ORIGINAL RESEARCH article
Front. Educ.
Sec. Teacher Education
This article is part of the Research TopicGenerative Artificial Intelligence and Writing Instruction in K–12 and college educationView all 4 articles
AI-Assisted Evaluation of Revision Patterns in Young Students' Argument Writing
Provisionally accepted
- 1University of Pittsburgh, Pittsburgh, United States
- 2RAND Corporation, Santa Monica, United States
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Revision is a crucial component of the writing process, yet few formative assessments focus on students' revision processes. This study proposed an AI-assisted formative assessment that identifies revision patterns across drafts (i.e., first and second drafts) of students' text-based argument writing. In particular, we examined the performance of GPT-4.1 in predicting revision patterns using two prompting strategies: few-shot prompting and few-shot Chain-of-Thought (CoT) prompting. The results show that GPT-4.1 exhibits strong potential for evaluating the revision process for formative purposes. It demonstrates excellent intra-rater reliability in predicting revision patterns across multiple runs. We also find that using CoT prompting that incorporates intermediate evaluation steps improves the accuracy of predicting explanation-focused revision patterns, a task that requires a more cognitively demanding evaluative process than assessing evidence-focused revisions. Implications for the conditions under which CoT prompting yields added value for enhancing prediction accuracy in writing evaluation are discussed.
Keywords: adaptive expertise, Automated writing evaluation, Chain-of- thought prompting, formative assessment of writing, revision process
Received: 31 Oct 2025; Accepted: 22 Jan 2026.
Copyright: © 2026 Li, Matsumura, Wang, Litman, Liu and Correnti. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Tianwen Li
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