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Continue reading →: The Vanishing First Draft: What Happens to Language When We Stop Writing Badly?
Generative AI is changing writing in ways that extend far beyond speed, accuracy or convenience. One of its most consequential effects may be surprisingly mundane: the gradual disappearance of the bad first draft. Writing has traditionally begun in linguistic disorder. Writers search for words, produce awkward sentences, repeat ideas, delete…
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Continue reading →: When Writing Becomes Evaluation: Generative AI and the Changing Nature of Composition
Generative artificial intelligence has transformed writing in ways that extend far beyond efficiency or textual quality. Within seconds, large language models (LLMs) can suggest vocabulary, improve grammatical accuracy, reorganise paragraphs, strengthen arguments and refine stylistic choices. While much of the current discussion has focused on AI-generated text, academic integrity and…
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Continue reading →: Linguistic Persistence: Rethinking Language Learning in the Age of Generative AI
One of the defining promises of generative AI is the removal of linguistic friction. Within seconds, learners can obtain a grammatically accurate sentence, a more precise lexical choice, a coherent paragraph, or an entire essay. From an instructional perspective, this represents a remarkable advance in access to linguistic support. From…
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Continue reading →: The Algorithmic Learner: When Generative AI Becomes an Invisible Language Policy
Introduction: Who Is Adapting to Whom? Languages have never evolved independently of the environments in which they are used. Across history, educational institutions, publishing practices, technological infrastructures, and social communities have shaped which linguistic forms become standard, which rhetorical conventions acquire prestige, and which communicative practices gradually recede. Standardisation has…
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Continue reading →: From Revision to Selection: Rethinking the Developmental Locus of Writing in the Age of Generative AI
Introduction: A Foundational Assumption Under Pressure Few assumptions have been more deeply embedded within writing pedagogy than the belief that writing develops through revision. Across cognitive, sociocultural, and process-oriented traditions of writing research, drafting has long been understood not merely as a mechanism for producing text but as a mechanism…
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Continue reading →: When Error Becomes Invisible: Generative AI and the Future of Developmental Evidence in Applied Linguistics
The Emerging Invisibility of Language Development Few concepts have occupied a more central position in language education than error. For more than half a century, errors have functioned as the primary empirical window through which language development becomes visible. Teachers diagnose errors to identify emerging difficulties, learners engage with errors…
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Continue reading →: Beyond Fluency: How AI Is Reconfiguring the Epistemic Foundations of Writing in Education
The integration of Large Language Models (LLMs) into education is often discussed through the familiar language of disruption, efficiency, productivity, academic integrity, and institutional adaptation. Yet this vocabulary remains inadequate because it treats generative AI primarily as an operational technology rather than as an epistemic force. The deeper issue is…
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Continue reading →: Using Learner Corpora to Audit AI Feedback
The accelerating integration of Large Language Models (LLMs) into writing instruction has produced a methodological paradox that educational linguistics has not yet adequately resolved. While generative systems are increasingly capable of producing fluent, immediate, and rhetorically polished feedback, the empirical foundations upon which such feedback operates remain largely opaque. Most…
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Continue reading →: From Fluency to Fidelity: Reassessing Validity in AI-Mediated Writing Feedback
By Joanne Nifli-Sakali The accelerated incorporation of large language models (LLMs) into writing pedagogy has unsettled long-standing assumptions about what constitutes proficiency, how feedback functions, and on what grounds assessment claims can be considered valid. While the apparent fluency, coherence, and immediacy of AI-generated feedback have been widely celebrated, such…
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Continue reading →: Using Learner Corpora to Audit AI Feedback
Toward an Empirical Criterion for Pedagogical Alignment in AI-Mediated Writing 1.Replacing Plausibility with Evidence The rapid normalisation of generative feedback in writing pedagogy has produced a quiet but consequential shift in evaluative standards. Correctness is increasingly inferred from fluency; pedagogical value is tacitly equated with stylistic polish. Yet neither fluency…
The Meta Linguist
The Meta Linguist is a professional space for English language educators, teacher trainers, and researchers engaging with language teaching at its deepest level. The blog explores the intersections of applied linguistics, corpus-informed pedagogy, and emerging technologies shaping contemporary ELT. With a particular focus on teacher education, writing development, and GenAI-mediated feedback practices, it foregrounds principled, research-informed approaches beyond prescriptive methodology. Designed for CELTA tutors, MA students, and doctoral scholars, The Meta Linguist invites critical reflection on how language, learning, and pedagogy are being reconfigured in an evolving educational landscape.
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