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Cited by 10EFL/ESL Teaching and LearningEnglish Language Learning and TeachingStudent Assessment and Feedback

Beyond red ink: AI feedback and ESL academic writing development in higher education

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Mujeeb, Nashra, Arfan Lodhi, Muhammad, Ali, Aqib, Mashwani, Wali Khan, Mostafa, Samih M., Ahmadini, Abdullah Ali H.·2026·Language Testing in Asia
DOI 10.1186/s40468-026-00442-8
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Abstract

This study examines the effect of artificial intelligence (AI) generated feedback on the academic writing skills of English as a Second Language (ESL) students at the Faculty of Computing, Islamia University of Bahawalpur, Pakistan. A quasi-experimental mixed-methods design was employed with 400 students from first to eighth semesters, divided into an experimental group (receiving AI-generated feedback using Grammarly and ChatGPT) and a control group (receiving Teacher feedback). Pre and post-test essays, questionnaires, and interviews were used for data collection. Results showed that students receiving AI-generated feedback achieved greater improvements in grammar (43.5%), vocabulary (37.3%), cohesion (49.5%), and overall writing quality (37.8%) compared to the control group, with large effect sizes across all components. Significant reductions were observed in minor errors, including article usage, spelling, and consistency of tense. Qualitative findings revealed that students had positive perceptions of AI-generated feedback, reporting increased awareness of errors, improved confidence, and greater independence during revision; however, some concerns about overreliance were also noted. The study concludes that AI-generated feedback effectively supplements Teacher feedback, enhancing short-term writing performance and indicating potential for increased learner autonomy in ESL academic contexts.

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  • A study of 156 Pakistani students found that 35% reported accepting AI suggestions for English academic writing without critical evaluation.

    “Similarly, a study of 156 Pakistani students found that 35% reported accepting AI suggestions for English academic writing without critical evaluation (Mujeeb et al., 2026).”

    From Think Before You Accept: Can Written Justification Reduce Uncritical Uptake of AI Writing Suggestions? (2026) · cites this paper as Mujeeb et al., 2026

    The qualitative results (Related work; page unavailable) say that over a third of students admitted a tendency to accept AI suggestions uncritically, supporting the behavior described. But the Participants and Results sections report 400 students, not 156, and the retrieved text does not give an exact figure of 35%. (LLM, conf=0.91)

    Experimental — accuracy not yet measured

  • Some students adopt AI suggestions mechanically, with limited evaluation of their appropriateness.

    “Evidence from recent studies suggests that while some students engage with AI suggestions through deliberate evaluation and decision making, others adopt suggestions more mechanically, with limited evaluation of their appropriateness (Du & Chen, 2026;Mujeeb et al., 2026;Alghamdi & Alghizzi, 2026).”

    From Think Before You Accept: Can Written Justification Reduce Uncritical Uptake of AI Writing Suggestions? (2026) · cites this paper as Mujeeb et al., 2026

  • The findings echo students' self-reported experiences of uncritical AI uptake in prior survey and interview research and add behavioral evidence that students may still accept AI suggestions wholesale even when reminded that AI can make mistakes.

    “This echoes students' self-reported experiences of uncritical AI uptake in prior survey and interview research (Du & Chen, 2026;Mujeeb et al., 2026), and adds behavioral evidence that students may still accept AI suggestions wholesale even when they are reminded that AI can make mistakes.”

    From Think Before You Accept: Can Written Justification Reduce Uncritical Uptake of AI Writing Suggestions? (2026) · cites this paper as Mujeeb et al., 2026

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Research works printed but not linked: 24

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Authors
Mujeeb, N., Arfan Lodhi, M., Ali, A., Mashwani, W. K., Mostafa, S. M., & Ahmadini, A. A. H.
Affiliations
  • Tun Hussein Onn University of Malaysia (MY)
  • Kohat University of Science and Technology (PK)
  • Jazan University (SA)
Corresponding author
  • Mujeeb, Nashra
  • Mashwani, Wali Khan
ORCID
Venue
Language Testing in Asia
Publisher
Springer Science and Business Media LLC
Published
2026-03-04
Volume
16
Issue
1
ISSN
2229-0443 (ISSN-L)
DOI
10.1186/s40468-026-00442-8
OpenAlex
W7133481869
Cited by
10
References
7
Citations by year
2026: 10
FWCI
113.32
Type
journal article
Language
en
Open access
Licence
Locations
  • Language Testing in Asia (publishedVersion, cc-by-nc-nd)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Social Sciences › Arts and Humanities › Language and Linguistics › EFL/ESL Teaching and Learning
  • Physical Sciences › Computer Science › Information Systems › English Language Learning and Teaching
  • Social Sciences › Education › Student Assessment and Feedback
Keywords
AI-generated feedback, ESL academic writing, English as a second language, Grammarly, ChatGPT, teacher feedback, writing quality, grammar, vocabulary, cohesion, learner autonomy, student perceptions, overreliance on AI, writing revision

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