L2 student engagement with automated feedback on writing: Potential for learning and issues of trust
Abstract
Abstract Recent case-study research on L2 student engagement with automated writing evaluation (AWE) feedback shows it is not simply a matter of understanding and implementing suggested changes but can instead be strongly influenced by individual and contextual factors. The present multiple-case study builds on this work by investigating the extent to which engagement supports learning from, and is influenced by trust in, AWE feedback. Six Mandarin L1 university students at different levels of English writing instruction participated, each providing a text they had written for a current course, which was submitted to the tool Grammarly for feedback. Screen-capture recordings, stimulated recalls, and interviews were used to collect information about how much of the feedback participants viewed, which of it they accepted, which they ignored or modified, and why they did so. Findings showed participants adopting a proofreading as opposed to a learning orientation toward the feedback, perhaps because most of it included specific corrections, thus requiring little work on the user’s part. Whereas previous studies attributed more successful engagement with AWE feedback to higher language proficiency, the present research showed different dimensions of trust in Grammarly’s feedback to be determining factors.
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Ranalli (2021) found high engagement in using Grammarly Premium among 6 high school students in China.
“Results showed high engagement, increased agency in tool use, and flexible integration of AI-generated text, contributing to both linguistic creativity and learner autonomy.”
From AI-Integrated Scaffolding to Enhance Agency and Creativity in K-12 English Language Learners: A Systematic Review (2025) · cites this paper as [34]
Ranalli (2021) found increased agency in tool use among 6 high school students in China.
“Results showed high engagement, increased agency in tool use, and flexible integration of AI-generated text, contributing to both linguistic creativity and learner autonomy.”
From AI-Integrated Scaffolding to Enhance Agency and Creativity in K-12 English Language Learners: A Systematic Review (2025) · cites this paper as [34]
Ranalli (2021) found flexible integration of AI-generated text contributing to linguistic creativity and learner autonomy.
“Results showed high engagement, increased agency in tool use, and flexible integration of AI-generated text, contributing to both linguistic creativity and learner autonomy.”
From AI-Integrated Scaffolding to Enhance Agency and Creativity in K-12 English Language Learners: A Systematic Review (2025) · cites this paper as [34]
Ranalli found that precise feedback on language use led to more sophisticated sentence structures without compromising student agency in the revision process.
“Ranalli [34] provided important insights into the role of automated grammar support, finding that precise feedback on language use led to more sophisticated sentence structures without compromising student agency in the revision process.”
From AI-Integrated Scaffolding to Enhance Agency and Creativity in K-12 English Language Learners: A Systematic Review (2025) · cites this paper as [34]
Ranalli's multi-level scaffolding approach helped students develop both procedural and metacognitive skills, allowing them to make more informed decisions about integrating AI feedback into their writing.
“This hands-on approach helped students develop both procedural and metacognitive skills, allowing them to make more informed decisions about integrating AI feedback into their writing.”
From AI-Integrated Scaffolding to Enhance Agency and Creativity in K-12 English Language Learners: A Systematic Review (2025) · cites this paper as [34]
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“Students have been shown to heavily rely on AI for superficial functions such as grammar checking, spelling correction, and paraphrasing, and not for more substantial and challenging learning exercises (Le, 2023; Ranalli, 2021).”
Cotos, E. (2023) · Springer eBooks · doi:10.1007/978-3-031-36033-6_22 · Cited by 15 · see citing works
How L2 student writers engage with automated feedback: A longitudinal perspective
Li, X., & Ping, K. (2025) · Assessing Writing · doi:10.1016/j.asw.2025.100919 · Cited by 15 · see citing works
Ebyary, K. E., Shabara, R., & Boraie, D. (2024) · Language Testing in Asia · doi:10.1186/s40468-024-00312-1 · Cited by 20 · see citing works
“Furthermore, the role of teacher feedback has been well documented (e.g., Sermsook et al., 2017), but little is known about the role of AI-operated feedback in noticing errors and uptake (Barrot, 2023a; Ranalli, 2021).”
Namaziandost, E. (2025) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2025.100446 · Cited by 15 · see citing works
Pearson, W. S. (2022) · SAGE Open · doi:10.1177/21582440221079842 · Cited by 20 · see citing works
“There has been increasing interest in L2 student engagement as the key to unlocking how and why learners do or do not gain utility from written feedback (Han & Hyland, 2015; Han & Xu, 2021; Ranalli, 2021; Uscinski, 2017; Yu & Jiang, 2020; Zhang, 2020).”
BackgroundVisibilizing invisible engagement behind students’ no-revision operation to written feedback
Fu, X., Yang, C., & Zhang, T. (2024) · Language Teaching Research · doi:10.1177/13621688241227666 · Cited by 14 · see citing works
“Many recent studies also notice students’ unaction to some AWE feedback, as in Lavolette et al. (2015), Zhang (2016), Bai and Hu (2017), Ranalli (2018, 2021), and Koltovskaia (2020); and to peer feedback as in Yu et al. (2018), Fan and Xu (2020), and Aben et al. (2022) as well, to name a few.”
Zhao, X., Sbaffi, L., & Cox, A. M. (2025) · Electronics · doi:10.3390/electronics14061194 · Cited by 18 · see citing works
Gao, B., Yan, J., & Zhong, R. (2025) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2025.3560032 · Cited by 11 · see citing works
“Thisimmediacysignificantlyenhancesinteractivity andreducesfeelingsofisolationinvirtuallearningenvironments [85].”
Abdullah, M. Y. (2025) · Education and Information Technologies · doi:10.1007/s10639-025-13601-w · Cited by 14 · see citing works
“Ranalli (2021) noted that students’ willingness to accept feedback from AI tools depends on their trust in the tool.”
Wang, Y., Wang, N., Liu, F., & Yu, X. (2024) · International Journal of Applied Linguistics · doi:10.1111/ijal.12638 · Cited by 10 · see citing works
Ginting, R. S. B., & Fithriani, R. (2022) · LLT Journal A Journal on Language and Language Teaching · doi:10.24071/llt.v25i2.4879 · Cited by 10 · see citing works
“First, the correlation between automated and human grading of student writing (Bridgeman et al., 2012); and second, the effects of automated analysis on student writing performance (Jiang et al., 2020; Ranalli, 2021).”
BackgroundMethodKashiha, H. (2025) · Journal of Second Language Writing · doi:10.1016/j.jslw.2025.101254 · Cited by 10 · see citing works
AI and VR Converge: The Future of Language Learning in an Emerging Metaverse
Godwin‐Jones, R. (2025) · Chinese language learning sciences · doi:10.1007/978-981-95-0245-5_11 · Cited by 10 · see citing works
Overstreet, M., Akhmedjanova, D. R., & Vaccino‐Salvadore, S. (2023) · Journal of Second Language Writing · doi:10.1016/j.jslw.2023.101019 · Cited by 9 · see citing works
EFL Learner Engagement in Automatic Written Evaluation
Fu, H., & Liu, X. (2022) · Frontiers in Psychology · doi:10.3389/fpsyg.2022.871707 · Cited by 15 · see citing works
“…students with different language proficiency were employed to analyze students’ engagement with AWCF qualitatively in the use of stimulated recall, semi-structured interviews and retrospective verbal reports, reflective journals, etc. (Han, 2017; Koltovskaia, 2020; Zhang, 2020; Ranalli, 2021).”
BackgroundMethodKloppers, J. (2023) · International Journal of Computer-Assisted Language Learning and Teaching · doi:10.4018/ijcallt.325792 · Cited by 17 · see citing works
Establishing Trustworthy Artificial Intelligence in Automated FeedbackPreprint
Lin, J., Sha, L., Li, Y., Gašević, D., & Chen, G. (2022) · doi:10.35542/osf.io/5efxn · Cited by 8 · see citing works
Less-proficient EFL students' use of Grammarly in writing: Behavior, cognition, and affect
Anastasia, W., Murtisari, E. T., & Isharyanti, N. (2024) · The JALT CALL Journal · doi:10.29140/jaltcall.v20n1.1089 · Cited by 7 · see citing works
Kang, S., & Kim, M. (2026) · Journal of English for Academic Purposes · doi:10.1016/j.jeap.2026.101639 · Cited by 11 · see citing works
Papi, M., Turner, J. E., Song, W., Soliman, H. A. A. M., Mahbodi, M., & Yarbrough, A. (2026) · Journal of Second Language Writing · doi:10.1016/j.jslw.2026.101318 · Cited by 7 · see citing works
Yamashita, T. (2022) · Computer Assisted Language Learning · doi:10.1080/09588221.2022.2068612 · Cited by 8 · see citing works
“First, as comments are provided by the instructor, human-delivered SWCF may be perceived as trustable, which is said to increase revision engagement (Ranalli, 2021), by learners.”
Designing Language Learning Experiences With Generative AI Tools
Czerkawski, B. C. (2024) · Advances in educational technologies and instructional design book series · doi:10.4018/979-8-3693-0872-1.ch015 · Cited by 7 · see citing works
Research impacts and trends in feedback literacy: a bibliometric analysis
Liang, X., Soh, S. B., Hamzah, N. H., & Wang, P. (2024) · Assessment & Evaluation in Higher Education · doi:10.1080/02602938.2024.2407945 · Cited by 9 · see citing works
“Recent studies have highlighted the importance of student engagement with feedback in L2 writing (e.g. Zhang and Hyland 2018; Fan and Xu 2020; Ranalli 2021).”
Xu, Q., & Wang, L. (2025) · Innovation in Language Learning and Teaching · doi:10.1080/17501229.2025.2490100 · Cited by 6 · see citing works
“The findings underscore both the importance of accurate and appropriately aligned AF and the need for learners to develop metacognitive skills in AI-assisted learning environments (Ranalli 2021).”
GenAI in Academic Writing- Empowering Learners or Redefining Traditional Pedagogical Practices?
Zaheer, S., Ma, C., Zhu, Y., & Vasinda, S. (2025) · International Journal of Artificial Intelligence · doi:10.4018/ijaitl.373582 · Cited by 9 · see citing works
“The impact of GenAI tools on improving linguistic accuracy is well documented, with platforms such as Grammarly and Criterion providing precise, immediate feedback to address common writing challenges (Liu et al., 2023; Ranalli, 2021).”
InfluentialChen, J., & Huang, K. (2025) · Computer Assisted Language Learning · doi:10.1080/09588221.2025.2508805 · Cited by 7 · see citing works
Corrective Feedback of AI Tools in Writing and Translation
Jomaa, N. J. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3693-9511-0.ch010 · Cited by 6 · see citing works
Wilson, J., Cordero, T. M. C., Potter, A., Myers, M., MacArthur, C. A., Beard, G., Fudge, E. A., Raiche, A., & Ahrendt, C. (2024) · International Journal of Changes in Education · doi:10.47852/bonviewijce42024011 · Cited by 5 · see citing works
Yan, Z., Jalaluddin, I. B., Mamat, R. B., & Zhao, T. (2025) · Journal of Language Teaching and Research · doi:10.17507/jltr.1603.20 · Cited by 5 · see citing works
“Some scholars also express concerns over the potential misuse of AWE, such as relying solely on automated feedback without incorporating certain human evaluations (Ranalli, 2021).”
Student engagement with oral feedback on second language learning: A methodological review
Liu, J., & Su, W. (2025) · System · doi:10.1016/j.system.2025.103752 · Cited by 5 · see citing works
Yan, X., & Huang, B. H. (2025) · TESOL Quarterly · doi:10.1002/tesq.70043 · Cited by 10 · see citing works
Beyond error correction: lower-proficiency EFL learners’ engagement with Grammarly
Murtisari, E. T., Januardi, J. I., Bonar, G., & Kurniawan, D. (2025) · Language Awareness · doi:10.1080/09658416.2025.2585004 · Cited by 5 · see citing works
“Ranalli (2021) found that trust, rather than proficiency, shaped feedback engagement among Chinese EsL students (B2–C1), highlighting the complex, contextual nature of engagement (Hiver et al., 2024).”
InfluentialStudent Engagement With AI Feedback: Evidence From Ethnic Minority Students in China
Yang, F., Li, K., & Qian, L. (2025) · International Journal of Applied Linguistics · doi:10.1111/ijal.70066 · Cited by 5 · see citing works
Li, X., Wang, X., & Daems, J. (2026) · Computer Assisted Language Learning · doi:10.1080/09588221.2026.2631658 · Cited by 5 · see citing works
Using Online Tools to Help Students Elevate Lexical Range in English-as-a-Foreign-Language Writing
Nguyen, B. T. T. (2023) · RELC Journal · doi:10.1177/00336882231217528 · Cited by 5 · see citing works
“It was utilized largely for proofreading purposes rather than contributing to learning (Ranalli, 2021).”
Enhancing the English writing skills of in-service students using marking mate automated feedback
Huynh-Cam, T.-T., Agrawal, S., Bui, T.-T., Nalluri, V., & Chen, L. (2023) · Asia Pacific Education Review · doi:10.1007/s12564-023-09904-7 · Cited by 5 · see citing works
“…& Wilson, 2020) students in the United States; undergraduate students in Taiwan (Liao, 2016), Japan (Suzuki et al., 2019), the United States (Ranalli, 2021), Saudi Arabia (Aluthman, 2016), Hong Kong (Cheng, 2017), China (Zhang & Hyland, 2018), and Bahraini (Wali & Huijser, 2018);…”
Advancing Language Assessment with GPT: Is It Nonnative-Language Friendly?
Won, D.-O., Shin, Y. K., Kim, H.-J., & Yoo, I. W. (2025) · Language Assessment Quarterly · doi:10.1080/15434303.2024.2444349 · Cited by 5 · see citing works
“These systems, driven by natural language processing algorithms, provide diagnostic scores and comprehensive feedback on written performances (e.g., McCarthy et al., 2022; Ragheb Al-Ghezi et al., 2023; Ranalli, 2021).”
Chon, Y. V., & Shin, D. (2026) · Journal of Computer Assisted Learning · doi:10.1002/jcal.70259 · Cited by 7 · see citing works
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Exploring student interpreters’ engagement with different sources of feedback on note-taking
Xue, R., & Liu, Q. (2024) · Innovations in Education and Teaching International · doi:10.1080/14703297.2024.2354739 · Cited by 5 · see citing works
“…on feedback sources, concentrating on teachers (e.g. Han & Hyland, 2015; Zheng & Yu, 2018), peers (e.g. Geng & Yu, 2023; Han & Yang, 2021), automated systems (e.g. Ranalli, 2021; Y. Xu & Han, 2020), and multiple-sources interaction environment (e.g. Z. Zhang & Hyland, 2022; Y. Zhang & Jiang, 2022).”
Response to written commentary in preparation for high-stakes second language writing assessment
Pearson, W. S. (2022) · Asian-Pacific Journal of Second and Foreign Language Education · doi:10.1186/s40862-022-00145-6 · Cited by 4 · see citing works
“These figures are higher than studies of teacher WFC in tertiary-level process writing environments (Christiansen & Bloch, 2016; Conrad & Goldstein, 1999; Ene & Upton, 2014; Hyland, 2003; Nurmukhamedov & Kim, 2009; Ranalli, 2021; Zhang & Hyland, 2018), where comments across multiple drafts serve as scaffolding to help…”
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Review of Feedback in Automated Essay ScoringPreprint
Jong, Y.-J., Kim, Y.-J., & Ri, O.-C. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4462105 · Cited by 4 · see citing works
Taguba, H., & Plata, S. M. (2025) · Language Testing in Asia · doi:10.1186/s40468-025-00362-z · Cited by 3 · see citing works
“Most studies on learner engagement have focused on university contexts (Zhang & Hyland, 2018; Zhang & Hyland, 2022; Man et al., 2021; Zhang, 2022; Koltovskaia & Mahapatra, 2022; Ranalli, 2021).”
Lee, M., Jang, E. E., & Hannah, L. (2025) · TESOL Quarterly · doi:10.1002/tesq.70032 · Cited by 3 · see citing works
Li, X., Wang, W., & Gu, L. (2025) · Assessment & Evaluation in Higher Education · doi:10.1080/02602938.2025.2587925 · Cited by 4 · see citing works
“Students may allocate a hierarchy of trust and value to different feedback processes based on the feedback providers’ qualities, regardless of the content of the feedback message received (Ranalli 2021; Zhou et al. 2021).”
Haddadian, G., Kim, M. K., & Haddadian, N. (2025) · Research Synthesis in Applied Linguistics · doi:10.1080/29984475.2025.2598266 · Cited by 3 · see citing works
Soyoof, A., Reynolds, B. L., Rassaei, E., Kao, C.-W., & Ha, X. V. (2025) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2025.100530 · Cited by 3 · see citing works
- Authors
- Ranalli, J.
- Affiliations
- Iowa State University (US)
- Corresponding author
- Ranalli, Jim
- ORCID
- Ranalli, Jim
- Venue
- Journal of Second Language Writing
- Publisher
- Elsevier BV
- Published
- 2021-06
- Volume
- 52
- Pages
- 100816
- ISSN
- 1060-3743 (ISSN-L)
- OpenAlex
- W3148248532
- Semantic Scholar
- bc20929cd12bf53ca312d3549b91fc4e442bebd3
- Cited by
- 184
- References
- 51
- Citations by year
- 2026: 68 · 2025: 46 · 2024: 30 · 2023: 18 · 2022: 19 · 2021: 3
- FWCI
- 69.13
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Journal of Second Language Writing (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, student engagement with feedback, Grammarly, second language writing, learning orientation, feedback acceptance, English as a foreign language
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