Editorial

Abstract

The rapid proliferation of large language models (LLMs), particularly generative AI tools such as ChatGPT, has challenged the validity of writing-based assessment in educational contexts. When AI-generated text is submitted as authentic student work, assessment instruments lose their capacity to measure the constructs they were designed to evaluate, rendering grades epistemically unreliable and educationally meaningless. Current institutional responses have focused predominantly on AI detection technologies. However, evidence indicates that such tools demonstrate significant methodological limitations, including elevated false-positive rates among non-native English writers whose linguistic patterns are characterised by restricted syntactic variation and high-frequency lexical choices that are systematically misclassified as machine-generated. This raises substantive concerns regarding the fairness of assessments, construct validity, and the equitable treatment of linguistically diverse student populations. In second- and foreign-language education specifically, AI-mediated writing production displaces the productive cognitive struggle that research consistently identifies as essential to language acquisition. When learners bypass the writing process entirely, the formative dimension of assessment is eliminated, reducing the written task to an inauthentic performance with no measurable learning outcome. This editorial argues that sustainable institutional responses must move decisively beyond detection toward the redesign of principled assessment. Effective alternatives include process-oriented portfolios, oral defense components, and tasks requiring critical engagement with AI-generated output. The integrity of educational measurement depends not on technological surveillance, but on reconceptualising assessment as a transparent, process-visible, and pedagogically accountable practice. In this context, it remains obligatory upon the researcher to assert their cognitive presence through a distinct personal imprint, subordinating AI tools to their scholarly agency rather than the reverse. Generative artificial intelligence, regardless of its purported efficacy, constitutes nothing more than an assisting resource that neither substitutes for the authorial voice nor absolves the researcher from mastering methodological rigor or from the repercussions of intellectual plagiarism or uncritical imitation.

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