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Effectiveness of Generative AI in Automated Written Corrective Feedback with Prompting

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Title: Effectiveness of Generative AI in Automated Written Corrective Feedback with Prompting
Language: English
Authors: Jiahui Wu (ORCID 0009-0006-5465-0019), Jianwei Li (ORCID 0000-0002-5704-6216), Zigang Ge (ORCID 0000-0002-1733-2429), Mingrui Xu, Li Lin, Ru Zhang
Source: Journal of Educational Computing Research. 2025 63(6):1493-1527.
Availability: SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://sagepub.com
Peer Reviewed: Y
Page Count: 35
Publication Date: 2025
Document Type: Journal Articles
Reports - Research
Education Level: Elementary Education
Secondary Education
Descriptors: Error Correction, Feedback (Response), Artificial Intelligence, Computer Software, Technology Integration, Accuracy, Prompting, Writing Evaluation, Teacher Attitudes, Student Attitudes, Instructional Effectiveness, Self Efficacy, Faculty Workload, Educational Benefits, Writing Instruction, Elementary School Students, Secondary School Students, English (Second Language), Second Language Instruction, Second Language Learning, Foreign Countries
Geographic Terms: China
DOI: 10.1177/07356331251359430
ISSN: 0735-6331
1541-4140
Abstract: Automated written corrective feedback (AWCF) tools play a crucial role in supporting English writing instruction. However, issues such as insufficient accuracy and hallucination have undermined users' trust in these systems. To address these challenges, this study investigates the potential of Generative Artificial Intelligence (GAI) enhanced by prompting. Specifically, we evaluate the performance of several GAI models, including GPT-4, in providing written corrective feedback compared to commercial AWCF tools and other advanced models. The study adopts a dual-method approach: (1) a comprehensive model evaluation using established English writing datasets to assess error correction performance via various prompting strategies, and (2) an empirical study involving teachers and students to examine the system's practical efficacy and users' perspective. Quantitative results indicate that GPT-4 with chain-of-thought prompting significantly outperforms commercial tools, achieving improved consistency and accuracy in error detection and correction. Qualitative feedback from participants further supports the system's potential to enhance students' writing quality and confidence, while concurrently reducing teachers' workload and optimizing instructional efficiency, despite concerns regarding occasional overcorrection. These findings emphasize the benefits of task-specific prompting in GAI-based AWCF systems and provide actionable insights for integrating advanced AI feedback into educational practices.
Abstractor: As Provided
Entry Date: 2025
Accession Number: EJ1480245
Database: ERIC
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  Data: Effectiveness of Generative AI in Automated Written Corrective Feedback with Prompting
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  Data: <searchLink fieldCode="AR" term="%22Jiahui+Wu%22">Jiahui Wu</searchLink> (ORCID <externalLink term="https://orcid.org/0009-0006-5465-0019">0009-0006-5465-0019</externalLink>)<br /><searchLink fieldCode="AR" term="%22Jianwei+Li%22">Jianwei Li</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-5704-6216">0000-0002-5704-6216</externalLink>)<br /><searchLink fieldCode="AR" term="%22Zigang+Ge%22">Zigang Ge</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-1733-2429">0000-0002-1733-2429</externalLink>)<br /><searchLink fieldCode="AR" term="%22Mingrui+Xu%22">Mingrui Xu</searchLink><br /><searchLink fieldCode="AR" term="%22Li+Lin%22">Li Lin</searchLink><br /><searchLink fieldCode="AR" term="%22Ru+Zhang%22">Ru Zhang</searchLink>
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  Data: <searchLink fieldCode="SO" term="%22Journal+of+Educational+Computing+Research%22"><i>Journal of Educational Computing Research</i></searchLink>. 2025 63(6):1493-1527.
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  Data: SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://sagepub.com
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  Data: 35
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  Data: <searchLink fieldCode="DE" term="%22Error+Correction%22">Error Correction</searchLink><br /><searchLink fieldCode="DE" term="%22Feedback+%28Response%29%22">Feedback (Response)</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+Intelligence%22">Artificial Intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+Software%22">Computer Software</searchLink><br /><searchLink fieldCode="DE" term="%22Technology+Integration%22">Technology Integration</searchLink><br /><searchLink fieldCode="DE" term="%22Accuracy%22">Accuracy</searchLink><br /><searchLink fieldCode="DE" term="%22Prompting%22">Prompting</searchLink><br /><searchLink fieldCode="DE" term="%22Writing+Evaluation%22">Writing Evaluation</searchLink><br /><searchLink fieldCode="DE" term="%22Teacher+Attitudes%22">Teacher Attitudes</searchLink><br /><searchLink fieldCode="DE" term="%22Student+Attitudes%22">Student Attitudes</searchLink><br /><searchLink fieldCode="DE" term="%22Instructional+Effectiveness%22">Instructional Effectiveness</searchLink><br /><searchLink fieldCode="DE" term="%22Self+Efficacy%22">Self Efficacy</searchLink><br /><searchLink fieldCode="DE" term="%22Faculty+Workload%22">Faculty Workload</searchLink><br /><searchLink fieldCode="DE" term="%22Educational+Benefits%22">Educational Benefits</searchLink><br /><searchLink fieldCode="DE" term="%22Writing+Instruction%22">Writing Instruction</searchLink><br /><searchLink fieldCode="DE" term="%22Elementary+School+Students%22">Elementary School Students</searchLink><br /><searchLink fieldCode="DE" term="%22Secondary+School+Students%22">Secondary School Students</searchLink><br /><searchLink fieldCode="DE" term="%22English+%28Second+Language%29%22">English (Second Language)</searchLink><br /><searchLink fieldCode="DE" term="%22Second+Language+Instruction%22">Second Language Instruction</searchLink><br /><searchLink fieldCode="DE" term="%22Second+Language+Learning%22">Second Language Learning</searchLink><br /><searchLink fieldCode="DE" term="%22Foreign+Countries%22">Foreign Countries</searchLink>
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  Data: <searchLink fieldCode="DE" term="%22China%22">China</searchLink>
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  Data: 10.1177/07356331251359430
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  Data: 0735-6331<br />1541-4140
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Automated written corrective feedback (AWCF) tools play a crucial role in supporting English writing instruction. However, issues such as insufficient accuracy and hallucination have undermined users' trust in these systems. To address these challenges, this study investigates the potential of Generative Artificial Intelligence (GAI) enhanced by prompting. Specifically, we evaluate the performance of several GAI models, including GPT-4, in providing written corrective feedback compared to commercial AWCF tools and other advanced models. The study adopts a dual-method approach: (1) a comprehensive model evaluation using established English writing datasets to assess error correction performance via various prompting strategies, and (2) an empirical study involving teachers and students to examine the system's practical efficacy and users' perspective. Quantitative results indicate that GPT-4 with chain-of-thought prompting significantly outperforms commercial tools, achieving improved consistency and accuracy in error detection and correction. Qualitative feedback from participants further supports the system's potential to enhance students' writing quality and confidence, while concurrently reducing teachers' workload and optimizing instructional efficiency, despite concerns regarding occasional overcorrection. These findings emphasize the benefits of task-specific prompting in GAI-based AWCF systems and provide actionable insights for integrating advanced AI feedback into educational practices.
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        Value: 10.1177/07356331251359430
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      – Text: English
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        PageCount: 35
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      – SubjectFull: Error Correction
        Type: general
      – SubjectFull: Feedback (Response)
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      – SubjectFull: Artificial Intelligence
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      – SubjectFull: Accuracy
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      – SubjectFull: Prompting
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      – SubjectFull: Writing Evaluation
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      – SubjectFull: Teacher Attitudes
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      – SubjectFull: China
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      – TitleFull: Effectiveness of Generative AI in Automated Written Corrective Feedback with Prompting
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