From "No Cap" To Empirical Truth: A Qualitative Inquiry into AI-Driven Register Translation as an Academic Writing Scaffold for Generation Z LG120 Undergraduates
2026· International journal of research and innovation in social science· Vol 10, pp. 594-601· 0 citations
TL;DR
AI is viewed as an equitable learning scaffold that supports students in maintaining their authorial voice and intended meaning while adapting their writing to institutional academic conventions that maintain students' voice and intent while making institutional academic codes accessible.
Abstract
With the rapid surge in the use of this new technology called generative artificial intelligence (AI), university students are also undergoing a transformation in their academic writing. The transition between informal social media and online communication and academic discourse may pose linguistic challenges for LG120 Generation Z undergraduate students whose daily communication is dominated by social media and digital culture. While AI-supported writing has already garnered increased academic interest, the prevailing focus in the current literature has been on ethical issues, academic integrity, and learning outcomes. The use of AI to aid transitions between various language registers is not as well-documented. This concept paper suggests a qualitative study of the academic writing scaffold of LG120 Generation Z undergraduate students in the scope of AI-based register translation. Within the scope of this inquiry, register translation is conceptualized as the strategic application of generative AI to transform colloquial digital vernacular into institutional academic prose, ensuring the preservation of the authorial intent. Furthermore, AI-based academic writing scaffolding encompasses the linguistic and cognitive assistance provided by these technologies, enabling undergraduates to navigate complex disciplinary codes through lexical refinement, syntactic restructuring, and the adaptation of formal stylistic conventions. The study adopts a sociocultural perspective on learning and frames the use of generative AI as a mediating tool, which can help facilitate students' ability to adapt colloquial language into more academic forms. The purpose of the proposed study is to examine how undergraduates are using AI for this purpose, the meaning(s) they are giving to these uses, and whether and how they think the reformulations are helping them to grow as academic writers. Using purposive sampling, approximately 12–15 LG120 undergraduate students who regularly use generative AI for academic writing will be recruited. Data will be collected through semi-structured interviews and analyzed using Braun and Clarke's (2006) thematic analysis to explore students' experiences and perceptions of AI-mediated register translation. The study aims to inform the ongoing debates around the pedagogical implications of the use of generative AI tools for academic literacy, conceptualized as students' ability to communicate effectively using appropriate academic language conventions, disciplinary discourse, and formal writing practices in learning in higher education, and to extend the knowledge about the mediating role of new technologies in the development of academic literacy. In this study, AI is viewed as an equitable learning scaffold that supports students in maintaining their authorial voice and intended meaning while adapting their writing to institutional academic conventions that maintain students' voice and intent while making institutional academic codes accessible.
The massive spread of Generative Artificial Intelligence (Gen AI) into language classrooms has reopened a currently relevant question related with the human teacher’s role, and in some quarters revived the worry that the instructor who once stood as the “Sage on the Stage” has become obviously less important. This narrative literature review asks what actually happens to the roles and professional identities of language teachers as Gen AI has massively intervened their work, with particular attention to English for Specific Purposes (ESP). Following a protocol-guided search of academic databases, thirteen peer-reviewed studies published between 2022 and 2026 were synthesized, read alongside a smaller body of abundant literature on general language teaching. Taken together, the studies point away from displacement and toward reinvention. Teachers, ESP practitioners in particular, are taking on the work of prompt design, drawing on what one study terms AI-pedagogical knowledge to translate tacit expertise into instructions a model can follow. Since Gen AI can produce credible but incorrect content in specific fields such as engineering, medicine, law, etc., ESP teachers also find themselves play roles as domain validators and as guides to critical AI literacy. What emerges is less an authoritative source of knowledge than a facilitator who decides when to lean on automation and when to rely on judgment, context, and rapport that a model cannot supply.
Gregorius Punto Aji, Angelina Kusuma Jelita Mawarni· International Journal of Edu...· 0 citations
The AI-mediated knowledge construction (AMKC) framework is proposed to explain how GenAI may support graduate students' reading-to-write development and provides a theoretically grounded account of GenAI-mediated academic literacy development in higher education.
Yang Jiao, Jing Huang· Region - Educational Researc...· 0 citations
Investigating whether first-year university students perceive a loss of authenticity in their writing when assisted by AI finds that students value AI for reducing anxiety and improving grammatical accuracy, yet many express ambivalence about authorship and personal voice.
Manel Mizab· International Journal of Cur...· 0 citations
Introduction
English as a Foreign Language (EFL) writing is inherently fraught with linguistic uncertainty, which frequently triggers anxiety and stifles learner motivation. While generative AI (GenAI) is increasingly adopted, current research predominantly treats these tools as text-generation shortcuts for final writing products, leaving the cognitive and affective mechanisms of multimodal GenAI as a process-oriented scaffold largely underexplored.
Method
To bridge this gap, this study investigated the effects of a text-to-image (T2I) AI-supported visual narrative intervention on 78 EFL undergraduates' writing anxiety, motivation, and performance through an 8-week quasi-experiment. Specifically, the intervention required the experimental group to engage in an iterative cycle of text input, image generation, and prompt refinement to construct their drafts, whereas the control group employed traditional text-only drafting.
Results and discussion
Quantitative results revealed that the experimental group exhibited significantly lower writing anxiety, alongside enhanced situational writing motivation and a modest but significant improvement in writing performance, compared to the control group. Qualitative thematic analysis of retrospective interviews indicated that, during the intervention, the T2I AI transformed linguistic uncertainty from a psychological threat into a gamified exploration, although the durability of this shift beyond the intervention period remains to be established. The immediate, non-judgmental visual feedback functioned as an autonomy-supportive scaffold, driving learners to notice the gap and autonomously engage in lexical risk-taking and syntactic refinement. However, as the intervention bundled visual feedback with AI immediacy and interactivity, the specific contribution of the visual modality warrants further investigation. This study contributes to reframing the understanding of uncertainty in GenAI-supported EFL writing and highlights the pedagogical affordances of leveraging multimodal AI as a collaborative, affective scaffold rather than a mere linguistic shortcut.
Generative artificial intelligence (GAI) is now widely used in L2 writing, but much existing research still relies on learner attitudes rather than traceable evidence of writing behaviour. This study examines how Jordanian EFL writers judged, used, revised, verified, and sometimes rejected AI‐generated language during actual composing. It asks how students evaluated the alignment between AI wording and their intended meaning, voice, genre purpose, and evidential responsibility; what uptake pathways they followed when working with AI output; and how verification shaped revision depth and authorial ownership. The study draws on a qualitative‐led multiple‐case design with embedded descriptive episode analysis. Thirty second‐year English majors at a Jordanian public university completed three course‐based assignments: a personal narrative, a source‐based argument essay, and a public‐facing opinion piece. The corpus included AI chat logs, draft pairs, writer memos, teacher feedback, verification logs, screen‐session logs, stimulated recalls, diagnostic samples, and descriptive score records. Analysis centred on 196 traceable revision episodes linking prompts, AI responses, writer decisions, and textual changes. Four patterns were identified: fluent but wrong, the comfort of ready‐made English, voice under pressure, and trust must be earned. Students used AI productively for structure, option generation, and sentence reshaping, but resisted wording that generalized local experience, imposed a ready‐made stance, or exceeded available evidence. The study shows that critical AI literacy in L2 writing is enacted through revision, verification, and ownership decisions, not through attitudes alone.
Hesham Aldamen, Mohamad Almashour, Marwan Jarrah· International Journal of App...· 0 citations
The key findings reveal a detection process that synthesizes textual and contextual evidence by combining objective textual indicators with subjective pedagogical assessments, and highlight the significant role of subjective contextual intuition and the ethical and emotional challenges educators face.
Ekrema Shehab, Mohammad Alkhateeb· Journal of Academic Ethics· 0 citations
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