It is argued that generative AI is reigniting long-standing debates in World Englishes about standardization, legitimacy, and the ownership of English, now playing out in algorithmic systems, model training, evaluation practices, and public discourse, where non-dominant Englishes are increasingly conflated with AI-generated speech.
Abstract
The rapid growth of large language models (LLMs) has resurrected age-old questions in sociolinguistics and world Englishes, such as who decides what counts as legitimate English, whose English is suspect etc. This paper examines how AI systems, their uses and discourse on them reflect, reinforce, and occasionally challenge (standard) language ideologies, which privilege Inner Circle norms and marginalize non-dominant Englishes. Drawing on evidence from empirical studies, media commentary, social media debates, and examples from AI outputs, the paper shows that AI technologies reproduce dominant language ideologies at different levels: training data, design protocols, evaluation benchmarks, user feedback and public commentary. The analysis uses the public controversy over AI-sounding language, especially the fixation on the word delve, to illustrate how speakers of English from the Global North police the English language norms of Global South English users. The paper also identifies what Christian Mair has called a"standardisation paradox": AI may homogenize English by privileging standard forms and at the same time pluralize Englishes through exposure to wide-ranging corpora and annotation work carried out by Global South users. In doing so, the paper argues that generative AI is reigniting long-standing debates in World Englishes about standardization, legitimacy, and the ownership of English, now playing out in algorithmic systems, model training, evaluation practices, and public discourse, where non-dominant Englishes are increasingly conflated with AI-generated speech. Discussing AI systems as a site where language ideologies are (re)produced, the paper argues for more inclusive design approaches that recognize the plurality of Englishes in order to address the real-world negative consequences of treating some as more legitimate than others.
Through a new formalist reading of English- and Spanish-language short stories generated by ChatGPT-5, it is demonstrated that distributional regularities in language modelling scale upward, producing gendered and culturally normative stylistic patterns.
While large language model outputs are frequently analysed as a collective super variety termed"AI language,"this chapter argues that this perspective coexists with distinct, model-specific linguistic signatures akin to human idiolects. We analyse two datasets of LLM-generated texts on societal topics: a 2024 corpus of six models (Improta et al. 2024) and a newly generated 2026 corpus using the same prompts featuring six contemporary models. Our findings, utilising computational descriptors and stylometric principal component analysis reveal a generational shift between the style of the 2024 and 2026 cohorts, while demonstrating that each individual model maintains a unique linguistic profile. This multi-layered interplay is illustrated by contraction frequencies, which vary from over 1,200 to over 30,000 per million words within the same cohort of models (2026). Ultimately, we conclude that treating LLM output as idiolectal in nature provides a valuable framework with potential implications for research on variation and change, LLM-generated text detection, forensic linguistics and usage-based approaches to language.
Karolina Rudnicka, Thomas Stephan Juzek· 0 citations
The findings show that AI-generated texts exhibit greater lexical diversity and syntactic complexity; however, they often exhibit structural uniformity, overuse of cohesive devices, and limited pragmatic depth, and should not replace professionally designed educational materials.
V. Smaglii, T. Korolova, Svitlana Yukhymets et al.· Arab World English Journal· 0 citations
Large language models are already widely used in our society. Hitherto, most critical studies have relied on quantitative methods or studied the mode of production of AI, which can yield important insights. However, cross-model and cross-linguistic critical discourse analyses of LLM outputs remain relatively limited, even though they can offer important insights into LLM outputs by focusing on the production of meaning through an overall assessment. Our interdisciplinary paper offers a critical, qualitative approach, discussing the outputs of six LLMs regarding their definitions of diversity. For our analysis, we draw on theories of discourse to offer new possibilities from the critical social sciences for the analysis and critique of LLM outputs. Although we initially focused on their differences, the outputs were dominated by the pervading neoliberal appropriation of diversity, represented by a focus on its economic utility and the subsequent individualisation of differences. Overall, it is not surprising that LLMs reproduce the discourse and inherent power structures in their outputs, since they are embedded in their context and societies. Therefore, our paper demonstrates that LLMs need the critical company of qualitative social sciences to point to the reproduction of power structures.
Valerian Thielicke-Witt, Ana-Nzinga Weiß, Hannah Miltzow· AI & SOCIETY· 0 citations
The rapid integration of artificial intelligence (AI) into educational contexts has prompted urgent reconsideration of established pedagogical frameworks, particularly within the domain of English Language Teaching (ELT). While AI-powered tools are increasingly present in language classrooms, the theoretical foundations needed to guide their thoughtful and equitable integration remain underexplored. This paper examines the reconceptualisation of ELT in the age of AI through a multi -theoretical lens, drawing on constructivism, Vygotsky's sociocultural theory, connectivism, the Technology Acceptance Model (TAM), and Communicative Language Teaching (CLT). The central argument advanced is that no single theory adequately accounts for the complexity of AI -mediated language learning; instead, an integrated framework is needed, the one that positions the teacher as a critical mediator, foregrounds social interaction as the locus of language developmen t, and embraces networked knowledge as a legitimate epistemic resource. The paper proposes a conceptual model; the AI-Mediated Language Learning Model (AMLL) to map the dynamic relationships among AI tools, teacher agency, student interaction, and language development. Implications are drawn for classroom practice, institutional policy , and future research directions. It is argued that the future of ELT must be neither technophilic nor technophobic, but critically reflexive.
Y. Y. Adam· Australian Journal of Busine...· 0 citations
