Large Language Model-Driven Dynamic Assessment of Grammatical Accuracy in English Language Learner Writing

• URL: http://arxiv.org/abs/2505.00931v1
• Date: Fri, 02 May 2025 00:19:50 GMT
• Title: Large Language Model-Driven Dynamic Assessment of Grammatical Accuracy in English Language Learner Writing
• Authors: Timur Jaganov, John Blake, Julián Villegas, Nicholas Carr,
• Abstract summary: This study investigates the potential for Large Language Models (LLMs) to scale up Dynamic Assessment (DA)<n>To facilitate such an investigation, we first developed DynaWrite-a modular, grammatical-based tutoring application.<n> GPT-4o and neural chat have the most potential to scale-up DA in the language learning classroom.
• Score: 0.4374837991804086
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: This study investigates the potential for Large Language Models (LLMs) to scale-up Dynamic Assessment (DA). To facilitate such an investigation, we first developed DynaWrite-a modular, microservices-based grammatical tutoring application which supports multiple LLMs to generate dynamic feedback to learners of English. Initial testing of 21 LLMs, revealed GPT-4o and neural chat to have the most potential to scale-up DA in the language learning classroom. Further testing of these two candidates found both models performed similarly in their ability to accurately identify grammatical errors in user sentences. However, GPT-4o consistently outperformed neural chat in the quality of its DA by generating clear, consistent, and progressively explicit hints. Real-time responsiveness and system stability were also confirmed through detailed performance testing, with GPT-4o exhibiting sufficient speed and stability. This study shows that LLMs can be used to scale-up dynamic assessment and thus enable dynamic assessment to be delivered to larger groups than possible in traditional teacher-learner settings.

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