AI Feedback Boosts Writing Skills, Motivation and Self-Regulation in Saudi EFL Learners

Artificial intelligence has quietly entered one of the most stubborn bottlenecks in language education: the second-language writing classroom. A new quasi-experimental study from Saudi Arabia suggests that when the AI-powered platform Grammarly is woven into a structured process-writing course, university students learning English as a foreign language make markedly larger gains in writing quality, motivation, feedback use, and self-regulated learning strategies than peers taught with the same curriculum but without automated feedback. The research, published in the open-access journal Heliyon, offers one of the more carefully instrumented looks yet at what happens when automated feedback meets the recursive craft of drafting and revision.

The study, conducted by Sayed M. Ismail, Faris Allehyani, Khaled Ahmed Abdel-Al Ibrahim, and Mohamad Ahmad Saleem Khasawneh, recruited 60 undergraduate English majors aged 20 to 29 at a Saudi university, all screened as intermediate-level learners using the Oxford Quick Placement Test. Participants came from two intact sections of the same academic writing course taught by the same experienced instructor. One section of 30 students received twelve weeks of process-based writing instruction supplemented with Grammarly during drafting and revision; the other 30 completed an identical syllabus, task sequence, and revision routine without any AI-generated feedback. Crucially, the researchers standardized nearly everything else: the same ten argumentative writing tasks, the same three-draft cycle per task, the same deadlines, testing conditions, and assessment rubrics.

The theoretical scaffolding behind the experiment is as interesting as the results. Rather than treating Grammarly as a generic digital add-on, the authors framed automated feedback through three interlocking lenses: self-determination theory, which holds that learners persist when tools support competence, autonomy, and meaningful participation; social-cognitive and self-regulated learning theory, which emphasizes goal setting, monitoring, and strategic adjustment; and feedback theory, which insists that feedback only has value when learners understand it, judge its relevance, and convert it into revision action. In this model, Grammarly’s suggestions act as revision cues that can trigger a self-regulatory sequence: a learner notices recurring article errors, sets a goal to reduce them, checks later drafts, and reflects on whether the strategy worked.

The quantitative results were striking across all four measured outcomes. Using linear mixed-effects models that accounted for repeated measurements within each learner, the team found that the Grammarly-supported group improved significantly more than the control group from pre-test to post-test. Writing quality, scored on a 20-point rubric covering grammatical and lexical accuracy, fluency and coherence, and text structure, rose by 5.42 points in the experimental group versus 1.47 points in the control group, a differential gain of 3.95 points with a large standardized effect size of 1.77. Motivation climbed 13.50 points against 4.40, feedback utilization rose 4.44 points against 0.93, and self-regulated learning strategies jumped 13.90 points against 2.23. All four time-by-condition interactions were statistically significant.

Behavioral evidence from draft-to-draft comparisons reinforced the test scores. Trained raters, working independently and achieving strong inter-rater agreement, scored how accurately, completely, deeply, and consistently learners incorporated available feedback into successive drafts. In the Grammarly group, 77 percent reached the highest rubric level for accurate incorporation of feedback without distorting intended meaning, compared with 43 percent of controls. Eighty percent of the AI-supported students addressed all feedback points versus 60 percent of controls, 70 percent made revisions that went beyond surface-level correction versus 50 percent, and 85 percent maintained improvements across later drafts versus 65 percent. The pattern suggests that immediate automated cues did not merely decorate the texts; they changed what students actually did between drafts.

Semi-structured interviews with the experimental group added texture and caution in equal measure. Participants reported that Grammarly was easy to access and use outside class, and that it sharpened their awareness of grammar, punctuation, sentence structure, and word choice. Immediate feedback supported revision while writing rather than after the fact. But the students were far from uncritical. Some found suggestions overly general, contextually inappropriate, or inadequate for complex sentence structures, and they consistently wanted more help with content development and argument organization than any sentence-level tool can provide. Notably, they credited their teacher with helping them decide when to accept, modify, or reject automated suggestions, underscoring that writer agency remained central to the intervention’s design.

That design deliberately constrained what Grammarly could do. Students used the platform’s grammar, spelling, punctuation, clarity, concision, word-choice, and style functions through its web editor and Microsoft Word integration, but generative rewriting, text-generation prompts, plagiarism detection, and citation generation were switched off. Before the intervention began, the experimental group received two 45-minute orientation sessions teaching them to evaluate suggestions against their intended meaning rather than accept them automatically. The researchers were explicit that the treatment targeted automated written feedback and learner-initiated revision, not AI-generated composition, a distinction that matters as institutions grapple with how generative AI reshapes academic writing.

The authors are equally candid about the limits of their evidence. Because participants belonged to pre-existing class sections, individual randomization was impossible, and instructional condition was perfectly aligned with class membership, meaning class-level confounding cannot be ruled out. The experimental group actually started with slightly lower scores on all four outcomes, which the longitudinal models incorporated but which cannot substitute for randomization. Novelty is another live concern: learners knew they were using a new AI tool, and prior research has shown that perceived novelty can inflate motivational responses. The motivation and self-regulation findings rest on self-report questionnaires, so the researchers interpret them alongside the harder performance and behavioral data, and they caution that no delayed post-test was administered to check whether gains persisted.

Despite those caveats, the convergence across evidence streams is what gives the study its force. Writing quality and feedback utilization, measured by independent raters on matched prompts without platform assistance, moved in the same direction as the self-reported motivation and self-regulation gains, and the interview accounts of immediate revision and contextual judgment complemented the numbers rather than contradicting them. The study was not designed to test whether feedback use statistically mediates writing improvement, so the proposed pathway from automated cues to self-regulation to better texts remains a theoretically informed interpretation rather than a demonstrated causal chain. Future work, the authors argue, should employ randomized designs, larger and more diverse samples, delayed assessments, and explicit mediation models, while comparing Grammarly with other AI writing tools.

The practical takeaway resists both hype and dismissal. Automated feedback, embedded in a disciplined process-writing routine with teacher guidance, appears to give second-language writers more opportunities to notice problems, revise strategically, and experience visible progress, which in turn may feed motivation and self-regulation. But the same students who benefited most also flagged the tool’s blind spots at the level of argument, audience, and organization, the dimensions where human instruction remains irreplaceable. As universities worldwide debate the role of AI in the writing classroom, this Saudi experiment suggests a middle path: treat platforms like Grammarly as supplementary revision resources within structured instruction, teach learners to judge suggestions critically, and keep teachers firmly in the loop for everything that lies beyond the sentence.

Subject of Research: Effects of Grammarly-supported AI writing instruction on EFL learners' writing quality, motivation, feedback utilization, and self-regulated learning strategies

Article Title: Grammarly-supported AI writing instruction in a Saudi EFL context: Motivation, writing quality, feedback utilization, and self-regulated learning strategies

Article References: Ismail, S. M., Allehyani, F., Ahmed Abdel-Al Ibrahim, K., & Khasawneh, M. A. S. (2026). Grammarly-supported AI writing instruction in a Saudi EFL context: Motivation, writing quality, feedback utilization, and self-regulated learning strategies. Heliyon, 12(15), Article e45557. https://doi.org/10.1016/j.heliyon.2026.e45557

Image Credits: AI Generated

DOI: 10.1016/j.heliyon.2026.e45557

Keywords: Grammarly, AI feedback, EFL writing, self-regulated learning, writing motivation, automated writing evaluation, Saudi Arabia, process writing, feedback utilization, quasi-experimental study, second language acquisition, Heliyon