The study, led by Neda Naqdi Babaee, adopted a nonrandomized pretest-posttest design. Twenty-five learners received ChatGPT-generated feedback while 21 received teacher-written comments from the same instructor, who taught both groups within an identical instructional sequence. Across two complete feedback-revision cycles, students in each condition wrote argumentative and expository paragraphs of roughly 200 words and revised them after receiving comments. Writing was scored by two trained raters, blind to condition and testing phase, using adapted IELTS-based analytic descriptors covering task achievement, coherence and cohesion, lexical resource, and grammatical accuracy, with the four domain scores averaged into an overall composite.

The headline result was clear-cut. After adjusting posttest scores for corresponding pretest performance, the ChatGPT condition showed a robust advantage in overall writing, F(1, 43) = 16.86, p < .001, with a partial eta squared of 0.282. None of the baseline differences between groups was statistically significant, and tests confirmed that the relationship between pretest and posttest scores was comparable across conditions, supporting the validity of the adjustment approach.

The domain-level results demanded more caution. Unadjusted tests favored the ChatGPT condition for task achievement, coherence and cohesion, and lexical resource, but not for grammatical accuracy. Because five related outcomes were examined, the researchers applied a Holm adjustment across the four domain tests as a familywise sensitivity analysis. Only task achievement survived this correction, with an adjusted p value of .031. Lexical resource, at an adjusted p of .057, and coherence and cohesion, at .081, fell just above the threshold and were interpreted as suggestive rather than confirmatory. Robustness checks told a similar story: raw posttest comparisons, nonparametric tests, and Hedges g values ranging from 0.69 to 1.27 supported the ChatGPT advantage for several outcomes, but gain-score analyses reached the conventional significance boundary only for overall writing.

Perhaps the most practically important null finding concerned grammar. Both conditions directed attention to grammatical accuracy, and both groups improved descriptively over the four weeks, yet neither the adjusted models nor any of the sensitivity analyses detected a reliable difference between them. The authors suggest that short interventions may be insufficient to distinguish grammatical development, which typically requires repeated practice and sustained attention to recurring error patterns. The result, they stress, does not establish complementary roles for the two feedback sources; it indicates only that the implemented ChatGPT condition offered no clear grammatical advantage over teacher comments.

The qualitative strand of the study, based on open-ended post-intervention responses analyzed through reflexive thematic analysis, revealed two broad themes: actionable revision support, and trust, control, and feedback authority. Students in the ChatGPT condition repeatedly described the AI comments as immediate and elaborated. One learner explained that the feedback did not simply flag a weak paragraph but showed that an example was insufficient and suggested how to explain it more clearly, helping revise content rather than only grammar. Another said the most useful part was organization, because the system identified where connections between sentences were unclear, allowing new linking words or reordering of ideas — a shift, the student noted, from a previous habit of only checking grammar.

Vocabulary emerged as a particularly revealing area of learner experience. Students reported that ChatGPT offered alternatives for repeated or imprecise wording, expanding their options for academic expression, but they also emphasized the responsibility to judge appropriateness. One participant noted that some suggested words were too formal for their level and had to be checked against the intended sentence. This combination of availability and selection — a wider menu of choices paired with the learner’s own evaluative filtering — recurred throughout the accounts and is consistent with the favorable but statistically inconclusive lexical pattern in the quantitative results.

Trust worked differently across the two conditions. ChatGPT users valued speed and detail but did not treat the output as uniformly authoritative; several reported that suggestions sometimes altered their intended meaning or produced grammatical advice that made sense only in context, so they read carefully and rejected comments that distorted their ideas. Some linked immediacy to momentum, saying that instant access let them revise while their intended meaning was still fresh, and one student reported feeling more independent and confident. Teacher feedback recipients, by contrast, trusted comments because the instructor knew the course, the task, and the assessment expectations — but that authority could narrow revision. Several said they corrected only the parts the teacher marked and did not explore alternative versions, appreciating the prioritization while sometimes wanting more explanation or examples for content expansion and lexical refinement.

The authors are explicit that these findings concern implemented feedback conditions, not feedback source in isolation. The ChatGPT condition bundled AI generation with rapid access, extensive comments, and revision alternatives, while the teacher-written condition was more selective and contextually situated, so the estimated difference may reflect differences in amount, timing, specificity, and elaboration rather than the mere presence of AI. The study also cannot claim complementarity, because it included no combined ChatGPT-plus-teacher condition; a sequenced approach remains a pedagogical proposition for future testing rather than an empirical conclusion. Additional limitations include the nonrandomized allocation, the modest single-university sample, the four-week duration without a delayed posttest, and the fact that the qualitative strand captured perceptions rather than observed draft-to-draft uptake.

For educators navigating the rapid arrival of generative AI in language classrooms, the study offers a measured message. AI-generated feedback appears most useful when learners need elaborated options for task fulfillment, organization, or lexical revision, provided instruction requires them to evaluate suggestions against meaning and task criteria. The null grammatical result warns against assuming such tools can replace explicit grammar instruction or sustained corrective-feedback practice, and teacher feedback retains a distinct role in prioritizing problems and aligning revision with course goals. What the research most clearly demonstrates is not that ChatGPT is a superior teacher, but that the way feedback is delivered — how much, how fast, and how elaborated — may matter as much as who or what produces it.

Subject of Research: Comparing ChatGPT-generated and teacher-written feedback effects on EFL university students' writing development

Article Title: Comparing ChatGPT and teacher feedback for EFL writing development

Article References: Babaee, N. N. (2026). Comparing ChatGPT and teacher feedback for EFL writing development. Discover Education, 5(1), Article 1211. https://doi.org/10.1007/s44217-026-02329-5

Image Credits: AI Generated

DOI: 10.1007/s44217-026-02329-5

Keywords: ChatGPT, generative AI, EFL writing, written corrective feedback, second language writing, teacher feedback, quasi-experimental study, ANCOVA, learner perceptions, task achievement, lexical resource, grammatical accuracy

The study, conducted by Bo Yuan of the School of Educational Studies at Universiti Sains Malaysia, followed 1,043 Chinese undergraduates across three waves of data collection, each separated by intervals of three to four weeks. Rather than treating AI dependency as a single monolithic construct, the research distinguished three dimensions: cognitive and behavioral dependence, which reflects how deeply students integrate AI into their thinking and routine actions; functional dependence, which captures the practical reliance on AI to complete tasks that students might otherwise manage themselves; and emotional dependence, which describes the affective attachment and comfort students derive from having the tool at hand. This dimensional approach matters because each component may develop through different psychological mechanisms and may therefore require different interventions.

The methodological centerpiece of the study is the cross-lagged panel network analysis, a technique that combines the longitudinal rigor of cross-lagged panel models with the granularity of network psychometrics. In a conventional cross-lagged design, researchers examine whether variable A at time one predicts variable B at time two after accounting for B’s own prior level, thereby offering a window into prospective directional relationships. The network extension treats each measured construct as a node in a web of interconnections, allowing researchers to see not just whether two variables influence each other but how entire constellations of psychological factors interact across time. This approach has gained traction in studies of psychopathology and, more recently, in educational psychology, where the interplay between motivation, emotion, and behavior resists simple linear explanation.

Yuan grounded the investigation in two complementary theoretical frameworks. The first is Pekrun’s control-value theory of achievement emotions, which holds that emotions arising in academic settings are shaped by students’ perceptions of control over outcomes and the subjective value they attach to those outcomes, and that these emotions in turn feed back into learning behavior and performance. The second is self-determination theory, which frames human motivation around the satisfaction of autonomy, competence, and relatedness needs. Together, these frameworks predict that the experience of using AI in writing is not emotionally neutral: each encounter with the tool generates affective responses such as enjoyment, hope, anxiety, or boredom, and those responses may either consolidate healthy engagement or nudge students toward problematic reliance.

The central finding is that AI writing experience was prospectively associated with all three dimensions of GenAI dependency, but only through dimension-specific achievement-emotion pathways. Enjoyment emerged as a key emotional conduit. Students whose prior AI writing experience predicted greater enjoyment subsequently showed higher emotional dependence, suggesting that the pleasure of effortless, AI-assisted composition can quietly deepen an affective attachment to the tool. Enjoyment, together with hope, also mediated the path to functional dependence, indicating that positive emotions about AI use may encourage students to hand over more of the practical writing workload to the machine. Hope, in this context, likely reflects students’ anticipation that AI will help them achieve better outcomes, a forward-looking emotion that makes delegation to the tool feel like a rational strategy rather than a crutch.

Anxiety told a different and more surprising story. The indirect association between AI writing experience and cognitive and behavioral dependence through anxiety was negative, meaning that students who experienced more anxiety in connection with AI writing were less likely to develop the deepest, most habit-like form of reliance on the tool. This finding complicates the intuitive assumption that negative emotions always fuel problematic use. Instead, anxiety may function as a psychological brake, signaling to students that something about their relationship with AI feels wrong and prompting them to pull back before dependence hardens into routine. All four of these supported indirect pathways produced bootstrap confidence intervals that excluded zero, a statistical standard indicating that the effects were unlikely to be artifacts of sampling variability.

Equally informative were the pathways that failed to reach that threshold. The remaining five achievement emotions examined in the study did not yield indirect effects whose bootstrap confidence intervals excluded zero, meaning the evidence for their mediating role was not statistically compelling. This pattern of specificity is scientifically valuable: it suggests that not all emotions matter equally in the formation of AI dependency, and that blanket assumptions about the emotional drivers of technology overuse may obscure the more precise mechanisms at work. Enjoyment and hope appear to open the door to dependence, while anxiety may help keep it closed, at least for the cognitive and behavioral dimension.

An important caveat accompanies these conclusions. The supported indirect associations were estimated at the between-person level, meaning they describe differences across individuals rather than fluctuations within a single person over time. In practical terms, a student whose AI writing experience is generally higher than their peers’ tends to show the predicted emotional and dependency patterns relative to those peers, but the study cannot confirm that an individual student’s day-to-day changes in AI use produce corresponding changes in their own emotions and dependence. This distinction, long emphasized in critiques of cross-lagged panel methodology, is crucial for interpreting the findings and for designing future research that can separate within-person dynamics from stable between-person differences.

The study’s implications reach beyond the statistics classroom. As generative AI becomes embedded in writing instruction worldwide, educators and policymakers face the challenge of distinguishing productive tool use from dependency that erodes independent skill. The dimensional findings suggest that interventions should be tailored: addressing emotional dependence may require fostering alternative sources of enjoyment in writing, while curbing functional dependence might involve building students’ confidence and hope in their own unassisted capabilities. Meanwhile, the protective role of anxiety hints that a degree of critical self-awareness about AI use, rather than unqualified enthusiasm, may serve students well. The research also connects to a broader scientific conversation about cognitive offloading, the well-documented tendency to delegate memory and reasoning tasks to external tools, raising the question of whether AI writing assistants represent a new frontier of that ancient cognitive strategy or a qualitatively different kind of dependence. As the technology evolves, longitudinal studies of this kind will be essential for understanding not just how much students rely on AI, but why.

Subject of Research: Longitudinal study of how AI writing experience shapes generative AI dependency among Chinese EFL student writers

Article Title: The Longitudinal Relationship between EFL Writers’ AI Writing Experience and GenAI Dependency: A Cross-Lagged Panel Network Analysis

Article References: Yuan, B. (2026). The Longitudinal Relationship between EFL Writers’ AI Writing Experience and GenAI Dependency: A Cross-Lagged Panel Network Analysis. International Journal of Mental Health and Addiction. https://doi.org/10.1007/s11469-026-01745-w

Image Credits: AI Generated

DOI: 10.1007/s11469-026-01745-w

Keywords: generative AI, AI dependency, EFL writing, achievement emotions, cross-lagged panel network, longitudinal study, self-determination theory, cognitive offloading, higher education, academic emotions, Longitudinal, Relationship

The episode comes from a new study published in Discover Education by Eshetie Kasie, an Ethiopian TEFL practitioner and researcher at Bahir Dar University. The research followed ten undergraduate English as a foreign language (EFL) students across one full academic semester as they used generative AI, mostly through mobile phones and often through patchy internet connections, to manage writer’s block. The findings extend an influential framework for critical AI literacy with two new layers, one emotional and one structural, and they offer a rare Global South perspective on a conversation that has been dominated by classrooms in wealthy, well-connected countries.

Writer’s block is not a trivial complaint. Researchers describe it as a tangle of cognitive overload, emotional strain, linguistic difficulty, and conflicts of identity that surface during composition, and these factors intensify in EFL contexts where students face limited proficiency, cultural expectations, and high-stakes assessment. A student who struggles to retrieve vocabulary in a second language often experiences that struggle as anxiety rather than as a purely linguistic gap, which then compounds the original difficulty. In Ethiopia, where English is the medium of instruction but Amharic and other local languages dominate everyday life, the blank page can feel especially unforgiving. Generative AI promised relief, but the study shows that the relief was conditional, arriving only when students learned to interrogate the tool rather than simply obey it.

The study’s theoretical anchor is the Affective Process and Strategic Engagement (APSE) framework, developed by researchers from first-year second-language writing classrooms in the United States. APSE identifies four interrelated dimensions of critical AI literacy: awareness of what AI can and cannot do, positionality toward its use, strategies for interacting with it, and evaluation of its output. But the framework was built in a setting that assumes stable internet access and Anglocentric rhetorical norms, assumptions that collapse quickly in a university where power outages are routine and connectivity is mobile-only. Kasie’s extension, which he calls APSE-Extended for Block, keeps the four core dimensions and wraps them in an inner affective layer addressing anxiety reduction, confidence building, and identity resilience, and an outer equity layer addressing connectivity constraints, linguistic hierarchies, and resistance to Anglocentric assumptions embedded in AI outputs.

The methodology was deliberately multimodal. Kasie purposefully selected ten undergraduates, five women and five men spanning first to third year, all of whom screened as experiencing moderate or high writer’s block severity. Over fourteen to sixteen weeks, he gathered semi-structured interviews before, during, and after the semester, weekly reflective journals, anonymized ChatGPT conversation logs, successive writing drafts, and classroom observations. Amharic portions of journals and interviews were professionally translated and checked against the originals. To quantify how critically students engaged with the technology, Kasie compared every AI-generated output in the conversation logs against the corresponding segment of each student’s revised draft, coding outputs as accepted, substantially revised, or rejected, and then corroborating those judgments in stimulated recall sessions where students explained their decisions.

The developmental pattern that emerged was strikingly consistent. Most participants began the semester trusting ChatGPT almost unconditionally for English writing tasks, then encountered inaccuracy, cultural misrepresentation, and Anglocentric bias the moment they applied it to Ethiopian content. One student asked for help drafting a reflection on Timkat, the Ethiopian Orthodox celebration of Epiphany, and received a generic description of a religious festival that omitted the procession of the tabot and the ritual blessing of water entirely. The student responded by researching and rewriting the section from memory and family accounts. By semester’s end, eight of the ten participants treated AI outputs as provisional starting points requiring cultural filtering rather than finished material.

Positionality, the framework’s term for how writers situate themselves relative to the tool, proved equally consequential. Eight participants actively reclaimed authorship and resisted perceived Western or native-speaker bias in AI outputs. A third-year student named Selam, given a pseudonym in the study, described how the AI consistently framed gender issues through a Western feminist lens, presenting women’s oppression as uniform across cultures. That framing, she said, did not reflect her own story, and she had to decide for herself what was true for Ethiopian women. Students revised or rejected content that flattened communal and relational framings common in Amharic argumentative writing into individualistic Western argument structures. A second-year student, Kidist, rejected outright an AI characterization of Ethiopian schools as underdeveloped and backward, rewriting the passage to say instead that her schools faced limited resources but also showed innovation and resilience.

The quantitative picture reinforces the qualitative one. Based on the draft-log comparisons, nine participants rejected an estimated 60 to 80 percent of AI-generated content overall, particularly when it appeared biased, generic, or culturally inappropriate, and they did not simply delete unwanted material but rewrote it to reflect their own framing and tone. Nine students also developed structured prompting strategies that functioned as affective scaffolding for block relief. A first-year student, Dawit, described asking ChatGPT for three short ideas about Ethiopian youth unemployment without full sentences, then using those fragments to begin his own writing. Others requested a single counterargument to test against their thesis, or an outline skeleton they filled in entirely with their own wording. All ten participants reported that careful, limited use of generative AI reduced anxiety and increased confidence, with one student describing AI as a helper, not a boss.

Yet the equity layer of the extended framework tells a sobering story. All ten participants faced persistent connectivity challenges and linguistic mismatch. Dawit explained that when the internet cuts off, his ideas disappear and he feels blocked again, a description of how an infrastructure gap translates directly into a personal interruption of the writing process. Students often drafted offline and revised only once connectivity returned. The study connects this lived constraint to broader scholarship: a systematic review of AI use in Global South EFL classrooms found that AI holds real potential for student autonomy only when implementation is context-sensitive and equity-driven, while research on South African higher education cautions against treating AI literacy as a purely technical skill separable from local conditions. Notably, one first-year student with high block severity copied full paragraphs from AI outputs without revision all semester, suggesting that block severity, not just year of study, shapes how readily students resist algorithmic norms.

The practical payoff is a four-phase classroom cycle that educators in similar contexts can adapt: an awareness phase in which students experiment with AI on local topics and discuss cultural mismatches as they arise; a positionality phase in which they revise AI content to incorporate Amharic rhetorical patterns and local perspectives; a strategies phase introducing spark prompting for ideas only, preserving ownership of the text; and an evaluation phase in which students apply a personal checklist judging whether an output respects Ethiopian realities and sounds like their own voice. Kasie is careful about the limits of his findings, which remain bound to ten students at a single university over one semester, and he calls for longitudinal and comparative research across urban and rural institutions. But the core insight travels well beyond Bahir Dar. Critical AI literacy, the study suggests, is not just a technical skill of writing better prompts. It is an emotional and political practice in which learners decide what the machine may say about them, and in which a coffee ceremony, a religious festival, or a school described as backward can become the moment a student stops outsourcing judgment and starts writing as themselves.

Subject of Research: Critical AI literacy and writer's block among Ethiopian EFL undergraduates using generative AI in low-resource settings

Article Title: Extending the affective process and strategic engagement framework to address writer’s block in Ethiopian English as a foreign language contexts through critical artificial intelligence literacy and ethical generative artificial intelligence in low-resource settings

Article References: Kasie, E. (2026). Extending the affective process and strategic engagement framework to address writer’s block in Ethiopian English as a foreign language contexts through critical artificial intelligence literacy and ethical generative artificial intelligence in low-resource settings. Discover Education, 5(1), Article 1133. https://doi.org/10.1007/s44217-026-02049-w

Image Credits: AI Generated

DOI: 10.1007/s44217-026-02049-w

Keywords: critical AI literacy, generative AI, writer's block, EFL writing, Ethiopia, Bahir Dar University, APSE framework, Global South, equity, ChatGPT, second language writing, digital divide

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