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Grammatical accuracy remains a significant challenge for EFL learners in Myanmar, where limited access to individualized feedback, resource constraints, and L1 interference from Burmese hinder writing development. Automated Writing Evaluation (AWE) tools such as Grammarly offer a potential solution by providing immedia...
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Linguistic Variety as a Framework for Evaluating LLM Behavior: A Comparative Study of Irish English and General American in Large Language Model Responses
While large language models (LLMs) are increasingly used across social domains, current bias evaluations often rely on demographic proxies such as names, pronouns, and social categories. Linguistic variety itself receives less attention as an evaluative variable. This study therefore uses sociolinguistic variety to exa...
When Correct Isn’t Fluent: Translationese in Turkish-to-English Academic Writing and Its Implications for EAP
Turkish scholars, like many non-native English speakers, routinely translate manuscripts written in their first language before submitting them to international journals, a practice that leaves linguistic traces even in grammatically sound texts. This study examines that residue, commonly termed translationese, in the...
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The availability of Large Language Models (LLMs) reshaped scientific discourse at a linguistic level. LLMs are assumed to homogenize academic writing, flattening it into a single generic lexical register. To understand how CHI writing has changed since the public release of LLMs, we analyzed full texts of 14,262 archiv...
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