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

The context matters enormously. Chinese classrooms operate within one of the world’s most assessment-centered education systems, one that traditionally emphasizes memorization, structured learning, and mastery of prescribed forms. Decades of research have noted the tension between this transmission-oriented tradition and the dialogic, inquiry-driven learning celebrated in Western pedagogy, a tension often cited in explanations of why Chinese postgraduates sometimes struggle with critical thinking when they arrive at universities abroad. Against that backdrop, generative AI arrives as something genuinely disruptive. Because large language models respond conversationally, they invite learners to question, negotiate, and iterate rather than simply to absorb. The review argues that this dialogic quality opens new possibilities for critical thinking inside a system that has historically left little room for it.

Technically, the linchpin of this interaction is prompt engineering, and the authors are careful to frame it as both a technical skill and a pedagogical one. A well-constructed prompt specifies the rhetorical task, the audience, the genre, the desired structure, and the constraints of the output; a poorly constructed one yields generic, sometimes confidently wrong prose. Research cited in the review shows that students who experiment with chain-of-thought prompting, role assignment, and iterative refinement engage in precisely the kind of metacognitive reflection that writing instructors try to cultivate. Deciding what to ask the model, evaluating what comes back, and revising the request forces learners to articulate their own goals and standards. In other words, the act of engineering a prompt can itself become a lesson in rhetoric, genre awareness, and self-regulated learning.

The scaffolding metaphor from second-language acquisition runs throughout the analysis. Generative AI can model academic phrasing, supply grammatical corrections, generate outlines, and provide feedback at a scale no human teacher could match, effectively extending the tradition of automated written corrective feedback into a far more responsive domain. Studies of GPT-4 and of dedicated grammar-checking systems suggest that such feedback can meaningfully support language production, particularly for learners who lack access to native-speaker interlocutors or one-on-one tutoring. Just as important, the review emphasizes student agency: when learners direct the interaction, decide which suggestions to accept, and justify their choices, they retain authorship of the text while borrowing computational muscle. The tool functions as a scaffold that can be gradually dismantled as competence grows, echoing classic computer-based scaffolding research in STEM education.

Yet the authors refuse to tell a purely triumphant story, and their treatment of risk is among the most substantive parts of the paper. The first danger is epistemic dependency: a learner who accepts every AI suggestion without scrutiny outsources judgment itself, hollowing out the very cognitive processes writing is supposed to develop. The second is the erosion of critical thinking, a particularly acute concern in a context where rote learning already discourages independent argument. The third is ethical ambiguity. Models hallucinate, fabricating citations and distorted facts with fluent confidence, a phenomenon now extensively documented in the literature on AI-generated content. Students who lack critical AI literacy may not even recognize when a source is invented, and the boundary between legitimate assistance and academic misconduct, sometimes dubbed ‘AI-giarism,’ remains contested among students and institutions alike.

These risks collide with specific features of the Chinese educational environment. High-stakes examinations such as the National College Entrance Examination shape what counts as good writing, and curriculum standards set by the Ministry of Education channel classroom practice toward measurable outcomes. Automated evaluation platforms like Pigai, which has accumulated more than a decade of use in Chinese English writing instruction, fit comfortably into that assessment culture because they score quickly and consistently. But the review warns that efficiency-oriented adoption could entrench the very habits—formulaic structures, surface-level correction, answer-seeking—that generative AI could otherwise disrupt. The technology’s pedagogical value, in other words, is not intrinsic; it depends on how educators mediate its use.

That is why the authors advance human–AI co-learning as a theoretical framework rather than a slogan. In their account, the learner and the model form a reciprocal system: the student supplies intent, disciplinary context, and critical evaluation, while the machine supplies fluency, alternatives, and tireless feedback. Each interaction potentially improves the human partner’s understanding, even though the model itself does not learn from the exchange in any pedagogical sense. Co-learning, properly understood, means the human side of the loop must be taught—explicitly, deliberately, and critically. Prompt engineering becomes part of digital literacy; rhetorical awareness becomes the standard against which AI output is judged; and metacognition becomes the habit of asking not just ‘is this correct?’ but ‘why is this appropriate, and did I decide it?’

The practical recommendations follow directly. The review calls for critical AI literacy to be built into curricula, so that students learn the capabilities and failure modes of large language models alongside their own writing development. It calls for educator mediation, noting recent intervention studies showing that language teachers can develop professional generative AI competence when given structured training, and that teacher guidance dramatically changes how productively students use these tools. And it calls for culturally responsive pedagogy—an approach that reconciles traditional Chinese learning practices, with their genuine strengths in disciplined practice and textual mastery, with reflective engagement in digital environments, rather than treating memorization and critical inquiry as enemies. Freirean dialogue, the authors suggest, need not require abandoning the intellectual virtues of the Chinese classroom.

The paper’s closing reframing may prove its most quotable idea: generative AI should be understood not as a shortcut but as a scaffold. A shortcut moves a student from question to answer while bypassing learning; a scaffold supports the climb while the learner’s own muscles do the work. By redesigning writing instruction around prompting, evaluation, and revision, the authors propose a pedagogical model in which learners reclaim authorship and engage more deeply in academic inquiry than either pure human drafting or pure machine generation would allow. For the hundreds of millions of ESL learners worldwide now staring at a chat window instead of a blank page, the stakes of getting that distinction right could hardly be higher.

Beneath the framework lies a body of empirical work the review draws on selectively. Eye-tracking studies of second-language learners engaging with automated feedback reveal that attention is uneven: students often skim surface corrections while overlooking deeper rhetorical comments, a finding that cautions against assuming AI feedback is absorbed simply because it is delivered. Similarly, evaluations of GPT-4 as a source of written corrective feedback show genuine grammatical competence but inconsistent judgment about discourse-level issues, reinforcing the review’s insistence that a human evaluator remain in the loop.

The theoretical lineage is also broader than the co-learning label suggests. The authors situate their argument within classic scaffolding theory, which holds that support should be contingent—offered when needed and withdrawn as competence develops—and within genre-based approaches to academic writing that treat text forms as socially situated choices rather than templates. Their invocation of Bloom’s taxonomy underscores a worry echoed across the AI-in-education literature: if learners delegate the lower-order tasks of drafting and polishing, the higher-order work of analysis and evaluation must be deliberately designed into instruction, or it will simply not occur.

Practical constraints receive attention too. Surveys of privacy and security in generative AI document real risks when students paste unpublished manuscripts into commercial systems, and the review’s emphasis on institutional mediation implicitly includes data governance. At the same time, teacher-facing studies suggest optimism is warranted: structured professional development can move language instructors from anxiety about misconduct toward confident integration, and students themselves report more productive use when teachers model critical prompting rather than banning the tools outright.

What emerges is a picture of a field still defining its terms. Whether human–AI co-learning becomes a durable theoretical contribution or a transitional description will depend on longitudinal evidence the review, by its own admission as a conceptual synthesis, cannot yet supply.

Subject of Research: Human–AI co-learning in generative AI–assisted academic writing among Chinese ESL learners

Article Title: Human–AI Co-Learning in Academic Writing Among Chinese ESL Learners

Article References: Chan, J., Wong, J., Ieong, J., & Pang, H. (2026). Human–AI Co-Learning in Academic Writing Among Chinese ESL Learners. Frontiers of Digital Education, 3(2), Article 15. https://doi.org/10.1007/s44366-026-0089-8

Image Credits: AI Generated

DOI: 10.1007/s44366-026-0089-8

Keywords: generative AI, prompt engineering, human–AI co-learning, Chinese ESL learners, academic writing pedagogy, AI literacy, ChatGPT, scaffolding, critical thinking, digital literacy, student agency, automated writing evaluation