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Ikkyu Choi; Jiyun Zu – Language Testing, 2025
Today's language models can produce syntactically accurate and semantically coherent texts. This capability presents new opportunities for generating content for language assessments, which have traditionally required intensive expert resources. However, these models are also known to generate biased texts, leading to representational harms.…
Descriptors: Artificial Intelligence, Language Tests, Test Bias, Test Construction
Farshad Effatpanah; Purya Baghaei; Mona Tabatabaee-Yazdi; Esmat Babaii – Language Testing, 2025
This study aimed to propose a new method for scoring C-Tests as measures of general language proficiency. In this approach, the unit of analysis is sentences rather than gaps or passages. That is, the gaps correctly reformulated in each sentence were aggregated as sentence score, and then each sentence was entered into the analysis as a polytomous…
Descriptors: Item Response Theory, Language Tests, Test Items, Test Construction

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