Generative AI and linguistic diversity in academic writing and publishing: Perspectives from World Englishes

The rise of generative artificial intelligence (GenAI) in academic writing and publishing (AWP) raises questions about linguistic inclusivity and the legitimacy of diverse Englishes in global scholarly communication. This article responds to these questions through a structured scholarly dialogue involving five sociolinguists from World Englishes and adjacent fields. Organised around five guiding questions, the dialogue interrogates how GenAI tools influence writing practices, reinforce or disrupt dominant language norms, and raise ethical challenges. Contributors reflect on the potential of GenAI to democratise writing processes while also raising concerns about GenAI's tendency to marginalise minoritised varieties and flatten nuance in scholarly writing. Across the dialogue, themes of linguistic (in)justice, researcher agency, and institutional responsibility emerge, with contributors calling for equity-informed policies, critical AI literacy, and inclusive co-design in GenAI development. The article shows the value of dialogic reflection in understanding GenAI's role in AWP. It concludes that while GenAI may reinforce existing hierarchies, it can also serve as a site of resistance, depending on how it is designed, governed and used within scholarly communities committed to linguistic diversity.

Measuring How Students Rely on Generative AI in Academic Writing: Development and Multi-Source Validation of the Generative AI Reliance Types Scale (GenAI-RTS)

As generative AI (GenAI) becomes increasingly embedded in undergraduate academic writing, how students rely on these tools, rather than simply whether they use them, has become a central question for learning, academic integrity, and educational equity. Existing measures of reliance were developed inductively, focused on discrete problem-solving tasks, and validated mainly with homogeneous samples. This study developed and validated the GenAI Reliance Types Scale (GenAI-RTS), a 20-item instrument measuring four theoretically derived types of GenAI reliance: Strategic, Instrumental, Dependent, and Dialogic. Validation followed the multisource framework of the Standards for Educational and Psychological Testing, drawing on a survey of 382 undergraduates at a U.S. Minority-Serving Institution and interviews with 14 purposively sampled students. Confirmatory factor analyses of six competing models supported a five-factor structure in which Strategic Reliance comprises two facets, Deliberate Use and Critical Evaluation, alongside Instrumental, Dependent, and Dialogic factors (CFI = .92, RMSEA = .08; DWLS CFI = .98, RMSEA = .07). Subscale reliability was acceptable to good (omega = .75-.88), and scalar measurement invariance held across gender, first-generation status, and STEM/non-STEM majors, to our knowledge the first such evidence for a GenAI reliance instrument. Rasch analysis indicated that a five-point response format would improve category functioning. Strategic reliance was positively associated with AI literacy, and the reliance types differentiated students across multiple writing process and outcome variables. The GenAI-RTS offers researchers and educators a theoretically grounded, psychometrically validated instrument for identifying undergraduate reliance profiles and supporting research, assessment, and AI literacy intervention.

Co-Writing with AI: An Empirical Study of Diverse Academic Writing Workflows

Despite AI tools becoming increasingly embedded in academic practice, little is known about how university students integrate them into their writing processes. We examine how students engage with AI across different writing tasks, and how this engagement is shaped by individual factors including AI literacy, writing confidence, trust, authorship concerns, and motivation. Study~1 surveys 107 UK university students to map task-specific and co-occurring patterns of AI use across five writing stages (ideation, sourcing, planning, drafting, and reviewing) and their associations with individual factors. Study~2 complements this by exploring how these patterns can be assembled in practice, through interviews with 12 postgraduates reflecting on their established use of AI in assessed writing. Together, the studies suggest that AI integration is selective and heterogeneous, forming three recurring and value-oriented configurations: (1) early-stage (learning-oriented), where tools support exploration and understanding; (2) late-stage (quality-oriented), where tools support drafting and refinement; and (3) peripheral (productivity-oriented), where tools are used to reduce friction and sustain momentum across the process. We offer a workflow-level account of AI-supported academic writing, showing how students navigate competing priorities of learning, quality, productivity, and authorship, and how they evaluate and take responsibility for AI-generated outputs.