TL;DR
It is suggested that evaluating LLM-based readability adaptation should combine objective measures with learner-centered feedback, rather than relying solely on conventional text difficulty.
Although LLM-based benchmark ratings can approximate expert judgments and reduce the need for labor-intensive human triple coding, limitations remain regarding cost, genre- and task specificity, and sensitivity to text presentation and student grade level—factors that constrain immediate classroom use, particularly for...
While various organizations now actively encourage LLM use in classrooms, we still lack rigorous, systematic evaluations of how well these models actually perform the fundamental tasks of language pedagogy. This paper examines whether state-of-the-art LLMs can deliver the kind of corrective feedback and methodological...
A systematic benchmark of diverse open-source LLMs for multilingual readability assessment is conducted, focusing on the prediction of discrete readability levels required by educational frameworks, to establish the viability of out-of-the-box LLMs as robust, cross-lingual readability assessors.
Large classes in Chinese College English programmes make frequent analytic assessment of student writing difficult. Large language models (LLMs) may support more frequent formative assessment, but their scores may vary across queries and be systematically harsher or more lenient than local teacher ratings. Using a corp...
This study examines the challenges faced by lecturers and item writers in selecting and creating texts for assessing reading comprehension in teaching Turkish as a foreign/second language. Conducted within the national e-YDTS four-skill Turkish proficiency test development project coordinated by the Measurement, Select...
The results show that high self-consistency does not necessarily indicate high agreement with human judgments when using local LLMs as automatic judges, and highlight the need to evaluate both consistency and human alignment when using local LLMs as automatic judges.
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