Jun 2026· The Korean Association of General Education· 0 citations· 15 references
Abstract
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.
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
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.
This study aims to examine the necessity of writing education in the age of artificial intelligence (AI) and to explore its future directions. To this end, the study analyzes key scenes from the film Her, which portrays emotional interactions between humans and AI, and identifies the unique values of human writing that cannot be replicated by artificial intelligence from three distinct perspectives.
In the film, the protagonist Theodore’s writing emerges as a reflective practice that integrates emotion and cognition based on lived experience, memory, and temporality. In contrast, Samantha, an AI operating system, demonstrates the inherent limitations of AI-generated language, as her responses remain grounded in data-driven and instantaneous processing rather than genuine existential experience. This contrast reveals that human-centered writing is deeply connected to distinctly human cognitive activities, such as the sharing of embodied emotions, the formation of identity, and the recognition of complex social relationships.
Based on this analysis, the study proposes three educational directions for writing education in the AI era: empathetic writing, reflective writing, and social writing. By moving beyond discussions centered on the efficiency and convenience of AI technologies, this study reexamines the essence of human writing and suggests practical implications for applying human-centered writing education within the context of university general education.
Min-gyu Song· Korean Association for Liter...· 0 citations
Generative artificial intelligence (AI) tools based on large language model (LLM) technology are transforming processes of creation, writing, and learning in higher education, raising questions about authorship, originality, and academic responsibility. Despite the growing body of research, existing studies mainly focus on regulation and plagiarism, while paying less attention to broader transformations in creativity and academic ethics. The aim of this article is to analyse how generative text reshapes the understanding of creative activity and academic ethics in educational and research contexts. The study adopts a qualitative, theoretical-analytical approach combining hermeneutic and discourse analysis. The analysis is based on three illustrative cases from language learning, translation practice, and academic writing, which are examined as analytical instances to explore emerging ethical and cultural tensions. The findings indicate that the key challenges associated with generative AI are primarily cultural and pedagogical rather than technological. Generative systems redistribute creative agency between human actors and algorithmic tools, transforming the role of the author into that of an editor, coordinator, and ethical decision-maker. While AI enhances productivity and linguistic accuracy, unreflective use risks diminishing interpretive depth, personal voice, and value-based reasoning. The results highlight the need to reconceptualise academic integrity as a reflective process-oriented practice and to develop educational frameworks that promote ethical literacy, transparency, and responsible authorship. The study contributes to the field by offering an integrative perspective that positions generative AI as a catalyst for rethinking creativity, authorship, and ethical responsibility in contemporary higher education.
Giedrė Paurienė· Mokslo taikomieji tyrimai /...· 0 citations
This study empirically investigates the stylistic and structural characteristics of reflective essays written by students highly dependent on artificial intelligence (AI)—a genre meant to capture unique individual experiences and subjective insights. Using a self-reported dataset in which students voluntarily disclosed their degree of AI reliance upon submission, thirty essays with an AI dependency rate exceeding 30% were selected for analysis. While existing literature has predominantly favored quantitative research on English-language data, this study qualitatively examines how AI intervention transforms thought structures and narratives in Korean subjective texts. The analysis reveals that, due to the next-token prediction and local optimization mechanisms inherent in Large Language Models (LLMs), such texts display a distinct “conceptual emptiness” in which surface-level fluency coexists with inner superficiality. Specifically, an abnormal excess of cliché-ridden metaphors and a standardization of sensory and synesthetic expressions led to data homogenization, while a lack of macro -level lexical coordination resulted in the overexposure of deictics and discourse markers such as *gyeolguk* and *ije*. Syntactic frameworks-negative constructions and the “Not A but B” contrastive structure-were mechanically repeated, and narratives relied on conclusion-oriented storytelling paired with formulaic, hopeful closures. These findings offer concrete linguistic criteria enabling educators to perceive patterns of AI intervention in student assignments, serving as a foundational resource for ensuring academic integrity and establishing practical guidelines for AI use in the generative AI era.
Yeon-jeong Lee· The Korean Language and Lite...· 0 citations
The rapid expansion of generative artificial intelligence (AI) has intensified debates on authorship and authenticity in academic discourse, yet empirical evidence remains limited. This ex post facto study examines how AI has transformed textual quality in education research. A corpus of 1,000 open access articles indexed in Google Scholar was analysed, comparing papers published up to 2021 (pre-AI) with those from 2024 onwards (AI era). Textual quality was assessed using a validated 25‑item instrument covering five dimensions: orthographic and grammatical accuracy, cohesion and coherence, adequacy to academic register, style and readability, and formal conventions. Results reveal significant differences. Pre‑2021 articles scored higher in accuracy, cohesion, register adequacy, and formal conventions, while post‑2024 articles excelled in style and readability. Findings indicate a discursive shift: AI enhances accessibility and fluency but may compromise rigour and authorial distinctiveness. These results highlight the need to reassess academic writing standards in digitalised contexts.