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Cited by 5Artificial Intelligence in EducationEFL/ESL Teaching and LearningStudent Assessment and Feedback

Exploring the Effectiveness of Cooperative Pre-Service Teacher and Generative AI Writing Feedback on Chinese Writing

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Yang, Hongli, Zhang, Yu, Guo, Jixuan·2025·Behavioral Sciences
DOI 10.3390/bs15040518PMC PMC12024180
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Abstract

Due to their efficiency, stability, and enhanced language comprehension and analysis capabilities, generative AIs have attracted increasing attention in the field of writing as higher-level automated writing evaluation (AWE) feedback tools. However, few studies have examined the impact of pre-service teachers using generative AI in combination with their own teaching experience to provide feedback on Chinese writing. To fill this gap, based on 1035 writing feedback texts, we examined the differences in writing feedback between 11 pre-service teachers and Erine Bot (a generative AI) and interviewed the pre-service teachers about their willingness to cooperate with generative AI. The collaborative writing feedback generated by the pre-service teachers using AI was compared with the feedback generated by the pre-service teachers and generative AI separately. We identified that, although Ernie Bot provided significantly better feedback than the pre-service teachers in three specific areas (except for language expression), and both Ernie Bot and the pre-service teachers had respective advantages in terms of writing strategy, human–computer cooperative writing feedback was significantly better than the writing feedback provided by either Ernie Bot or the pre-service teachers alone. The was true across all aspects of the feedback in terms of focus and strategy. These findings can support the training of pre-service teachers and improve the writing quality of their students via implementing AI to provide more effective writing feedback.

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  • Learners perceive a conflict between teacher guidance and GenAI support, diminishing their expectations for teacher-student interactions, which leads to TSI negatively impacting learning satisfaction.

    “Consequently, learners perceive a conflict between teacher guidance and GenAI support, diminishing their expectations for teacher-student interactions. This ultimately leads to TSI negatively impacting learning satisfaction (Yang Y. et al., 2025).”

    From Exploring the factors influencing college students’ learning satisfaction in generative AI-supported MOOCs learning environment: a learning experience framework perspective (2025) · cites this paper as Yang Y. et al., 2025

    The Introduction discusses controversy over replacing teacher feedback with AI, and Discussion §6.2 recommends teacher-led cooperation with AI as an auxiliary tool. Neither section reports that learners perceive a conflict, expect less teacher–student interaction, or experience lower learning satisfaction as a result. Searches for those terms found no passage addressing the claimed relationship. (LLM, conf=0.76)

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Cited by

Sorted by citations

Authors
Yang, H., Zhang, Y., & Guo, J.
Affiliations
  • Northeast Normal University (CN)
  • Sun Yat-sen University (CN)
Corresponding author
Yang, Hongli
ORCID
Yang, Hongli
Venue
Behavioral Sciences
Publisher
MDPI AG
Published
2025-04-13
Volume
15
Issue
4
Pages
518
ISSN
2076-328X (ISSN-L)
DOI
10.3390/bs15040518
PMC
PMC12024180
OpenAlex
W4409434355
Cited by
5
References
13
Citations by year
2026: 2 · 2025: 3
FWCI
5.20
Type
journal article
Language
en
Open access
Licence
Locations
  • Behavioral Sciences (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
National Planning Office of Education Sciences, PRC (BHA200135)
Topics
  • Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
  • Social Sciences › Arts and Humanities › Language and Linguistics › EFL/ESL Teaching and Learning
  • Social Sciences › Education › Student Assessment and Feedback
Keywords
generative AI, writing feedback, pre-service teachers, Chinese writing, automated writing evaluation, human-AI collaboration, ERNIE Bot, teacher professional development, writing quality, writing strategies

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