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<p>This study examined the usefulness and impact of AI-enabled automated essay scoring (AES) in providing formative feedback on student writing drafts. Despite the well-documented importance of writing instruction and feedback, educators face significant challenges in delivering timely, actionable feedback to students due to time constraints, large class sizes, and workload pressures. These barriers often limit students’ opportunities to practice writing and receive meaningful feedback on their writing. To address this problem, this research investigated WriteGrader, an AI-powered AES tool designed to score student essays and provide accurate and timely feedback. Results demonstrated strong agreement between WriteGrader scoring and expert human ratings, indicating reliable scoring accuracy. Teachers evaluated the AI-generated feedback as highly accurate and actionable, particularly for criteria related to organization, mechanics, and evidence use. Most significantly, students who received formative AI feedback on drafts showed measurable improvements in their final submissions compared to initial drafts. These findings suggest that AI-enabled AES tools can serve as effective teaching assistants, providing students with immediate, consistent feedback while alleviating teacher workload. The study contributes to understanding how generative AI can be integrated into writing instruction to support student learning and teacher effectiveness.</p>

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Using generative AI for formative feedback on student writing drafts

  • 期刊论文

  • 发表日期：

    2026年6月20日

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  • Randall Davies

摘要

This study examined the usefulness and impact of AI-enabled automated essay scoring (AES) in providing formative feedback on student writing drafts. Despite the well-documented importance of writing instruction and feedback, educators face significant challenges in delivering timely, actionable feedback to students due to time constraints, large class sizes, and workload pressures. These barriers often limit students’ opportunities to practice writing and receive meaningful feedback on their writing. To address this problem, this research investigated WriteGrader, an AI-powered AES tool designed to score student essays and provide accurate and timely feedback. Results demonstrated strong agreement between WriteGrader scoring and expert human ratings, indicating reliable scoring accuracy. Teachers evaluated the AI-generated feedback as highly accurate and actionable, particularly for criteria related to organization, mechanics, and evidence use. Most significantly, students who received formative AI feedback on drafts showed measurable improvements in their final submissions compared to initial drafts. These findings suggest that AI-enabled AES tools can serve as effective teaching assistants, providing students with immediate, consistent feedback while alleviating teacher workload. The study contributes to understanding how generative AI can be integrated into writing instruction to support student learning and teacher effectiveness.