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Artificial Intelligence Knowledge Development

<p style="text-align: justify;">This study examines the accuracy of fall, winter, and spring benchmark writing assessments, scored by the MI Write automated writing evaluation system, for predicting non-proficiency on the Smarter Balanced ELA assessment. This study considers how grade level, seasonality, and language status influence classification accuracy using Receiver Operating Characteristic (ROC) curve analyses. The results indicate that MI Write demonstrated acceptable overall classification accuracy, with the strongest performance among non-ELLs and Grade 7 students. However, accuracy was more inconsistent for ELLs. Across all grades and subgroups, the d-based cutpoints consistently provided the best balance between sensitivity and specificity. Implications for adopting AI-based assessment systems within the middle grades are discussed.</p>

ltrq.2025.51.04.pdf

Original Research 

ltrq.2025.51.04.xml

Assessing the Accuracy of Automated Writing Evaluation in Predicting English Language Arts Proficiency for Middle-Grade English Language Learners and Non-English Language Learners

Guest-editors: 
Christine Coombe 
Higher Colleges of Technology, Dubai, UAE 
Tony Clark 
Cambridge University Press & Assessment, UK 
Hassan Mohebbi 
European Knowledge Development Institute, Turkey


Special Issue: In Honour of Carol A. Chapelle’s Contributions to Language Assessment and Learning (Volume 51-Second Part)