Sustaining ESL Writing Development with AI-Driven Automated Feedback Systems: A Systematic Review (2006–2025)

Sustaining ESL Writing Development with AI-Driven Automated Feedback Systems: A Systematic Review (2006–2025)

M. Sharmithashini¹, Harwati Hashim²

¹Faculty of Education, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia

DOI: https://dx.doi.org/10.47772/IJRISS.2025.908000041

Received: 18 July 2025; Accepted: 26 July 2025; Published: 27 August 2025

ABSTRACT

The growing use of AI-driven feedback tools in ESL writing instruction has sparked considerable interest in recent years. This literature review explores how effective grammar checkers and paraphrasing tools are in supporting ESL learners’ writing proficiency. Using the PRISMA methodology, 107 studies were identified from Web of Science, Scopus, and ERIC, with 25 meeting the inclusion criteria. The findings indicate that while AI-driven grammar checkers improve grammatical accuracy and provide real-time corrective feedback, they are limited in addressing coherence, argument development, and critical thinking in writing. Similarly, paraphrasing tools support lexical variation and sentence restructuring but may contribute to semantic distortions and over-reliance among learners. The study also looks into how these tools impact teaching practices, emphasizing the importance of blending AI feedback with support and guidance from teachers. This review underscores the importance of future research in developing more advanced AI feedback systems capable of addressing higher-order writing skills.

Keywords: AI-driven feedback; grammar checkers; paraphrasing tools; ESL writing; writing proficiency; PRISMA methodology; lexical variation; sentence restructuring; semantic distortions; over-reliance; higher-order thinking skills

INTRODUCTION

The integration of Artificial Intelligence (AI) in education has significantly transformed how writing is taught and learned, particularly for English as a Second Language (ESL) learners. Over the past decade, the rapid advancement of AI technologies has led to the proliferation of digital writing tools that offer real-time feedback, error detection, paraphrasing support, and even predictive text suggestions. Tools such as Grammarly, QuillBot, and Turnitin have emerged as prominent examples of AI-powered platforms that assist learners in developing their writing accuracy, vocabulary usage, and stylistic control (Burstein et al., 2018; Wang et al., 2021). These applications are part of a broader category of automated writing evaluation (AWE) systems that rely on natural language processing (NLP) and machine learning algorithms to evaluate and improve student writing.

Across different regions, these tools are referred to using various terms. In East Asia, for instance, they are commonly known as “intelligent writing assistants,” while in European contexts, they are framed under the broader umbrella of “intelligent tutoring systems” (Kukulska-Hulme, 2020). In Southeast Asia—including Malaysia—learners and educators typically refer to them as “writing AI” or “auto-correct tools,” emphasizing their direct application in correcting surface-level language errors and enhancing clarity in academic tasks. Their growing use reflects a broader trend in education: the shift toward digitalization and the incorporation of AI-based feedback mechanisms into classroom instruction and independent study.

Despite the excitement surrounding these technologies, much of the existing research in this area is descriptive and lacks critical depth. Many studies and reports function more like policy briefs or white papers, emphasizing the promise and opportunity of AI in developing countries, rather than engaging with the potential challenges, failures, or unintended consequences of widespread adoption. For instance, ethical concerns surrounding data privacy, questions of digital inequality, the possibility of reinforcing linguistic bias, and the long-term effects of over-reliance on automated corrections are often under-examined. There is a notable absence of empirical fieldwork—including classroom observations, interviews with learners and teachers, or longitudinal case studies—that would help ground claims about the effectiveness and limitations of these tools.

Moreover, while the benefits of AI in education are often celebrated, little research has explored the tensions that arise between local data governance and the use of global AI platforms, particularly in under-resourced educational settings. As AI technologies become more integrated into curricula worldwide, developing nations face the risk of becoming passive consumers of digital solutions designed without considering local contexts, linguistic diversity, and pedagogical traditions. These issues highlight the urgent need for localized, contextualized, and ethically grounded research that evaluates not only what AI tools can do, but also how they are used, perceived, and challenged in real educational settings.

In the context of second language writing, the role of AI-powered tools is particularly significant. ESL learners often face challenges related to grammar, sentence structure, coherence, and vocabulary development—areas that AI tools claim to address. However, concerns remain about whether these tools truly enhance learners’ writing competence or simply offer surface-level fixes that mask deeper issues in writing development. Furthermore, the degree to which learners understand and engage with AI-generated feedback remains unclear. Some may benefit from instant correction and guided rewording, while others may misinterpret or overly depend on the tools, thus limiting their ability to develop independent writing strategies.

There is also growing concern among educators about how AI feedback aligns—or conflicts—with human instruction. Discrepancies between teacher feedback and AI suggestions can confuse learners, particularly in contexts where digital literacy levels vary. These realities underscore the importance of exploring how ESL learners interact with AI tools, how they make sense of feedback, and what pedagogical strategies can support more effective and ethical use of these technologies.

Against this backdrop, this study sets out to systematically review the current body of research on AI-powered writing tools in ESL contexts. By synthesizing findings from a wide range of empirical and theoretical studies, the review aims to evaluate how grammar checkers and paraphrasing tools influence learners’ writing skills, identify the common challenges faced by students and educators, and explore implications for future teaching practice.

This study is guided by the following research questions:

RQ1: What are the common types of automated feedback provided by AI-driven grammar checkers and paraphrasing tools in ESL writing?

RQ2: How do ESL learners utilize AI-generated feedback, and what challenges do they face in utilizing it effectively?

RQ3: What are the teaching implications of incorporating AI-powered feedback tools into ESL writing instruction?

To address these questions, this review adopts a systematic approach grounded in the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework. An extensive search was conducted across reputable academic databases, including Web of Science, Scopus, and ERIC, covering studies published between 2006 and 2025. Initial screening identified 107 relevant articles, out of which 25 studies were selected for in-depth analysis based on predetermined inclusion and exclusion criteria. These criteria prioritized studies that focused specifically on the use of AI-generated feedback tools—such as grammar checkers and paraphrasing software—within the context of ESL writing instruction at various educational levels. The selected studies offer a diverse range of insights into learners’ experiences, tool effectiveness, feedback types, and pedagogical implications across different countries and proficiency levels. Through this systematic synthesis, the review aims to provide a more balanced, comprehensive, and critically informed understanding of how AI writing tools influence ESL learners’ writing development, especially in light of both their pedagogical promise and potential pitfalls.

Trends in Automated Feedback Systems for ESL Writing

AI-powered tools such as Grammarly, QuillBot, and Turnitin offer features like grammar correction, paraphrasing assistance, and plagiarism detection, providing immediate feedback (Shadiev & Feng, 2023). These automated writing evaluation (AWE) tools have proven useful in reducing teachers’ workloads and promoting learner autonomy (Ranalli, Link, & Chukharev-Hudilainen, 2017). Research has shown that AI feedback can help improve students’ grammatical accuracy, vocabulary range, and sentence complexity (Ferris, 2018; Zahra & Saman, 2023). However, their usefulness in developing higher-order writing skills like coherence, argument structure, and critical thinking remains limited (Warschauer & Ware, 2006; Biber, Nekrasova, & Horn, 2011; Bailey & Lee, 2020).

The Role of AI-Driven Grammar Checkers in ESL Writing

Grammar checkers offer real-time, surface-level feedback on errors related to verb tense, subject-verb agreement, and prepositions (Lee, 2022). These tools help students become more aware of grammatical conventions and gradually improve their accuracy (Alharbi, 2023; Soegiyarto et al., 2022). However, their focus on local errors often overlooks discourse-level concerns, such as logical flow, cohesion, and argumentative clarity (Ferris & Roberts, 2001; Weigle, 2013). Moreover, excessive reliance on grammar checkers may hinder learners from developing their own editing strategies and understanding of grammar rules (Zhang & Hyland, 2022).

The Double-Edged Role of Paraphrasing Tools

Paraphrasing tools like QuillBot and Spinbot have become popular for their ability to improve lexical variety and fluency, and to help avoid plagiarism (Reguig & Mouffok, 2023). While these tools allow learners to restructure sentences and experiment with vocabulary, their capacity to preserve intended meaning is inconsistent (Raheem et al., 2023). In some cases, paraphrased output can distort meaning or reduce coherence. Furthermore, ethical concerns arise when learners overuse paraphrasing tools, potentially bypassing the cognitive effort involved in writing (Hyland, 2022; Fan & Xu, 2020).

Pedagogical Challenges and Considerations

In many low- and middle-income ESL contexts, AI tools are deployed to compensate for limited teacher feedback in overcrowded classrooms (Jou et al., 2023). However, their effectiveness varies depending on learners’ digital literacy levels and their ability to interpret feedback critically (Zhang & Hyland, 2022). Learners with low proficiency may accept AI suggestions passively, without understanding the rationale behind them, while advanced learners may use feedback more strategically. This raises the importance of scaffolding and guided use of AI tools in educational settings (Siemens et al., 2021).

Additionally, the inconsistency of AI-generated feedback across different platforms poses a problem. For example, the same sentence may be corrected differently depending on the tool, undermining the reliability of the feedback (Panadero et al., 2023). Furthermore, existing tools often lack the ability to tailor feedback to an individual’s proficiency level, cultural background, or learning goals (Wondim et al., 2024). Future AI systems should aim for adaptive, learner-centered feedback models (Cheng, 2017).

Limitations in Existing Research

Although there is increasing interest in AI-assisted ESL writing, most studies remain tool-specific and context-limited. There is little comparative analysis of grammar checkers versus paraphrasing tools, and few longitudinal studies examining their long-term impact on writing development. Furthermore, little attention has been paid to localized challenges such as digital infrastructure limitations, privacy concerns, and language-specific issues in developing countries.The literature also rarely addresses the unintended consequences of AI adoption—such as the erosion of students’ critical thinking and writing independence. Such gaps point to the need for more balanced and empirical research that includes real-world observations, fieldwork, and the voices of teachers and students.

Future Directions: A Balanced and Inclusive Approach

For developing nations, artificial intelligence holds immense transformational potential, but realizing this promise demands intentional and collaborative strategies. Governments, educators, developers, and international agencies must work together to build AI ecosystems that meet specific educational, ethical, and infrastructural needs. This includes investing in digital literacy, creating clear policy frameworks, and developing tools tailored to local linguistic and cultural contexts.Equitable implementation depends on prioritizing localization, contextual understanding, and active stakeholder involvement. Addressing the risks, limitations, and failures of AI is essential for responsible integration and should be seen as a foundation for sustainable development rather than a barrier.In ESL education, effectively leveraging AI requires a balanced approach that blends innovation with reflection. Strategies must embrace AI’s opportunities while remaining mindful of ethical concerns, educational inequalities, and the importance of human guidance. Through this approach, developing nations can use AI to enhance language learning and build more inclusive educational systems.

METHODOLOGY

This review follows the PRISMA guidelines, which outline four key steps: identification, screening, eligibility, and inclusion, as shown in Figure 1. PRISMA is a widely accepted method known for its clear and organized approach, making it suitable for research across many disciplines, including studies on AI-powered feedback in ESL writing.

The purpose of this review is to examine existing research on the effectiveness, challenges, and educational value of automated feedback tools in ESL writing instruction.By applying strict inclusion and exclusion criteria, this review ensures the selection of high-quality studies and aims to offer deeper insights into how AI tools support writing development in language learning.

Figure 1. PRISMA Flow Diagram for Systematic Review Adapted from Page et al. (2021)

Identification

As part of the initial phase of the review process guided by the PRISMA framework, three academic databases were chosen to source relevant studies for this research which includes Web of Science (WoS), Scopus, and the Educational Resources Information Centre (ERIC). These databases were chosen for their high-quality indexing of educational and technological research. The key search terms were designed to reflect the study’s core focus: AI-powered tools and ESL (English as a Second Language) writing. Table 1 below provides the search strings used in each database.

Table 1. Search string used in this study.

*: Search String.

Screening

After identifying the initial set of studies, a screening process was carried out to narrow down the selection and ensure that only relevant research was included in the review. The first task in this phase was to eliminate duplicate entries. Since the literature search involved multiple databases, some articles appeared more than once. A total of 15 duplicates were identified and removed, bringing the number of unique articles to 92.

The remaining studies were then screened by reviewing their titles, abstracts, and keywords. At this stage, the main inclusion criterion was whether the article focused on AI-based writing tools in the context of English as a Second Language instruction. Articles that did not meet this focus were excluded. In total, 42 studies were removed during this round of screening. Many of them addressed general applications of AI in education or language learning but were not specifically related to ESL writing or AI writing tools.

Following this initial screening, 50 full-text articles were selected for a more detailed evaluation. These studies were reviewed closely based on their scope, research methodology, and relevance to the topic. To maintain academic quality, 25 articles were excluded because they did not clearly address AI writing tools, lacked a direct link to ESL writing, or had weak research designs such as limited empirical evidence or unclear methodology. Most of the excluded works were conceptual papers, opinion pieces, or studies on broader educational technology that did not specifically align with the goals of this review. The criteria used for inclusion and exclusion during this stage are summarized in Table 2 below.

Table 2. Inclusion and Exclusion Criteria for Screening

As a result of this thorough screening, 25 studies were identified as appropriate for inclusion in the systematic review. These studies were chosen due to their solid research design, clear emphasis on AI-supported tools, and direct relevance to ESL writing education.

Included

After a thorough screening process, 25 studies were identified as suitable for inclusion in this systematic review. These studies were selected based on their methodological rigor, relevance to AI-powered tools, and their contribution to understanding the role of automated feedback in ESL writing. The final selection included articles from three major academic databases: Web of Science (WoS), Scopus, and the Educational Resources Information Centre (ERIC), ensuring a balanced representation of perspectives in the field.

The distribution of studies across these databases is as follows:

Web of Science (WoS): 10 studies

Scopus: 9 studies

ERIC: 6 studies

The studies retrieved from Web of Science mainly examined how AI-based tools like Grammarly, QuillBot, and ChatGPT support ESL learners in enhancing their writing skills. These works investigated aspects such as the accuracy of error correction, improvements in vocabulary use, and the complexity of sentence structures in student writing.

Research sourced from Scopus focused more on how students interact with AI-generated feedback and the teaching implications of using automated writing evaluation tools in ESL settings. Several of these studies also pointed out the shortcomings of AI tools in supporting advanced writing elements, including logical flow, argument structure, and content organization.

Articles from the ERIC database centered on both teacher and student perspectives regarding AI-supported feedback. They emphasized the crucial role of educators in helping learners understand and apply AI suggestions effectively. These findings highlighted the value of blending AI-generated feedback with teacher support to better assist students in their writing development.

The selected studies are summarized in Table 3, which outlines the study database, aim, sample population, and key findings as below.

Table 3. Summary of the selected studies

These findings provide a comprehensive overview of how AI-powered tools are shaping ESL writing development, highlighting both their benefits and challenges. The following sections will further analyze these insights and discuss their pedagogical implications.

Data Analysis Procedure

All selected articles were exported to Mendeley, a reference management software, for systematic organization. A thematic analysis was conducted to identify key themes aligned with the research questions:

What are the common types of automated feedback provided by AI-driven grammar checkers and paraphrasing tools in ESL writing?

How do ESL learners utilize AI-generated feedback, and what challenges do they face in utilizing it effectively?

What are the teaching implications of incorporating AI-powered feedback tools into ESL writing instruction?

This review analyzed the articles interpretively, categorizing them according to themes relevant to AI-driven feedback in ESL writing. For the first research question, automated feedback types were classified based on the functionalities of AI-driven grammar checkers and paraphrasing tools, such as error correction, sentence restructuring, lexical enhancement, and coherence improvement.

For the second research question, learner engagement and challenges were categorized based on student interactions with AI-generated feedback, covering aspects such as revision behavior, digital literacy, over-reliance, and misinterpretation of feedback. For the third research question, pedagogical implications were examined by analyzing how AI-generated feedback aligns with existing writing instruction methods, including teacher intervention, student autonomy, and curriculum integration. The findings from the articles are systematically discussed in the following section.

RESULTS

RQ 1: What Are the Types of Automated Feedback Provided by AI-Driven Grammar Checkers and Paraphrasing Tools in ESL Writing?

In this systematic review, AI-driven feedback is categorized into (1) grammar correction feedback, (2) paraphrasing and rewording assistance, (3) spelling and punctuation correction, (4) style and clarity enhancement, (5) plagiarism detection and citation suggestions, (6) vocabulary enhancement, (7) sentence structure improvement, (8) coherence and cohesion evaluation, and (9) engagement and readability analysis. These themes were identified through an in-depth review of the literature and organized to provide clearer insights into the role of AI tools in enhancing ESL writing skills. Table 4 below presents the categorization of AI-driven feedback types and the respective studies analyzed in this review.

Table 4. Types of AI-Driven Feedback in ESL Writing

As depicted in Table 4, multiple studies investigated AI-driven grammar correction tools. Alharbi (2023) and Raheem et al. (2023) found that Grammarly improved ESL learners’ grammatical accuracy, while Soegiyarto et al. (2022) highlighted its limitations in explaining complex grammatical errors. Similarly, Mahapatra (2024) and John & Woll (2020) examined the role of spelling and punctuation correction, reporting that AI-based feedback effectively minimized errors but sometimes misinterpreted context-specific spelling variations.

Another major category of AI feedback involves paraphrasing and rewording assistance. Studies by Chui (2022) and Reguig & Mouffok (2023) explored how QuillBot and Spinbot facilitated lexical variety in student writing. However, these tools sometimes altered the intended meaning of a sentence, raising concerns about over-reliance and academic integrity.

AI-driven tools also assist with style and clarity enhancement. According to Zhang & Hyland (2022) and Fan & Xu (2020), AI writing assistants like Hemingway Editor and Grammarly help ESL students refine sentence structure and improve writing fluency. However, these tools occasionally oversimplify complex sentence constructions, affecting the intended tone and depth of argumentation.

Plagiarism detection is another critical function provided by AI writing assistants. Shi & Aryadoust (2024) and Panadero et al. (2023) reviewed Turnitin’s role in AI-driven feedback, noting that while it effectively identified direct plagiarism, it sometimes flagged properly paraphrased content as unoriginal.

Furthermore, studies on vocabulary enhancement by Nassaji (2016) and Zahra & Saman (2023) found that AI-powered suggestions improved lexical diversity, but ESL students often misapplied suggested synonyms, leading to unintended shifts in meaning synonyms, leading to unintended shifts in meaning. Similarly, Bailey & Lee (2020) and Cheng (2017) analyzed AI-driven sentence restructuring, concluding that while tools like Grammarly and ChatGPT improved sentence flow, they occasionally produced awkward phrasing.

Finally, research on engagement and readability analysis found that AI feedback provided useful insights into sentence complexity and reader engagement. Warschauer & Ware (2006) and Wondim et al. (2024) concluded that readability scores offered valuable guidance for ESL students but recommended instructor support to grasp and utilize the feedback meaningfully.

These findings suggest that while AI-driven grammar checkers and paraphrasing tools provide valuable automated feedback, their effectiveness depends on learner engagement, digital literacy, and proper pedagogical integration. The following sections examine how ESL students engage with these tools and consider the teaching implications of integrating them into writing instruction.

RQ2: How do ESL learners utilize AI-generated feedback, and what challenges do they face in utilizing it effectively?

The engagement of ESL learners with AI-generated feedback varies depending on multiple factors, including digital literacy, prior writing experience, and their ability to critically assess and apply the feedback provided. Studies such as Warschauer & Ware (2006) and Fan & Xu (2020) indicate that while many students find AI-driven tools beneficial, they often struggle with interpreting nuanced feedback correctly. AI feedback provides direct error correction but may lack the necessary explanations to help learners understand why a correction was made, leading understand why a correction was made, leading to potential misinterpretations or passive reliance on AI suggestions.

Some ESL learners, particularly those at lower proficiency levels, tend to accept AI-generated corrections without critically analyzing them. Bailey & Lee (2020) and Cheng (2017) found that learners with limited linguistic awareness often failed to distinguish between appropriate and inappropriate AI recommendations, resulting in awkward sentence structures or incorrect word choices. This concern points to the importance of providing clear instruction on how to interpret and apply AI-generated feedback effectively.

Another key challenge relates to the adaptability of AI tools to individual writing needs. John & Woll (2020) and Mahapatra (2024) observed that AI tools tend to provide standardized feedback that does not always account for contextual or disciplinary-specific writing conventions. For example, while Grammarly and QuillBot effectively enhance general writing clarity, they may not be suited to domain-specific writing styles, such as academic writing or technical reports.

Moreover, several studies, including those by Shi and Aryadoust (2024) and Reguig and Mouffok (2023), have raised concerns about students becoming overly dependent on AI tools. Instead of actively revising and editing their own work, some learners rely too much on automated suggestions. This kind of reliance can slow the development of independent writing skills and critical thinking, which are important for success in both academic and professional settings.

Table 5. Language Skills Focused on AI-Driven Feedback for ESL Writing

All things considered, while AI-generated feedback offers significant benefits for improving writing fluency and grammatical accuracy, it should be thoughtfully integrated into ESL instruction to ensure that students use it as a supportive tool rather than a replacement for developing critical writing skills. The next section explores the pedagogical implications of using AI-based feedback systems in ESL writing instruction.

RQ 3: What are the teaching implications of incorporating AI-powered feedback tools into ESL writing instruction?

The third research question explores the different fields of study where AI-driven feedback has been applied in ESL writing. Identifying the relevant fields of study is crucial to understanding how automated feedback tools address writing challenges in specific academic and professional contexts. The categorization of fields follows established academic disciplines. Based on the findings, AI-powered writing tools are most commonly used in higher education settings, particularly in social sciences and business-related disciplines. A more detailed representation of these fields is provided in Table 6.

Table 6. Fields of Study Focused on in AI-Driven Automated Feedback for ESL Writing

The results of this review reveal that a significant portion of existing research on AI-generated feedback in ESL writing is situated within the broader field of the social sciences, particularly in areas such as business studies, management education, and language instruction. In these contexts, AI-powered tools like Grammarly, QuillBot, and ChatGPT are frequently studied for their role in enhancing academic writing quality and professional communication. For instance, Panadero et al. (2023), Zhang and Hyland (2022), and Alharbi (2023) have explored the integration of grammar checkers and paraphrasing systems in both classroom and workplace settings. Their findings suggest that these tools can support students in improving linguistic accuracy, refining sentence structure, and increasing lexical diversity, thereby contributing to clearer and more effective writing in academic and business-related tasks. These studies also highlight the potential of AI tools to encourage more independent learning, as students engage more actively with revision processes outside of instructor-led feedback sessions.

In parallel, there has been growing interest in the use of AI-generated feedback within the fields of engineering and technology. This trend reflects the increasing emphasis on written communication skills in technical disciplines, where precise and structured writing is essential. Studies by Cheng (2017) and Mahapatra (2024) have examined how engineering and computer science students utilise AI tools to improve their technical reports, coding documentation, and project proposals. Their research points to notable gains in grammatical correctness and syntactic complexity, as well as increased student confidence in managing discipline-specific terminology and formats. However, these studies also caution that while AI feedback is helpful for surface-level corrections, its effectiveness in guiding the development of more complex technical arguments remains limited without instructor input.

In the health sciences, the application of AI in writing instruction is gaining momentum, particularly in relation to clinical and academic writing tasks. Studies conducted by Shi and Aryadoust (2024) and Soegiyarto et al. (2022) underscore the benefits of automated feedback for improving accuracy in medical documentation, including patient case reports, diagnostic summaries, and academic papers in healthcare contexts. Their findings suggest that AI-driven grammar and coherence checkers can assist students in reducing language errors and enhancing clarity, which is crucial in high-stakes, detail-oriented communication environments. However, these studies also raise concerns about over-reliance on automated systems, especially when nuanced medical terminology or context-specific phrasing is involved, indicating the need for complementary instructor oversight. Furthermore, the pedagogical implications of AI-based feedback in higher education and second language acquisition have been explored in research by Bailey and Lee (2020), Moon (2021), and Fathman and Whalley (1990). These studies reflect the growing use of AI-supported writing tools in the humanities and across interdisciplinary academic contexts.

Beyond discipline-specific applications, a number of studies have addressed the broader pedagogical implications of AI feedback tools in higher education and second language acquisition. Research by Bailey and Lee (2020), Moon (2021), and Fathman and Whalley (1990) explores how AI can be incorporated into writing pedagogy to support the development of both linguistic competence and critical thinking. These studies reflect a growing interest in the potential of AI tools to complement traditional feedback mehods, particularly in large classrooms where individualised teacher feedback may be limited. They also note that the effectiveness of AI-assisted writing instruction depends significantly on learners’ digital literacy, their ability to interpret and apply feedback appropriately, and the role of instructors in mediating this process. Collectively, these findings highlight the evolving role of AI in shaping writing instruction across diverse academic disciplines and suggest a need for context-sensitive integration strategies that align with both subject matter and student needs.

DISCUSSION

The findings shed light on the various types of feedback that AI tools such as grammar checkers and paraphrasing systems provide to support ESL writing. Overall, the review suggests that these tools can help learners improve multiple aspects of their writing, including grammatical accuracy, vocabulary use, coherence, and overall clarity. However, despite the benefits of receiving quick and automated responses, the impact of these tools largely depends on how students engage with the feedback and their ability to reflect on and apply the suggestions thoughtfully.

The Role of AI-Driven Feedback in ESL Writing Development

The types of automated feedback offered by AI tools vary, with grammar correction and sentence structure enhancement being the most prominent features. Tools like Grammarly, ProWritingAid, and Microsoft Word Editor provide real-time feedback, allowing learners to identify and correct grammatical, syntactical, and punctuation errors. However, as noted in studies such as Bailey & Lee (2020) and Cheng (2017), while these tools improve grammatical accuracy, they often lack contextual awareness, sometimes misinterpreting errors or oversimplifying sentence structures.Similarly, AI-powered paraphrasing tools like QuillBot and Spinbot assist learners by rewording sentences and improving lexical diversity (Chui, 2022; Reguig & Mouffok, 2023). While these tools help enhance writing fluency, concerns have been raised about their tendency to alter intended meanings and introduce errors due to overgeneralization. This highlights the need for human moderation and pedagogical support when using these AI-driven resources in ESL writing instruction.

ESL Learners’ Engagement with AI Feedback

An important aspect of this study involved examining how ESL students engage with AI-generated feedback and the challenges they face in using it effectively. While AI feedback is useful for addressing surface-level errors, research by Fan and Xu (2020) and Warschauer and Ware (2006) indicates that it lacks the depth required to support higher-order writing skills such as argumentation, coherence, and critical thinking. Without fully understanding the reasoning behind AI-generated corrections, many ESL learners tend to accept suggestions passively, which can lead to over-reliance on these tools and hinder the development of independent editing skills (John and Woll, 2020; Mahapatra, 2024). Moreover, students’ ability to interpret and apply AI feedback is closely linked to their level of digital literacy. Learners with limited technical skills may struggle to identify whether AI suggestions are appropriate or not, while those with stronger digital proficiency are more likely to engage critically with automated recommendations technical skills may struggle to identify whether AI suggestions are appropriate or not, while those with stronger digital proficiency are more likely to engage critically with automated recommendations (Zhang and Hyland, 2022; Shi and Aryadoust, 2024). These findings underscore the importance of integrating AI tools within structured ESL instruction, ensuring that students receive proper guidance on how to engage with automated feedback thoughtfully rather than relying on it uncritically.Bottom of Form

AI Feedback Across Different Academic Fields

The study also explored the use of AI-based feedback tools for ESL writing across various academic disciplines. The most frequent applications of AI writing support are found in the social sciences, particularly in areas related to business and education. This aligns with findings from Alharbi (2023) and Raheem et al. (2023), who highlight the growing use of AI grammar checkers and paraphrasing tools in academic writing and professional communication. In contrast, disciplines such as computer science and engineering have also adopted AI-powered tools to strengthen technical writing skills (Mahapatra, 2024; Cheng, 2017). While AI feedback has proven useful for improving sentence clarity and refining terminology, studies suggest that domain-specific writing—such as legal and medical communication—requires more tailored AI support to meet the unique linguistic demands of those fields (Zahra and Saman, 2023; Reguig and Mouffok, 2023).

LIMITATIONS AND FUTURE DIRECTIONS

One notable limitation of this review lies in its concentration on research conducted within higher education contexts, as the majority of existing studies examining AI-driven feedback tools tend to focus on university-level ESL learners. This focus reflects the current research trend, where tertiary institutions have greater access to digital resources and are more likely to integrate advanced educational technologies into language instruction. However, as pointed out by Soegiyarto et al. (2022) and Wondim et al. (2024), there remains a significant gap in the literature regarding the use and effectiveness of AI-based feedback tools in secondary education settings and among learners with limited English proficiency. These groups may encounter different challenges when engaging with AI feedback, including limited digital literacy, reduced familiarity with academic writing conventions, and a lack of support in interpreting automated suggestions. Therefore, future research should aim to explore how AI-powered writing tools can be adapted to meet the diverse needs of younger or less proficient ESL learners, and how these tools can be meaningfully embedded in school-level language curricula.

In addition to the limited scope of existing studies, another challenge is the current inability of AI tools to replicate the depth, contextual sensitivity, and pedagogical judgment that human instructors provide. While automated systems offer prompt feedback on grammar, vocabulary, and sentence structure, they often fall short in areas such as tone, nuance, argument development, and genre-specific conventions (Hyland, 2022; Ferris and Roberts, 2001). This limitation raises concerns about over-reliance on AI, especially if learners begin to trust automated suggestions without critically evaluating their accuracy or appropriateness. As such, it becomes crucial to implement a blended learning approach, where AI tools serve as a supplementary resource rather than a standalone solution. By combining AI-generated feedback with teacher-led instruction, learners can benefit from both the efficiency of technology and the depth of personalised guidance.

Furthermore, future studies should investigate how AI feedback systems can be personalised to support individual learner needs more effectively. Factors such as writing proficiency, learning style, motivation, and digital competence all influence how students engage with feedback and how they apply it to improve their writing. Designing adaptive AI systems that can adjust the level, tone, and focus of feedback based on learner profiles may significantly enhance the usefulness of these tools. Research in this area could contribute to the development of more inclusive and learner-centred AI writing support, ultimately leading to better learning outcomes and sustained improvements in writing skills over time.

CONCLUSION

This systematic review examined the role of AI-powered automated feedback tools in supporting ESL writing. It focused specifically on grammar checkers and paraphrasing applications and how

they influence learners’ writing development. Drawing upon 25 selected studies from the Web of Science, Scopus, and ERIC databases, the review addresses a gap in understanding the educational benefits and limitations of AI-assisted writing tools for English language learners. The analysis showed that these tools are mostly effective in improving surface-level writing aspects such as grammar, spelling, punctuation, and vocabulary. Commonly used platforms like Grammarly and ProWritingAid were frequently reported to help students enhance these areas.

However, their ability to support more complex writing skills, such as logical organization, argument development, and rhetorical effectiveness, remains limited. This finding highlights the continued need for more context-aware AI systems that can deliver meaningful and nuanced feedback to promote deeper learning.

The extent to which students benefit from AI-generated feedback depends heavily on factors such as digital literacy, language proficiency, and reflective thinking. Although AI tools often encourage learners to revise their work more independently, many students tend to accept the suggestions passively without critical analysis. This behavior may hinder the development of autonomous writing skills. Therefore, it is important to incorporate digital literacy training and teacher involvement to guide students in using AI feedback thoughtfully and effectively.

Regarding disciplinary use, AI-based writing support is most commonly found in the social sciences, engineering, and computer science. While these tools provide general assistance, their effectiveness is reduced in fields that require discipline-specific language use and writing conventions. This limitation points to the need for more adaptable AI systems that can meet the writing demands of different academic and professional contexts.

One limitation of this review is that it relies entirely on secondary sources. The absence of original research methods such as interviews, classroom observations, or learner reflections restricts the depth of analysis. Additionally, most of the selected studies are focused on university settings, with little representation of school-age learners, adults in non-academic settings, or individuals with lower English proficiency. These gaps suggest a need for future research that includes primary data collection and diverse learner contexts.

Moreover, although the review highlights the benefits of AI tools, it does not extensively address the risks and unintended consequences associated with their use. Issues such as academic integrity, algorithmic bias, over-reliance, and the lack of accountability in AI feedback remain underexplored. Future studies should include critical evaluations of these concerns and assess how AI tools interact with teacher feedback and peer review processes to form a more holistic and balanced approach to writing instruction.

For AI tools to reach their full potential in developing countries, there must be intentional efforts to address barriers related to infrastructure, access, ethics, and contextual relevance. The design and implementation of AI in education should involve collaboration among educators, developers, learners, and policymakers to ensure that these technologies align with local needs and values. Through such collaborative and inclusive efforts, AI can become a meaningful contributor to sustainable language education and support equitable learning opportunities across diverse contexts.

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