From "No Cap" To Empirical Truth: A Qualitative Inquiry into AI-Driven Register Translation as an Academic Writing Scaffold for Generation Z LG120 Undergraduates

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.