Machine Learning-Based Feedback in Essay Writing: Improving Accuracy and Student Engagement
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Automated Feedback, Essay Writing, Student Engagement
Abstract
Background. Advances in artificial intelligence have significantly reshaped language education, particularly in writing instruction where feedback is essential for learning development. Although teacher feedback remains pedagogically valuable, it is often limited by time, consistency, and scalability.
Purpose. This study aimed to examine the impact of ML-based automated feedback on writing accuracy and learner engagement among university-level English as a Foreign Language (EFL) students. It also explored students’ perceptions of automated feedback compared to traditional instructor feedback.
Method. A quasi-experimental research design was employed involving 120 undergraduate EFL students divided into an experimental group and a control group. The experimental group used an AI-assisted writing platform that provided automated feedback on grammar, cohesion, and lexical variety, while the control group received conventional teacher feedback only.
Results. The results showed a significant improvement in writing accuracy and syntactic complexity in the experimental group compared to the control group (p < .05). Qualitative findings indicated that students perceived ML-based feedback as timely, motivating, and helpful in promoting self-correction, reflection, and independent learning.
Conclusion. The study concludes that integrating ML-based automated feedback into EFL writing instruction enhances both linguistic performance and student engagement.
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Authors
Copyright (c) 2025 María García, Moussa Bamba, Ousseini Zongo
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