Year: 2025
Type: book-chapter
Abstract: This paper discusses the implications of large language model artificial intelligence for teaching, learning, testing, and producing Lx academic writing. The paper first provides a brief, non-technical description of artificial intelligence, machine learning, deep learning neural networks, and large language models (LLMs), drawing out the applications of the latter most relevant to the field of Lx academic writing. It then discusses and exemplifies the relevance of LLMs to influential models of academic writing, as outlined by Hyland (2019): structural, functional, creative expression, process, content, and genre-focused writing pedagogies. The next section discusses six major issues raised by the emergence of LLMs: differences in language support, the prevalence of LLM hallucinations, lack of intentionality, redefinition of the knowledge Lx writers need to bring to the writing task, assessment issues, and ethical issues. The paper concludes with recommendations as to how this technology can be incorporated within a consistent and defensible pedagogy, the research agenda needed to support such a pedagogy, and the need for a revaluation of the underpinnings of academic writing that the emergence of this technology necessitates
Authors: Oliver James Ballance, Averil Coxhead
Language: English
Subfield: Linguistics and Language
Field: Social Sciences
Domain: Social Sciences
SDG: Quality education
Open Access status: hybrid
OpenAlex ID: W4414965892