Feng (Kevin) Jiang

In high-stakes academic publishing, author responses to reviewers (ARRs) play a crucial yet underexplored role in the review process. These responses provide researchers with a valuable opportunity to engage in dialogue with expert peers and negotiate revisions that shape their scholarly work. This critical written exchange, however, presents formidable challenges, as authors must walk a fine line between defending their work and accommodating criticism. To examine how this rhetorical tension is managed, this study analyzes stance in 120 ARRs from the British Medical Journal . The findings show how authors deploy stance markers to position themselves in relation to reviewers, who hold significant influence over publication outcomes. Notably, the distribution of stance markers varies significantly across functional units, with gratitude units containing the highest density of stance markers, particularly attitude markers, while justifications more frequently use hedges and boosters. Furthermore, by tracking responses across two rounds of peer review, we show that authors recalibrate their stance: self-mentions and hedges decrease while boosters increase, suggesting a shift from cautious negotiation to more assertive positioning as the review process progresses. By illuminating this occluded genre, the study contributes to understanding how interpersonal resources manage reviewer–author interaction in academic publishing.

This study investigates the discourse characteristics of AI-generated argumentative essays designed to simulate student writing, comparing them with human-authored texts to explore linguistic variation and its implications for academic discourse. While large language models (LLMs) such as ChatGPT demonstrate impressive fluency and coherence, questions remain about their capacity to engage in nuanced argumentation and the critical reasoning expected in academic contexts. Employing Biber’s (1988) multidimensional (MD) analysis, this study examines systematic differences between LLM-generated and human-written essays across dimensions of register variation, informational density, syntactic complexity, and persuasion. The results reveal that ChatGPT essays are markedly more informationally dense, context-independent, and abstract, yet exhibit reduced interpersonal engagement and overt persuasion compared with student essays. The paper concludes by reflecting on broader pedagogical and ethical implications for the integration of AI tools into academic writing and corpus-based research.

Recent corpus‐based studies suggest generative AI systems such as ChatGPT can approximate human patterns of interpersonal metadiscourse in argumentative writing. These conclusions are typically based on AI‐generated texts produced using a single prompt, despite AI output being highly sensitive to even minor changes in prompt specification. This study examines prompt specificity as an elicitation variable in the production of metadiscourse. Using Hyland's interpersonal metadiscourse framework, we first compared university students’ argumentative essays from the LOCNESS corpus with GPT‐generated essays produced in response to a general essay (baseline) prompt specifying role, genre, topic, length and persuasive purpose, but providing no explicit guidance regarding metadiscourse use. We then generated a second GPT corpus using a metadiscourse‐inducing prompt that explicitly requested interactive, interactional, and engagement features that were found to be underrepresented in the initial GPT corpus relative to the LOCNESS corpus. The baseline prompt produced lower frequencies of transitions, hedges, self‐mention, attitude markers and engagement resources than human writing, while the metadiscourse‐inducing prompt substantially increased several target features over the baseline and human conditions. Some categories converged with baseline frequencies, but even where convergence occurred, these were often realised in more formulaic ways. The findings show prompt specificity significantly shapes both the distribution and rhetorical realisation of metadiscourse in AI‐generated writing. More broadly, the results highlight the importance of treating prompt specification as a key methodological variable when interpreting linguistic patterns in AI‐generated texts.

This commentary proposes AI for Applied Linguistics, hereafter AI4AL, as a framework for studying how artificial intelligence is changing language use, language learning and applied linguistic inquiry. Rather than treating AI primarily as a tool for automation, we argue that applied linguistics should examine AI through four connected lines of work. These are AI as a research instrument, AI as an object of applied linguistic inquiry, AI as a site of conceptual renewal and AI as a problem of pedagogy, policy and social justice. We ground this framework in applied linguistics’ long‐standing concern with language as situated social practice, while recognising that contemporary AI systems now participate in writing, translation, feedback, assessment, interaction and knowledge production. The commentary clarifies the scope of AI by distinguishing between predictive, classificatory, assistive and generative systems, with particular attention to the conceptual significance of generative AI. It then develops four domains of inquiry in writing and literacy, genre and discourse, pragmatics and interaction and multilingualism and inequality. AI4AL is presented as a disciplinary agenda through which applied linguistics can study, evaluate and shape AI‐mediated communication.

This chapter explores the recent and growing interest in using diachronic analyses to track changes in EAP research and features of academic research papers. Bibliometrics is a quantitative approach used in library and information sciences to analyse academic publications. It is the approach behind the Science Citation Index and has been used to study the research productivity of individual scholars and countries and to determine the degree of authorial collaboration and research networks in particular fields. In applied linguistics, it has begun to show the most popular research topics or highly cited authors and publications in a field and to explore changes in features such as stance, metadiscourse and interaction and to provide an overview of the main trends in second language acquisition, writing centre research, and key journals in the field of EAP. The results show that EAP has emerged as a theory and research-informed applied endeavour with a global reach, and how some of the key features of academic writing have come to dominate interest in teaching and research.