Jul 2026· Utamax Journal of Ultimate Research and Trends in Education· 0 citations
TL;DR
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.
Abstract
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.
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.
Dr. Daniel Tchorkpa Yokossi, Dr. Servais Dieu-Donne Yedia Dadjo, Dr. Cocou Andre DATONDJI· International Journal of Adv...· 0 citations
For most of literary history, the question of who wrote a text carried an assumption so obvious it rarely needed stating: a person did. The fluency of contemporary large language models has unsettled that assumption, producing sonnets, short stories, and novel chapters that readers in controlled studies frequently cannot distinguish from human-authored work. This paper argues that AI-generated literature does not so much destroy literary authenticity as force a reconfiguration of what the term has always meant. Synthesizing literary theory, empirical reception research, copyright law, and consumer psychology, the paper traces four converging lines of evidence: readers judged blind often cannot detect machine authorship and sometimes rate it more favorably, yet the same readers penalize a text emotionally once its AI origin is disclosed; structural analyses show AI-generated fiction and poetry remain measurably less inventive than the best human work even when formally polished; and copyright authorities have sidestepped the detection problem entirely by anchoring protection to traceable human decision-making rather than textual quality. Revisiting Roland Barthes's "death of the author" alongside a 2016 case in which an AI-assisted novella nearly won a Japanese literary prize, the paper argues that authenticity was never a property readers detected inside a text but a social practice negotiated among readers, critics, publishers, and legal institutions. It concludes that a more defensible, procedural model of authenticity is emerging one grounded in disclosed, traceable human judgment rather than the simple fact of a byline.
Keywords : AI-generated literature; authorship; authenticity; large language models; Roland Barthes; computational creativity; copyright law; literary reception studies
Dr Shipra Malik· International Journal of Cre...· 0 citations
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
This essay contributes to contemporary debates on artificial intelligence and qualitative inquiry by reflecting on what I argue are originary and epistemological limitations of AI. It interrogates these limits through a comparative engagement of AI-generated narratives and researcher-authored autoethnographic vignettes. While algorithms can reproduce structure and stylistic form, they remain devoid of our ordinary, specific realities, not archived on the internet, our lived memory and reflexive consciousness, which are the foundations of authentic autoethnographic practice. Grounded in embodied experiences of memory, materiality, and cultural belonging, the study demonstrates how autoethnography resists algorithmic generalization by foregrounding specificity, and situated truth. Through a comparative analysis of epistemic parameters (agency, data, methods, tools, techniques, results, origination, and growth), it delineates the ontological gulf separating computational synthesis from human meaning-making. Pedagogical experiments in a writing classroom further illustrate how students discern the irreplaceable value of specific lived knowledge when confronted with AI’s simulations. Ultimately, the work argues that autoethnography functions not merely as a qualitative method but as a phenomenological mode of being and knowing that safeguards the primacy of anthropic subjectivity against machinic imitation. In an age when digital systems increasingly simulate human expression, the findings affirm autoethnography as an epistemological site of resistance: reflexive and irreducibly human.
Artificial intelligence has expanded the speed and accessibility of multilingual translation, yet its consequences for gender representation and literary meaning require more precise claims than are often made. This critical narrative review synthesises evidence from ten recent publications in Scopus-indexed journals or conference proceedings, three directly relevant articles from Acta Humanitatis, DIALOGICA, and AI, Law, Politics, and selected foundational works in discourse, gender, and translation studies. It asks which forms of gender bias are documented in machine translation and large language models, how those findings intersect with the demands of literary translation, and what research protocol is needed for accountable human–AI interpretation. The literature provides convergent evidence of masculine defaults, occupational stereotyping, and failures to preserve gender-neutral or ambiguous forms in defined multilingual settings. It also shows that surface fluency does not guarantee creativity, narrative engagement, poetic force, or intercultural adequacy. At the same time, benchmark findings cannot be generalised automatically to every language pair, model, prompt, genre, or literary corpus. The review therefore rejects both technological determinism and uncritical claims of algorithmic agency. It proposes a transparent protocol centred on identifiable texts, model and prompt documentation, repeated runs, preserved outputs, bilingual evaluation, linguistic evidence, negative cases, and explicit human responsibility. An intergenerational research agenda is outlined, while no age-related empirical effect is claimed.
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.