal theory of the Systemic Functional Linguistics (SFL) framework, the study compares AI-produced and human-written texts on two distinct EAP topics. It uses a combination of corpus analysis and qualitative discourse examination to scrutinize how these texts express attitude, hedge claims, or invite reader alignment, and authorial presence. The findings revealed that GenAI texts underrepresent the subtle interpersonal cues that give writing its persuasive, dialogic, and ethical texture, though they excel in both grammatical accuracy, and lexical variety. Moreover,they sound too neutral, use fewer engagement signals, and lack guiding phrases that connect ideas. Such loss of interpersonal nuance in GenAI texts causes them to sound correct but detached from the intellectual dialogue that academic literacy requires.
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.
Seokkyun Lee· The Korean Association of Ge...· 0 citations
The article argues that weaknesses should not be treated as isolated language errors but as indicators of preparedness for responsible human-AI academic writing, and is a text-centred framework that connects academic literacies, language-communicative culture, and AI-use transparency in teacher education.
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.
This qualitative comparative study examines human and AI responses to four folktales, conceptualizing AI as a simulated reader rather than an interpretive subject and showing how folklore exposes the cultural limits of algorithmic interpretation.
A. Soraya, Rezky Ramadhani, Muthmainnah Bahri A. Bohang· Utamax Journal of Ultimate R...· 0 citations
Public responses to teacherless classrooms are shaped by specific ideologies of humanity, morality, labour, and educational authority, demonstrating how public responses to teacherless classrooms are shaped by specific ideologies of humanity, morality, labour, and educational authority.
It is argued 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.
Nyiramukama Diana Kashaka· NEWPORT INTERNATIONAL JOURNA...· 0 citations
A new method for surgically removing training examples from a model reveals that as datasets grow, the link between what a model learns and what it produces dissolves.
