Impact of automated writing evaluation on teacher feedback, student revision, and writing improvement
Abstract
Recent years have witnessed an increasing interest in the use of automated writing evaluation (AWE) in second language writing classrooms. This increase is partially due to the belief that AWE can assist teachers by allowing them to devote more feedback to higher-level (HL) writing skills, such as content and organization, while the technology addresses lower-level (LL) skills, such as grammar. As is speculated, student revisions will then be positively impacted. However, little evidence has supported these claims, calling into question the impact of AWE on teaching and learning. The current study explored these claims by comparing two second language writing classes that were assigned to either an AWE + teacher feedback condition or a teacher-only-feedback condition. Findings suggest that using AWE as a complement to teacher feedback did not have a significant impact on the amount of HL teacher feedback, but the teacher who did not use AWE tended to provide a greater amount of LL feedback than AWE alone. Furthermore, students seemed to revise the teacher’s LL feedback more frequently than LL feedback from the computer. Interestingly, students retained their improvement in accuracy in the long-term when they had access to AWE, but students who did not have access appeared to have lower retention. We explain the relevance of our findings in relation to an argument-based validation framework to align our work with state-of-the-art research in the field and contribute to a broader discussion about how AWE can be best provided to support second language writing development.
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AI-powered writing evaluation tools effectively enhanced learners' writing quality.
“Link et al. (2022), Huang and Wilson (2021), and Nunes et al. (2022)consistently emphasized the role of AI-powered writing evaluation tools, identifying that these automated systems effectively enhanced learners' writing quality.”
From The Role of AI-Assisted Learning in Academic Writing: A Mixed-Methods Study on Chinese as a Second Language Students (2025) · cites this paper as Link et al., 2022
AI-powered tools are particularly effective in identifying and addressing surface-level issues, such as grammar, punctuation, and spelling, while enabling teachers to focus on higher-order writing skills like idea development and coherence.
“These tools are particularly effective in identifying and addressing surface-level issues, such as grammar, punctuation, and spelling, while enabling teachers to focus on higher-order writing skills like idea development and coherence (Link et al., 2022).”
From Enhancing EFL Writing Revision Practices: The Impact of AI- and Teacher-Generated Feedback and Their Sequences (2025) · cites this paper as Link et al., 2022
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- Authors
- Link, S., Mehrzad, M., & Rahimi, M.
- Affiliations
- Oklahoma State University (US)
- Shiraz University (IR)
- Corresponding author
- Rahimi, Mohammad
- Venue
- Computer Assisted Language Learning
- Publisher
- Informa UK Limited
- Published
- 2020-03-26
- Volume
- 35
- Issue
- 4
- Pages
- 605-634
- ISSN
- 0958-8221 (ISSN-L)
- 1744-3210
- OpenAlex
- W3013848609
- Cited by
- 275
- References
- 66
- Citations by year
- 2026: 52 · 2025: 65 · 2024: 60 · 2023: 51 · 2022: 31 · 2021: 12 · 2020: 4
- FWCI
- 114.15
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Computer Assisted Language Learning (publishedVersion)
- Topics
- Social Sciences › Arts and Humanities › Language and Linguistics › EFL/ESL Teaching and Learning
- Social Sciences › Education › Student Assessment and Feedback
- Physical Sciences › Computer Science › Information Systems › English Language Learning and Teaching
- Keywords
- automated writing evaluation, teacher feedback, student revision, second language writing, writing accuracy, grammar feedback, writing development
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