Exploring the Role of AI-Driven Dynamic Writing Platforms in Improving EFL Learners' Writing Skills and Fostering Their Motivation

Exploring the Role of AI-Driven Dynamic Writing Platforms in Improving EFL Learners' Writing Skills and Fostering Their Motivation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Exploring the Role of AI-Driven Dynamic Writing Platforms in Improving EFL Learners' Writing Skills and Fostering Their Motivation Aliakbar Tajik This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5788599/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract AI-powered platforms for dynamic writing present transformative opportunities to enhance English language acquisition and actively engage learners in writing tasks. However, the role of artificial intelligence in improving writing proficiency and fostering motivation among English as a Foreign Language (EFL) learners remains an area that has yet to be thoroughly investigated. This research endeavor seeks to address the prevailing knowledge gap in the field by exploring the influence of AI-powered writing platforms on the writing skills of EFL learners. The study's objectives encompass the assessment of the impact of these platforms on coherence, vocabulary usage, grammatical accuracy, task accomplishment, and learner motivation. Utilizing a mixed-methods approach, the study examined 65 intermediate EFL students from the Islamic Azad University, Varamin-Pishva branch, who were divided into two groups: one employed AI-powered tools, while the other followed traditional classroom-based writing exercises. Quantitative data were gathered via IELTS-based assessments and motivation scales, while qualitative insights were derived from semi-structured interviews. The findings revealed substantial enhancements in the AI group across all measured dimensions in comparison to the control group. Additionally, the AI group demonstrated a significant surge in motivation levels. Learners in the AI group reported positive attitudes toward AI-based instruction, citing improvements in engagement, autonomy, and confidence in their writing. The platform also fostered greater self-regulation and personalized learning experiences, which participants found effective and enjoyable. The findings emphasize the efficacy of AI-based writing platforms in enhancing linguistic proficiency and motivational levels among EFL learners. The study provides practical insights for incorporating AI technologies into writing instruction, encouraging educators to leverage such tools for more effective and engaging language learning practices. Special Education Artificial Intelligence and Machine Learning AI-Driven Writing Writing skills Motivation to Write EFL Learners Mixed-Methods Study Technology-Enhanced Language Teaching 1. Introduction The proliferation of artificial intelligence (AI) tools in the domain of foreign language education has precipitated a paradigm shift in the methodology of writing skill instruction and acquisition. AI-driven interactive writing platforms are transforming the landscape of foreign language education by offering innovative tools to enhance learners' writing skills and autonomy (Zhao, 2022 ). These platforms are equipped with features such as grammar checkers, real-time writing analysis, and automated feedback systems, which are designed to support students in improving various aspects of their writing, including syntax, vocabulary, grammar, and content (Jeanjaroonsri, 2023 ). Leveraging advanced machine-learning algorithms, these platforms provide customized feedback by comparing user-generated text against extensive databases of exemplary and flawed writing samples (Jeanjaroonsri, 2023 ). Real-time feedback enables learners to identify and rectify errors in an instant, thereby cultivating a more profound comprehension of the fundamental principles that underpin effective writing. AI-driven platforms foster self-directed learning by enabling students to actively engage with their writing tasks and refine their skills independently (Wang et al., 2024 ). These platforms are particularly beneficial for foreign language learners, as they are designed to address the unique challenges posed by limited language proficiency. These tools offer user-friendly interfaces and dynamic feedback mechanisms, empowering learners to enhance their writing abilities while building confidence and motivation (Wang, 2022 ). A multitude of studies have indicated that these technologies are effecting a transformation in writing instruction and learning, thereby paving the way for more effective, interactive, and tailored educational experiences. In recent years, there has been a growing interest in the influence of AI-based writing platforms on learners' writing skills, particularly in the TEFL context. While some research highlights the benefits of these tools in improving students' writing skills (Fitriani, 2024 ; Wang, 2022 ; Zhao, 2023), other studies raise concerns about their potential limitations (e.g., Liu et al., 2021 ; Lund et al., 2023 ; Qadir, 2022 ). Nevertheless, much of the existing literature has focused on improving grammar and syntax, often neglecting other essential aspects of effective writing. Online platforms provide EFL learners with the means to overcome the limitations of the traditional classroom setting, including time limitations, large class sizes, and inadequate individualized attention. These platforms facilitate the provision of customized feedback, foster meaningful communication outside the classroom, and promote continuous enhancement of writing skills through interactive activities. Research indicates that such tools also positively influence learners' willingness to engage in writing tasks, fostering confidence and motivation to communicate their ideas effectively (Jeon, 2022 ; Pentina et al., 2023 ). This willingness, often shaped by contextual factors, plays a critical role in learners' ability to express themselves creatively and confidently (Ayedoun et al., 2015 ). Recent advancements in artificial intelligence (AI) technology have led to substantial expansions in the scope of research in the domain of language learning, as evidenced by a substantial body of research (see Araujo & Bol, 2024 ; Zhou et al., 2023 ). Among these innovations, AI-driven writing platforms and chatbot-mediated instruction have emerged as transformative tools for language education. Specifically, chatbots, defined as AI programs designed for real-time interaction, simulate authentic communication scenarios, enabling learners to practice writing skills dynamically and engagingly (Henkel et al., 2020 ). The incorporation of naturalness and the alignment of content with learners' contextual needs has been demonstrated to significantly enhance the appeal of writing tasks for EFL students. This pedagogical approach fosters a flexible learning environment, enabling learners to enhance their writing skills unencumbered by constraints related to time and location (Fathi & Rahimi, 2022; Hsu, 2016; Wu et al., 2017 ). In the context of English as a foreign language (EFL), the utilisation of chatbots can facilitate a range of opportunities for interaction, including the cultivation of conversational proficiency, the refinement of grammatical competence, and the augmentation of vocabulary (Kim et al., 2020 ). Furthermore, they function as a flexible resource for writing-focused tasks, enabling learners to draft texts, seek clarifications, and receive instant feedback generated by AI (Walker & White, 2013 ; Hsu et al., 2021 ; Jeon, 2021 , 2022 ). While preliminary studies have underscored the potential of AI-driven chatbots in language learning (Yang et al., 2022 ), further exploration is necessary to gain a more profound understanding of their broader effects, particularly in enhancing writing abilities and promoting sustained engagement in writing activities (Yanguas, 2010 ). A growing body of research has emerged that underscores the merits of chatbots in facilitating language acquisition (Kim, 2017; Timpe-Laughlin et al., 2020 ; Yang et al., 2022 ). However, the precise function of these bots in enhancing writing abilities, particularly in domains such as coherence, organisation, and lexical diversity, remains to be comprehensively investigated (Underwood, 2017 ; Kessler, 2018 ). This lacuna underscores the urgent necessity for additional research to examine how chatbot interactions can foster greater fluency and precision in writing, as well as the motivational factors that perpetuate learners' engagement with writing tasks. This research investigates the role of AI-powered chatbots in fostering an interactive and simulated environment that supports EFL learners in enhancing their writing skills. The investigation focuses on the impact of structured grammar exercises, vocabulary development, and interactive prompts on learners' abilities to organize ideas, sustain fluency, and produce coherent written texts. Furthermore, the study examines learners' attitudes and perceptions regarding the utilization of chatbot-assisted tools for writing tasks. The qualitative analysis illuminates the advantages and disadvantages of AI-driven platforms, highlighting their potential to create an engaging and supportive environment that motivates learners. By exploring the intersection of writing skills and learner motivation in chatbot-mediated environments, this study contributes to the growing body of literature on the integration of artificial intelligence (AI) in language education. Practical insights are provided for educators and researchers, advocating for the design of learner-centered writing activities that leverage chatbot technology to align with educational goals and address individual learner needs. 2. Literature review 2.1 . The Role of Technology in Enhancing Writing Skills and Engagement Advancements in technology have significantly influenced the methods used to teach writing in the digital era. Haleem et al. (2022) highlight that incorporating digital tools into educational settings has revolutionized traditional approaches to writing instruction, introducing more dynamic and interactive methodologies. Garlinska et al. (2023) further emphasize that tools like virtual classrooms, online workshops, and cloud-based writing platforms have initiated a substantial shift in teaching practices. These platforms provide functionalities such as instant feedback, collaborative editing, and plagiarism detection. As noted by Nykyporets (2023), these features not only improve students' writing skills but also encourage the development of critical thinking and independent problem-solving skills. Technology has been shown to have the potential to enrich the writing process by integrating multimodal approaches and promoting digital literacy. It has been demonstrated to facilitate cognitive growth, encourage independent thinking, and support the adoption of effective learning (Chauhan et al., 2023). Digital tools can address the unique needs of struggling writers by leveraging their existing knowledge and aiding in the cognitive processes involved in writing. This is particularly true when employing knowledge transformation strategies that align with writing goals such as topics, genres, and text structure (Rad et al., 2023; Hsu et al., 2023). In educational settings, there is an increasing interest in the influence of technology integration into writing instruction on outcomes (Ahmed et al., 2024). Effective technology integration involves the incorporation of digital resources, including computers, portable devices, online platforms, and applications, into daily classroom practices (Chang et al., 2021). This method is rooted in the Technological Pedagogical Content Knowledge Framework (TPACKF), which underscores the integration of technology, pedagogy, and subject matter expertise to improve students' writing skills, empowering them to craft more comprehensive and grammatically precise essays (Ahmed et al., 2024). An expanding body of research has demonstrated the potential for AI-powered platforms, such as chatbots, to significantly enhance learner motivation (Silitonga et al., 2023) and promote active engagement (Yashima, 2009; Carayannopoulos, 2018). In comparison to conventional classroom teaching methods, the utilization of chatbot-driven interactions has been demonstrated to engender a more personalized and dynamic learning experience, thereby fostering greater engagement in the educational process (Guo et al., 2023; Peng, 2015; Yashima, 2009).This heightened engagement has been identified as being instrumental in improving EFL learners' Willingness to Write (WTW) and fostering autonomy, thereby empowering learners to take responsibility for their progress and to persist in meeting their writing goals. For instance, Lee (2019) emphasized the pivotal role of socio-political factors, including instructional goals, teaching strategies, contextual elements such as community dynamics, and personal attributes such as confidence and anxiety, in shaping engagement in writing in digital spaces outside formal education. In a similar vein, Tai and Chen's (2020) research examined the utilization of Google Assistant among EFL learners, revealing that it substantially boosted their confidence and inclination to write while concurrently mitigating anxiety. The integration of AI into educational settings empowers individuals to effectively engage with computers, robots, machines, and various software tools. A notable innovation in AI is the chatbot, which facilitates endless authentic and natural interactions, serving as a versatile conversational partner to support language acquisition (Huang et al., 2022). These interfaces replicate human-like communication, enabling learners to inquire and receive responses naturally. This capacity is especially beneficial for individuals with limited opportunities to practice language production, as it provides them with access to interactions that resemble those with native English speakers, irrespective of time or location (Walker & White, 2013). AI-driven chatbots present a pragmatic solution to the challenges encountered by EFL learners with restricted access to native or non-native speakers. These chatbots furnish a malleable and attainable platform for language practice, empowering learners to partake in meaningful interactions unencumbered by the constraints of time, location, or the availability of human partners. The seamless and user-friendly design of these chatbots ensures consistent opportunities for learners to enhance their communication skills in diverse contexts.These bots offer numerous advantages in EFL teaching and learning, including conducting conversations via text and audio, generating intelligent responses, engaging actively, providing pronunciation feedback, and fostering understanding and insight (Walker & White, 2013). Consequently, EFL learners possess the potential to achieve improved learning outcomes (Walker & White, 2013; Fryer et al., 2020; Huang et al., 2022), benefit from increased convenience and autonomy in their learning processes (Walker & White, 2013), experience reduced anxiety and elevated comfort (Kim, 2016), enhance their engagement and confidence, and overcome psychological and transactional barriers in foreign language acquisition (Men et al., 2022). AI-powered tools and applications have increasingly revolutionized writing instruction by offering tailored learning experiences. As Chaisiri (2023) emphasize, these platforms empower educators to identify individual learners' strengths and areas requiring improvement, allowing them to adjust instructional strategies for optimized outcomes. In a similar vein, Bhutoria (2022) underscores the significance of such technologies in fostering personalized learning, ensuring that students receive support that aligns with their distinct needs and preferences. Furthermore, mobile technologies empower students to engage in public sharing of their work, promoting confidence and skill development through peer feedback (Umamah & Cahyono, 2022). Cahyono et al. (2023) contend that this collaborative approach fosters a supportive learning environment that enhances both individual and group learning experiences. However, as Duncan and Joyner (2022) caution, the adoption of digital writing platforms is not without challenges. These include issues of digital equity, data security, and potential distractions. These challenges must be carefully managed to maximize the benefits of these platforms. These challenges underscore the necessity of a proactive and reflective approach in the development of pedagogical strategies and policies to address the evolving landscape of writing education in the age of artificial intelligence. 2.2. The Role of AI-Driven Dynamic Writing Platforms in Enhancing EFL Learners’ Writing Skills and Motivation The utilization of AI-powered interactive writing tools in educational settings has garnered considerable attention for their potential to enhance the writing competencies of English as a Foreign Language (EFL) learners (Freiermuth, 2020; Dogan et al., 2023). These platforms have been demonstrated to improve writing accuracy, style, and overall quality, while concurrently exerting a favourable influence on student motivation and performance. Notable examples of such tools include Grammarly, QuillBot, WordTune, and Jenni, which have been shown to refine written work and foster engagement (Dogan et al., 2023; Freiermuth, 2020).Notwithstanding the aforementioned benefits, concerns have been raised about the potential for overdependence on such tools and their possible negative effects on learners' critical thinking skills (Dogan et al., 2023). Nonetheless, the growing adoption of these technologies underscores their transformative potential in EFL writing education. Grammarly, for instance, offers immediate feedback on grammar, punctuation, spelling, clarity, and engagement, thereby transforming the writing process into an interactive learning experience. As Tambunan et al. (2022) have demonstrated, Grammarly has been shown to improve students' grammatical accuracy, punctuation, and sentence structure. The platform's capabilities extend beyond mere error detection, as it provides actionable suggestions aimed at enhancing textual coherence and style. This fosters not only skill development but also confidence in learners. QuillBot, a widely recognized tool for its paraphrasing capabilities, assists students in avoiding plagiarism by preserving the fundamental essence of their content. Kurniati and Fithriani (2021) emphasize the tool's crucial role in improving paraphrasing skills, a fundamental aspect of academic writing.QuillBot's capacity to streamline complex sentences without compromising their contextual relevance renders it a valuable asset for students engaged in academic assignments or research projects. WordTune, an additional artificial intelligence-based tool, emphasizes the refinement of tone and the execution of stylistic adjustments.Lam and Moorhouse (2022) posit that WordTune facilitates self-assessment and reflection by encouraging users to examine and improve their writing weaknesses. A distinguishing feature of WordTune, according to them, is its capacity to go beyond conventional grammar checkers by offering a deeper examination of stylistic elements. This feature empowers students to explore a more expansive range of tones and expressions, thereby enhancing their writing repertoire. AI-powered tools like Grammarly, QuillBot, and Turnitin have demonstrated remarkable potential in fostering academic integrity(Farrokhnia et al., 2023). These platforms not only enhance grammar and writing style but also assist users in paraphrasing content effectively, reducing the risk of plagiarism while preserving the original meaning.However, the extent to which these tools have achieved a complete elimination of plagiarism is contingent on various factors. These tools assist users in paraphrasing text, correcting grammar, and providing feedback, thereby helping to prevent the unauthorized duplication of others' work. Nevertheless, the onus falls on the user to ensure ethical usage of these tools, as their improper or excessive use can potentially lead to plagiarism.By providing predictive suggestions and content ideas, Jenni helps reduce cognitive load and enhance writing efficiency, particularly for novice writer ) Moorhouse (2022) . To eradicate plagiarism in its entirety, the utilization of artificial intelligence (AI) tools should be centered on fostering originality and promoting proper citation practices. When utilized in the appropriate manner, these AI tools can provide English as a Foreign Language (EFL) learners with customized, adaptive assistance for their writing development (Farrokhnia et al., 2023). By addressing common language difficulties and fostering self-guided learning, these tools contribute to enhancing critical writing abilities and fostering long-term motivation in students. However, future studies must examine the long-term impacts of AI tool use on writing independence and critical thinking to ensure a comprehensive approach to technology-assisted learning. OpenAI's GPT-3 signifies a substantial advancement in the domain of language modelling, demonstrating sophisticated capabilities in producing text that emulates human language and in comprehending complex linguistic nuances.Its capacity to produce coherent, contextually relevant content makes it an excellent tool for stimulating creativity and fostering critical thinking among students (Mhlanga, 2023). By encouraging experimentation with diverse writing styles and concepts, GPT-3 can facilitate both creative and academic writing endeavors. The collective impact of these AI tools underscores the transformative capacity of artificial intelligence in enhancing students' writing abilities. 2.3. AI-Driven Platforms and Their Multifaceted Impact on EFL Learners Mhlanga(2023) examined the functionality of ChatGPT as a virtual tutor, with a focus on its capacity to facilitate goal setting and provide interactive guidance. Notwithstanding its advantages, they sounded a note of caution about the potential pitfalls of overreliance, which could result in superficial engagement with learning materials. In a similar vein, Mogavi et al. (2024) underscored the potential benefits of personalized learning offered by AI tools, while concurrently highlighting the associated challenges, including diminished critical thinking and concerns over academic integrity. To address these concerns, Ali et al. (2023) have proposed that learners engage in self-assessment to cultivate independence from technological reliance. In a collaborative study, Wiboolyasarin et al. (2024) demonstrated that artificial intelligence (AI)-assisted corrective feedback significantly augmented second-language (L2) writing competencies among Thai exchange students. The research under discussion highlights the profound impact that such tools can have when applied thoughtfully. In a similar vein, Hsu et al. (2023) observed that ChatGPT led to substantial enhancements in grammar, writing proficiency, and vocabulary acquisition among foreign language learners. Nevertheless, they also cautioned about its possible drawbacks, particularly in terms of its potential to limit creativity and critical thinking skills. Subsequent research by Wei (2023) corroborated the findings, demonstrating that AI-mediated teaching not only enhanced English learners' academic performance but also fortified their motivation and self-regulation skills. Additionally, Karataş et al. (2024b) examined incorporating AI-powered image recognition technology into vocabulary acquisition, reporting reduced anxiety and enhanced knowledge retention, although it did not notably influence self-regulation. Vanichvasin et al. (2021) demonstrated that AI tools play a significant role in enhancing EFL learners' grammar proficiency and overall writing performance. They further highlighted the facilitation of increased student engagement in academic writing tasks by AI feedback tools. Nevertheless, Kim (2016) and Vladova et al., (2023) noted that despite students' favorable views on the utility of AI in grammar correction and plagiarism detection, ethical and creative challenges persist. A study by Xu and Wang (2024c) synthesised data from 40 studies, thereby reinforcing the effectiveness of AI-integrated learning tools in improving English learning outcomes. This finding is consistent with the research conducted by Guo and Wang (2024), which identified that AI-enhanced instruction positively impacts learner engagement across a range of cognitive, emotional, and social dimensions. Furthermore, Mozumder et al. (2023) explored the role of AI tools in fostering intrinsic motivation, noting improvements in learner autonomy and critical thinking. However, they also observed that the impact varied based on factors such as nationality and academic discipline. Collectively, these studies underscore the multifaceted benefits and potential challenges of integrating AI technologies into EFL education. In consideration of the pivotal role of the variables examined, this study endeavors to address the following research inquiries: How do AI-driven dynamic writing platforms contribute to the improvement of EFL learners' writing skills and motivation compared to traditional methods? What are EFL learners' perceptions of the effectiveness of AI-powered writing activities in enhancing their writing skills and fostering motivation? 3. Method 3.1. Research Design The present study adopted a sequential explanatory mixed-methods design, utilising a systematic progression from quantitative to qualitative analysis, with the objective of investigating the research questions. The process commenced with the collection and analysis of quantitative data to evaluate how AI-powered interactive writing platforms influenced EFL learners' writing abilities and motivation. Subsequent to this quantitative phase, semi-structured interviews were conducted with participants to gain deeper insights into the findings and enhance understanding of the phenomenon. The combination of these approaches was deemed essential for a comprehensive examination of the impact of technology on various aspects of writing performance, including coherence, vocabulary, grammatical accuracy, and task achievement. Furthermore, it enabled an exploration of the variations in learner motivation and experiences. The integration of quantitative and qualitative methods was a deliberate strategy, with the aim of providing a comprehensive and nuanced analysis that would underpin the study's research objectives. 3.2. Participants The present study focused on a group of 65 undergraduate students specializing in Teaching English as a Foreign Language (TEFL) at the Islamic Azad University, Varamin-Pishva branch. These participants, enrolled in intermediate-level writing courses, were selected using convenience sampling. The participants' ages ranged from 18 to 25, ensuring a relatively homogenous age group. To qualify for participation, students needed to meet specific criteria, including verification of their intermediate English proficiency through placement tests, an absence of prior experience with AI-assisted language tools, and voluntary submission of informed consent forms. Participants were randomly divided into two groups: 33 students were assigned to the AI-driven interactive writing platform group, while the remaining 32 were placed in the classroom-based writing group. The randomization process was meticulously executed to ensure that the allocation of groups was unbiased and maintained parity between the two cohorts. The demographic variables were well-balanced across the groups. The AI group included 15 males and 18 females, while the traditional group comprised 14 males and 18 females. On average, both groups reported similar educational backgrounds, with participants having received 5 to 7 years of formal English education. This equitable distribution was instrumental in reducing potential confounding factors related to prior academic exposure. The study's rigorous sampling methodology ensured a valid comparison between the two groups, thereby facilitating an effective exploration of the impact of AI-driven interventions on EFL learners' writing skills and motivation within a controlled yet representative educational environment. 3.3. Materials and Instruments 3.3.1. AI-Powered Writing Platform This study employed an artificial intelligence-powered interactive writing platform that has been specifically developed to enhance learners' writing skills and engagement. The platform provided immediate feedback on various writing components, including coherence, vocabulary selection, grammatical correctness, and overall task completion. Furthermore, it offered customized recommendations and corrections, meticulously tailored to address the learners' distinct writing requirements, thus fostering enhanced self-regulation and autonomy in their writing process. 3.3.2. Writing Performance Assessment The assessment of the participants' writing skills entailed the implementation of IELTS-style writing tasks, utilized as both pre-tests and post-tests. These tasks were designed to evaluate key aspects of writing performance, including coherence and cohesion, vocabulary usage, grammatical accuracy, and task completion. The evaluation of these components was guided by the IELTS writing band descriptors. Scores for each component ranged from 1 to 9, with the overall score being the average of the four categories. To ensure the reliability and accuracy of the scoring process, two experienced raters independently evaluated the participants' written work. The inter-rater reliability was subsequently calculated using the Pearson correlation coefficient, yielding a substantial score of 0.87, thereby demonstrating the consistency of the evaluations. 3.3.3. Motivation Scale In this research, a carefully adapted motivation scale was utilized to evaluate EFL learners' motivation to engage in writing activities. The scale, comprising 20 items rated on a 7-point Likert scale (ranging from 1 = strongly disagree to 7 = strongly agree), measured key motivational aspects such as interest, self-confidence, and independence. Administered in English to correspond with the language used in the AI platform, the scale aimed to track shifts in learner motivation throughout the intervention. The scale demonstrated strong reliability, with a Cronbach's alpha of 0.89, indicating robust internal consistency. Despite the limited sample size, which hindered the comprehensive validation of the instruments, the psychometric properties of the scale were substantiated by prior research. A pilot study conducted prior to the main investigation affirmed the appropriateness of these tools for assessing both writing proficiency and motivation. The findings of this preliminary investigation indicated that the instruments consistently yielded meaningful and valid responses, thereby validating their relevance and effectiveness in achieving the objectives of the study. 3.3.4. Semi-Structured Interviews In order to gain deeper insights into the manner in which participants interacted with the AI-based interactive writing platform, semi-structured interviews were conducted with nine members of the AI group (see Appendix A). The aim of these interviews was to explore participants' perceptions regarding the platform's impact on their writing abilities and motivation. The qualitative data gathered through these interviews complemented the quantitative findings, providing a more comprehensive understanding of the results. The interview questions were meticulously crafted to elicit information regarding the underlying factors that contributed to the AI group's superior performance compared to the group using conventional classroom writing techniques. The interview protocol was developed through a comprehensive approach, which included an in-depth review of relevant literature, expert consultations, and a pilot phase to ensure the clarity and appropriateness of the questions. The final set of open-ended questions was designed to explore participants' experiences with the platform, their perceived progress in writing skills, and the impact of the AI tool on their motivation. This qualitative method yielded valuable insights into the key factors that contributed to the AI group's enhanced performance in writing tasks. The interviews were conducted in English to align with the language used in the AI-driven writing tasks, although participants had the option to switch to Persian if they faced challenges expressing themselves in English. This flexibility helped ensure smooth communication, allowing participants to fully share their experiences. In some cases, participants chose to answer specific questions in Persian, highlighting the importance of accommodating diverse linguistic needs. The interviews lasted between 20 to 35 minutes, offering ample time for thoughtful and detailed responses. To maintain confidentiality, participants were given pseudonyms (e.g., P1, P2, …, P9), ensuring privacy while safeguarding the integrity of the data. 3.4. Ethical Considerations The research was conducted in strict accordance with established ethical standards and received formal approval from the Institutional Review Board (IRB) at Islamic Azad University, Varamin-Pishva Branch. This approval ensured the protection of participants' rights and welfare, with various critical ethical safeguards implemented to uphold these principles throughout the study: Informed Consent : The participants were provided with a comprehensive consent form that elucidated the objectives of the research, the methodology employed, and the potential risks and benefits associated with their involvement. Participants were granted sufficient time to meticulously deliberate and arrive at a voluntary decision regarding their involvement. Confidentiality and Data Security : The anonymity of all participants was preserved, and the data was stored in encrypted systems, accessible only to authorized individuals with appropriate clearance. Transparency and Participant Awareness :The participants were provided with exhaustive information regarding the potential benefits of the study, including the opportunity to enhance their writing skills and motivation, as well as other available learning resources. To address the ethical concerns associated with the comparative experimental design, which had the potential to result in unequal experiences for the AI and traditional instruction groups, the following steps were taken: Balanced Learning Opportunities: Both groups participated in the same duration of instructional sessions and were given access to a wide range of writing exercises and supporting materials to maintain fairness in their educational experiences. Comprehensive Feedback : Following the conclusion of the study, all participants received detailed, personalized assessments of their writing abilities, designed to foster ongoing development irrespective of their group allocation. Participant Support and Autonomy : The well-being of the participants was closely monitored throughout the study, with any emerging issues addressed promptly. Additionally, participants were free to withdraw from the study at any time without facing negative consequences. 3.5. Data Collection Procedures At the inception of the study, participants completed a preliminary evaluation to ascertain their initial writing skills and motivation levels. The pretest encompassed a standardized IELTS writing test and a motivation scale. Both groups underwent a 12-week instructional period with identical content, with the exception of the integration of an AI-driven writing platform in the experimental group. 3.5.1. Artificial Intelligence Group The participants of the AI group engaged with an interactive AI writing platform designed to enhance their writing skills. Each participant was required to allocate a minimum of 20 minutes daily to utilize the platform and attend two hours of weekly classroom instruction. A monitoring system was implemented to ensure adherence to the prescribed regimen. This system revealed that 85% of participants met the daily requirement, while the remaining 15% engaged for an average of 15 minutes per day. The platform offered customized, real-time feedback and corrections based on the learners' input. Supplementary resources such as grammar exercises, vocabulary-building tasks, and practice tests supplemented the in-class learning experience. In the classroom, the AI group engaged in a variety of interactive writing activities, including peer feedback sessions, collaborative projects, and instructor-led writing exercises. These tasks were meticulously designed to cultivate a variety of writing competencies, including coherence, structural integrity, grammatical accuracy, and the effective use of vocabulary. The instructional materials were meticulously designed to be both stimulating and achievable, thereby fostering gradual skill enhancement within a positive and encouraging learning environment. Despite the AI platform's lack of real-time interaction, it served as a complementary component to classroom activities by offering consistent, individualized practice. The integration of AI-based and classroom-based activities ensured learners had multiple avenues to develop and refine their writing skills. 3.5.2. Classroom-Based Group The classroom-based group received an equivalent instructional duration and content as the AI group; however, they did not utilize the AI writing platform. Instead, their instruction was delivered in a traditional classroom setting by the researcher/instructor. The instructional program encompassed structured lessons on grammar, vocabulary development, and writing practice. Participants were expected to allocate two hours of weekly classroom instruction, in addition to homework assignments and quizzes designed to reinforce their writing skills. During class, the classroom-based group engaged in similar writing activities as the AI group, including collaborative tasks, guided writing sessions, and peer reviews. They were provided with identical practice materials, including worksheets and exercises, focusing on grammar, coherence, and vocabulary to strengthen their writing abilities. To account for the differences in task structure, the classroom-based group completed weekly assignments that were more comprehensive and aligned with the daily tasks assigned to the AI group. These assignments were meticulously designed to ensure that the learning objectives remained consistent across both groups, despite the variation in task frequency. After the 12-week instructional period, both groups underwent identical posttests, which included a standardized IELTS writing assessment and a motivation scale, to evaluate their progress. The posttests mirrored the pretests to ensure the comparability of results. Furthermore, semi-structured interviews were conducted with the AI group in order to obtain qualitative feedback on their interactions with the platform and its effects on their writing skills and motivation. 3.6. Data analysis 3.6.1. Quantitative Analysis In the analysis of the writing tasks completed by both groups, continuous scores were assigned for both the pretest and posttest. The normality of the data distribution was evaluated using Kolmogorov-Smirnov tests, and given the absence of outliers, parametric statistical techniques were applied for the analysis of the quantitative data. In order to evaluate the influence of the independent variables (AI-mediated vs. classroom-based interactions) on participants' writing skills and motivation to engage in writing tasks, paired sample t-tests were utilized. Furthermore, one-way analyses of covariance (ANCOVAs) were employed to examine group differences in writing performance and writing time (WT). Covariates that were deemed relevant were incorporated into the analysis with a view to controlling for baseline differences, ensuring an equitable comparison. 3.6.2. Qualitative Analysis The interview data were transcribed and analysed using Braun and Clarke's (2012) thematic analysis approach. The process commenced with an initial coding phase, during which key themes related to learners' perceptions and attitudes toward the AI-supported writing course were identified. These themes were then organised into broader categories, each of which was meticulously labelled to reflect the core concepts. To ensure the consistency and reliability of the coding, inter-rater agreement was evaluated using Cohen's Kappa coefficient. To ensure the robustness of the findings, a randomly selected subset of transcripts was independently coded by both the primary researcher and an EFL expert. The resulting Cohen's Kappa value of 0.82 indicated substantial agreement between the coders, and any inconsistencies were resolved through collaborative discussions to improve the accuracy of the analysis. 4. Results 4.1. Quantitative Analysis To assess the mean scores of EFL learners' writing skills before and after the intervention, descriptive statistics were computed, as summarized in Table 1 . It highlights the descriptive statistics for pretest and posttest writing skill scores across both AI-mediated and classroom-based groups.In the group using AI mediation,the mean scores on the pretest were as follows: fluency (5.98), vocabulary (5.45), accuracy (6.18), and overall writing performance (5.41).After the intervention, the posttest means increased to 6.82, 6.63, 6.98, and 6.84, respectively.In contrast, the classroom-based group exhibited initial means of 5.89 (fluency), 5.46 (lexicon), 6.87 (accuracy), and 4.99 (total writing). Following the intervention, the posttest means for these categories increased to 5.99, 5.81, 6.71, and 5.97, respectively.An analysis of the posttest outcomes shows that the AI group exhibited greater advancements in fluency, vocabulary, and total writing scores when compared to the classroom-based group. However, the classroom-based group demonstrated a slightly higher pretest mean in accuracy, though the posttest scores in this category slightly decreased. To determine whether these changes were statistically significant within the AI group, paired sample t-tests were performed. Table 2 presents the results, demonstrating significant improvements in most writing skill components.The fluency score demonstrated a marked increase, rising from a mean of 6.32 to 6.93, with a significant difference of 0.86 (t = 3.41, p = 0.03). A similar enhancement was observed in the lexicon score, which increased from 5.88 to 6.87, exhibiting a substantial mean difference of 1.65 (t = 6.45, p < 0.01). Accuracy scores also exhibited a significant increase, rising from 7.12 to 7.81, reflecting a mean difference of 0.88 (t = 4.05, p < 0.01). Theresults of the paired-sample t-test, as outlined in Table 3 , demonstrate alterations in mean writing skill scores for the classroom-based group from pretest to posttest. A slight increase in fluency scores was observed, with the mean rising from 5.81 to 5.99, resulting in a mean difference of 0.14, which did not reach statistical significance (t = 0.77, p = 0.45). In contrast, a significant enhancement was observed in lexicon scores, which increased from a mean of 5.28 to 6.42, with a mean difference of 0.34 (t = 3.31, p = 0.03). Although accuracy scores exhibited a modest rise from 6.51 to 6.74, this change was not statistically significant (t = 0.96, p = 0.36). The findings of the paired-sample t-test demonstrate that the AI-supported group has achieved statistically significant advancements in fluency, lexicon, accuracy, and coherence. In contrast, the classroom-based group has exhibited notable progress solely in the domain of lexicon scores. These findings underscore the efficacy of AI-assisted interventions in promoting comprehensive enhancements in the writing skills of EFL learners. To further validate these results, a one-way analysis of variance (ANOVA) was performed to examine both the statistical significance (p 0.14) of differences between the instructional approaches. According to the criteria established by ηp², values exceeding 0.14 are indicative of a substantial effect, values ranging from 0.06 to 0.14 denote a moderate effect, and values from 0.01 to 0.06 reflect a negligible effect. As outlined in Table 4 , a one-way ANCOVA was conducted, with pre-test fluency scores incorporated as covariates. The findings revealed a substantial benefit for the AI-assisted group, which attained higher fluency levels in comparison to the classroom-based group (F(1, 62) = 7.43, p = 0.01, ηp² = 0.15). This effect size is noteworthy and underscores the substantial impact of the AI intervention in enhancing fluency. Table 5 presents the findings of a one-way ANCOVA performed to assess lexicon scores, with pretest results serving as covariates. The analysis demonstrated a marked advantage for the AI-assisted group, which achieved significantly higher mean lexicon scores compared to the classroom-based group (F(1, 62) = 29.46, p < 0.01, ηp² = 0.41). This robust effect underscores the significant impact of AI integration on improving learners' lexical proficiency. In a similar vein, Table 6 presents the findings of the one-way ANCOVA for accuracy. The results of this analysis indicated a significant distinction between the two instructional approaches, as the AI group outperformed the Classroom-Based group in terms of accuracy scores (F(1, 62) = 76.12, p < 0.01, ηp² = 0.61). Additionally, pretest accuracy scores, as covariates, demonstrated a significant impact on the results (F(1, 62) = 518.21, p < 0.01, ηp² = 0.89), highlighting the considerable variance accounted for by prior performance. As illustrated in Table 7 , the findings from the ANCOVA results (F(1, 62) = 29.07, p < .001, ηp² = .46) demonstrate a substantial effect of group membership on the overall writing scores. The findings collectively underscore those learners in the AI-driven instructional group exhibited significantly higher levels of performance in all domains of writing skills , including fluency and coherence, lexical resource, grammatical accuracy, and pronunciation, when compared with those in the face-to-face setting. Of particular note is the finding that these results were obtained after adjusting for pretest scores, underscoring the efficacy of AI-based interventions in promoting writing skills. Table 8 presents the descriptive statistics for Willingness to Write (WTW) scores in both groups before and after the intervention. In the pre-intervention phase, the AI group reported a higher average WTW score (M = 3.57, SD = 0.71) compared to the classroom-based group (M = 3.26, SD = 0.61). Following the intervention, the AI group demonstrated a substantial increase in their mean score, which rose to 4.16 (SD = 0.81). In contrast, the classroom-based group exhibited a more modest improvement, attaining a mean score of 3.58 (SD = 0.61). These results underscore the considerable impact of AI-driven pedagogical approaches in enhancing engagement in writing tasks among learners. To further explore this disparity, a one-way ANCOVA was conducted to assess the improvements in WTW between the two instructional methods. As demonstrated in Table 9 , the findings indicated that the AI group exhibited considerably higher posttest WTW scores in comparison to the classroom-based group (F(1, 62) = 29.16, p < 0.001, ηp² = 0.42). Table 1 . Descriptive Statistics of Writing skills Scores for AI and Classroom-Based Groups Group Writing skills N Pertest Post-test Mean SD Mean SD AI Fluency 33 5.95 1.03 6.53 1.14 Lexicon 33 5.08 .94 6.28 .79 Accuracy 33 6.14 1.01 6.93 .94 Total Writing 33 5.12 .51 6.67 .64 Classroom-Based Fluency 32 5.86 .87 5.97 .79 Lexicon 32 5.29 .97 5.58 .84 Accuracy 32 6.00 .91 6.16 .90 Total writing 32 4.98 .64 5.89 .82 Table 2. Paired sample T-test Results for Pretest and post-test writing Skills Scores in the AI Group Writing skills Mean (Pertest) Mean (Post-test) Mean Difference Std. Deviation t-value p-value Fluency 6.10 7.43 .86 1.04 3.41 .03 Lexicon 5.88 6.87 1.65 .89 6. 45 .00 Accuracy 7.12 7.81 .88 .95 4 . 05 .00 Table 3. Paired Sample t-test Results for Pretest and Posttest Writing Skills Scores in the Classroom-Based Group Writing skills mean (Pertest) Mean (Post-test) Mean Difference Std. Deviation t-value p-value Fluency 6.32 6.93 .14 .71 .77 .45 Lexicon 5.28 6.42 .34 .82 3.31 .03 Accuracy 6.51 6.74 .19 .91 .96 .36 Table 4. Comparison of Writing Fluency Between the AI Group and Classroom-Based Group Source Type III sum of squares df Mean square F P Partial eta squared Pre-fluency (covariates) 6.22 1 6.22 22.57 .04 .10 Groups 1.34 1 1.34 7.43 .01 .15 Table 5. Comparison of Lexical Scores Between the AI Group and Classroom-Based Group Source Type III sum of squares df Mean square F P Partial eta squared Pre-lexicon (covariates) 14.51 1 14 .51 72.12 .00 .56 Groups 5.15 1 5 .51 29.46 .00 .41 Table 6. Comparison of Writing Accuracy Between the AI Group and Classroom-Based Group Source Type III sum of squares df Mean square F Sig. Partial eta squared Pre-accuracy (covariates) 33.16 1 33.18 518.21 .00 .89 Groups 4.12 1 4.12 76.12 .00 .61 Table 7. Comparison of Total Writing Performance Between the AI Group and Classroom-Based Group Source Type III sum of squares Df Mean square F Sig. Partial eta squared Pre-total Writing (covariates) 8.76 1 8.76 81.23 .00 .71 Groups 4.03 1 4.03 29.07 .00 .46 Table 8. Descriptive Statistics of Pretest and Posttest WTW Scores for AI and Classroom-Based Groups Group N Mean Std. Deviation Std. Error Mean Pre-WTW AI 23 3.57 .71 .15 Classroom-Based 22 3.26 .61 .16 Post-WTW AI 23 4.16 .81 .14 Classroom-Based 22 3.58 .61 .15 Table 9. Comparison of WTW Scores Between the Two Groups Source Type III sum of squares df Mean square F Sig. Partial eta squared Pre-WTW (covariates) 31.12 1 31.12 186.42 .00 .73 Groups 3.02 1 3.02 29.16 .00 .42 4.2. Qualitative Analysis In order to examine the reasons for the enhanced effectiveness of AI-mediated instruction in improving writing skills and motivation in EFL learners when compared to traditional classroom approaches, semi-structured interviews were conducted with nine participants from the AI-supported writing group. Thematic analysis was employed to explore the participants' perspectives on the interactive AI platform, illuminating its perceived benefits and the challenges encountered during its utilization. A recurrent theme that emerged was the personalization afforded by the AI-mediated tools. Participants frequently highlighted how the platforms offered tailored feedback and guidance aligned with their individual proficiency levels and learning needs. Many students expressed appreciation for the ability of the AI to identify specific writing challenges and provide instant, targeted feedback, which facilitated their progress significantly. For instance, one student articulated their experience by stating: "The AI provided feedback that was customized to my writing level, helping me address my weaknesses more effectively. It also corrected my mistakes promptly, enabling me to make immediate improvements." Another salient theme that emerged pertained to the low-pressure environment engendered by the AI-mediated platforms. Students characterized the virtual interface as a non-judgmental space where they could engage in practice without the concern of criticism or embarrassment. This environment was conducive to enhancing their engagement with writing tasks and fostering their confidence. As one participant articulated, "Writing with the AI platform felt less intimidating than traditional peer reviews or instructor feedback. I could experiment with different styles and ideas without worrying about making mistakes." Learners indicated that the utilization of AI tools for writing tasks had a considerable impact on enhancing fluency and confidence. It was emphasized by the learners that the diverse writing prompts and interactive exercises offered by these platforms contributed to significant improvements in grammar and vocabulary. Furthermore, the tools were regarded as instrumental in fostering creativity and bolstering self-confidence in their writing abilities. As one participant remarked: "The AI exercises helped me refine my grammar and vocabulary while also making me more confident in my ability to write fluently and coherently." Furthermore, the learners expressed appreciation for the diversity of activities provided by the AI-driven platform. They emphasized that these platforms integrated writing tasks with complementary exercises, such as grammar drills and vocabulary-building activities, which enriched their learning experience. One student offered the following comment: "The platform included a range of exercises, from brainstorming and drafting to vocabulary enrichment and editing tasks. This diversity kept the process engaging and helped me improve across multiple dimensions of writing." However, not all feedback was unequivocally positive. A subset of learners indicated a predilection for human instructors, attributing the value of nuanced, contextually rich feedback to a quality that AI systems might be deficient in providing. Additionally, participants noted that while AI responses were generally beneficial, occasional inaccuracies or irrelevant suggestions undermined their learning experience. As one participant noted, "Although the AI tool was useful, it sometimes failed to understand the context of my writing or gave feedback that didn’t align with my goals. In such cases, I missed having a teacher’s guidance." In sum, the findings of the present study demonstrate that AI-driven interactive writing platforms have a positive impact on learners by offering personalized feedback, fostering a supportive learning environment, and providing varied and engaging activities. However, it is important to note the limitations of these platforms, which include a lack of contextual depth and occasional errors in AI responses. These limitations underscore the ongoing significance of human interaction in the context of EFL writing instruction. 4.2.1.Qualitative Insights While many EFL learners found the AI-driven platforms beneficial, some expressed a preference for human instructors, citing the distinct advantages of human interaction. These learners noted that human instructors were more engaging, capable of delivering detailed and contextually rich feedback, and adept at creating a realistic communicative environment. Furthermore, they placed a high value on the cultural insights and nuanced context imparted by human instructors, which they believed enriched their overall learning experience. One participant offered the following remark: "I believe human instructors are better because they provide more detailed feedback and keep the learning process engaging. They create a realistic environment for communication and add cultural context to the language, which AI cannot fully replicate." Notwithstanding the merits of the AI-mediated platforms, certain participants identified deficiencies in their functionality. Specifically, participants noted that AI responses were, on occasion, either irrelevant or inaccurate, thereby detracting from the learning experience. For instance, a learner commented: "The Chatbot isn’t always precise. Sometimes, its responses don’t make sense or fail to align with the flow of the conversation." The themes derived from this qualitative analysis indicate that AI-driven interactive platforms had a predominantly positive impact on learners' attitudes and perceptions. These platforms were particularly appreciated for their ability to provide customized feedback, create stress-free practice environments, and offer a variety of activities that promoted fluency and self-confidence. Moreover, the instantaneous feedback provided by AI was identified as a pivotal benefit, enabling the swift identification and correction of errors. However, a segment of learners emphasized the irreplaceable value of human interaction. These individuals noted that human instructors provide more comprehensive feedback and maintain motivation through engaging and dynamic interaction. Furthermore, they acknowledged the limitations of AI tools in handling nuanced or culturally specific contexts and highlighted occasional errors in the AI-generated feedback. In summary, while AI-mediated platforms offer innovative and effective solutions for language learning, human interaction remains a critical component for addressing the social, cultural, and contextual aspects of language acquisition. 5. Discussion This research, grounded in Engeström's (1987) activity theory, examined the impact of AI-driven interactive writing platforms on the writing skills and motivation to engage in writing tasks among EFL learners. The findings indicated that these AI-mediated platforms significantly enhanced both aspects. Consistent with these observations, subsequent studies have documented analogous trends. For instance, research by Zhao (202 2 ) and Liand Chan (2024) has underscored the efficacy of AI-based systems in enhancing language skills and instilling confidence in learners. One plausible explanation for these advancements is the supportive and judgment-free environment AI creates, allowing learners to experiment and refine their writing without fear of criticism. This observation is corroborated by Wu and Zhang (2022). In the current digital era, characterized by the pervasive influence of technology across various domains, including gaming, social interaction, and knowledge acquisition (Huang et al., 2023), there has been a notable increase in the adoption of AI tools by learners for academic purposes. AI-powered writing platforms offer a conducive environment for learners to practice, thereby reducing anxiety about making errors and enhancing their confidence in writing. Research has demonstrated that these tools effectively reduce writing anxiety (Chen et al., 2023) while enhancing coherence, vocabulary, and grammatical precision. Consequently, learners are empowered to continue their education beyond traditional classroom settings. Moreover, Tambunan et al. (2023) underscore the pivotal role of AI platforms in fostering self-directed learning, a notion that finds congruence with the findings of the present study. Engeström's activity theory offers a valuable framework for comprehending these outcomes. According to this theory, learning occurs within a complex system comprising interconnected elements such as the subject, the object, tools, rules, community, and the division of labor (Engeström, 1987). In the context of this study, the AI-powered writing platform functioned as a mediating instrument, thereby facilitating structured interactions through the provision of automated prompts and tailored feedback.This scaffolding, as posited by Wang et al. (2023), fostered learners' incremental development in writing skills , concurrently enhancing their self-efficacy and motivation. The division of labor within this system was evident as learners actively engaged with the platform by responding to tasks, while the AI system provided guidance and feedback. The community dimension involved interactions within a broader network of learners using similar platforms, fostering a collaborative and supportive environment. Lio et al., (2023) emphasize that such ecosystems mitigate anxiety and cultivate confidence, a finding that is corroborated by this study. These findings position AI-driven interactive writing platforms as transformative tools in language education, offering accessible and highly effective opportunities for learners to develop writing skills in a supportive, low-stress context. However, participants noted that while these platforms offer numerous benefits, they sometimes fail to match the depth of human instruction. In accordance with the observations made by Fryer and Carpenter in 2006, the limitations of AI systems pertain to the difficulty in accurately interpreting complex or nuanced written expressions. This challenge is especially pronounced for novice learners, as AI platforms frequently encounter difficulties in comprehending and responding to their intended meanings. Furthermore, learners identified the absence of authentic human interaction as a crucial element in fostering socio-cognitive and psychological support during writing tasks. The successful utilization of AI platforms is contingent upon access to adequate technological infrastructure, including reliable devices and high-speed internet, which can pose practical challenges. Prior studies have identified specific limitations of AI-based systems, including limited contextual understanding (Kim et al., 2020), inconsistent accuracy in responses (Lu et al., 2006), and difficulty managing complex inputs (Ayedoun et al., 2015). These findings underscore the necessity to remediate these deficiencies to enhance the efficacy of AI platforms in language learning contexts. While AI-driven writing platforms offer substantial advantages for EFL learners, the findings indicate that they should complement, rather than replace, human instruction. A hybrid approach, integrating AI tools with conventional teaching methods, has the potential to generate a more comprehensive and enriching learning experience that addresses both the technological constraints and learners' socio-cognitive needs. 6. Conclusion This study offers substantial evidence of the transformative potential of AI-driven dynamic writing platforms in the context of EFL learning. Through the utilization of AI-mediated tools, which possess adaptive and interactive capabilities, learners demonstrated quantifiable advancements in their writing skills across various dimensions, including fluency, coherence, lexical diversity, and grammatical accuracy. Furthermore, these platforms have been observed to elicit a substantial enhancement in learners' motivation to engage in writing tasks, thereby mitigating the disparity between the acquisition of technical writing skills and the fostering of affective engagement. A distinctive feature of this research is its emphasis on the dual impact of AI-mediated platforms, which not only enhance the technical aspects of writing but also cultivate a positive attitude toward writing as a creative and collaborative process. In contrast to conventional classroom-based methods, the AI-driven model provides customized feedback, real-time adjustments, and dynamic scaffolding, thereby empowering learners to overcome individual challenges and progress at their own pace. From a macro perspective, this study contributes to the advancement of the field of computer-assisted language learning (CALL) by demonstrating the potential of artificial intelligence (AI) to enhance linguistic skills and promote learner autonomy. The findings underscore the significance of integrating such technologies into language education, providing educators with novel methodologies for developing more interactive and impactful writing programs. Furthermore, this research underscores the pressing need for further investigation into the evolving role of AI in reshaping the learning experience. By fostering intrinsic motivation and offering personalized learning opportunities, AI platforms signify a shift in EFL education, empowering learners to become active participants in their educational journeys rather than passive recipients of knowledge. Future research should explore ways to further optimize these platforms for diverse educational settings and investigate their long-term effects on writing skills and learner motivation. In conclusion, AI-powered interactive writing platforms function not only as tools for enhancing language skills but also as catalysts for fostering a more engaged, autonomous, and confident generation of EFL learners. By leveraging the capabilities of AI, educators can transform the language learning experience, rendering it more personalized, motivating, and better suited to the demands of global communication. 6.1. Practical Implications for the EFL Context The findings of this study offer valuable insights and practical applications for various stakeholders, including EFL learners, educators, teacher trainers, and researchers. The study underscores the transformative function of AI-driven writing platforms in fostering learners' engagement with writing tasks and enhancing their overall proficiency. These platforms offer a multifaceted and interactive environment for practice, emphasizing key areas such as grammatical accuracy, vocabulary expansion, and the development of well-structured texts. A particularly salient feature of AI-based instruction is its capacity to deliver customized feedback, catering to the distinct requirements of individual learners while fostering a supportive and personalized learning environment. Additionally, the study underscores the significance of an environment devoid of stress in fostering language development. AI-mediated platforms offer learners the autonomy to progress at their own pace, unencumbered by the immediate pressure of assessment. This approach fosters a sense of confidence and encourages the development of fluency and creativity in written expression. By offering a learner-centered framework, AI-driven platforms have the potential to transform the traditional dynamics of writing instruction, making the process more engaging and effective for EFL learners. Moreover, by circumventing the limitations imposed by time constraints and large class sizes that are characteristic of traditional classroom settings, EFL learners can leverage AI-driven tools for uninterrupted writing practice. These platforms empower learners to extend their writing practice into informal contexts, fostering autonomy in addressing challenges and developing their skills. For EFL instructors, the integration of AI-driven platforms as supplementary tools in writing courses offers learners additional opportunities to practice and refine their skills. By integrating these AI-driven tools with conventional interactive writing tasks, instructors can foster a more comprehensive and supportive learning environment. Teacher educators and instructors are encouraged to train learners and their peers in the effective use of AI platforms for a variety of writing-related activities. The findings underscore the necessity for additional research to be conducted on the implementation of AI-driven writing platforms in language education. The development of more advanced systems capable of delivering highly personalized and adaptive feedback has the potential to further enhance their utility for EFL learners. These advancements would contribute to a more profound understanding of how AI-mediated tools can support language acquisition and inspire future innovations in language learning technology. 6.2. Limitations and Recommendations for Future Research As with numerous studies in this field, this research encountered several limitations that future studies in EFL could address. Firstly, the relatively small sample size may not provide a full representation of the diverse EFL learner population. In order to enhance the generalizability of the results, subsequent research should endeavor to incorporate larger, more heterogeneous sample groups, potentially examining various demographic factors such as age, proficiency levels, and cultural backgrounds. Furthermore, replicating the study in a range of educational contexts, such as different countries or academic settings, could help confirm the broader applicability of the findings.Secondly, the study's limited duration constrained the exploration of the long-term implications of AI-based instruction on learners' writing development and motivation. To build on this, future investigations should consider extended study periods, allowing for a deeper exploration of how prolonged exposure to AI-driven platforms influences writing skills, sustained motivation, and learner autonomy over time. Furthermore, examining the cumulative effects of AI interventions in various phases (e.g., short-term vs. long-term engagement) could yield a more comprehensive understanding of their overall impact. A critical constraint in the present study stemmed from the amalgamation of instructional strategies within the experimental group, wherein participants encountered both AI-supported and conventional writing exercises. This methodological detail introduces a potential confounding factor, which complicates the direct attribution of the observed enhancements in writing proficiency and motivation to the AI-driven platforms alone. To address this challenge, future investigations should aim for a more rigorously controlled experimental setup, wherein the precise impact of AI interventions can be isolated from other pedagogical approaches. Such an approach would serve to enhance the internal validity of the research and facilitate a more precise delineation of the unique contributions of AI technologies in fostering writing skills and motivating EFL learners. It is imperative to acknowledge that this study was conducted within a specific context, which may constrain its generalizability to diverse educational settings.Consequently, subsequent investigations could assess the efficacy of AI-mediated writing platforms across diverse contexts, such as higher education, professional training programs, or multilingual learning environments. This would provide insights into how different settings influence the outcomes of AI-driven writing instruction. This study focused on a specific AI-driven platform, which may limit the applicability of findings to other tools. Subsequent research endeavors may entail the exploration of a more extensive array of AI-driven writing systems, with the objective of assessing their potential in enhancing writing skills and motivation across a range of learner demographics. By examining various AI tools, researchers can ascertain whether the observed benefits are consistent across platforms or unique to certain systems. Declarations Author Contributions The author was responsible for the conceptualization, methodology, investigation, writing of the original draft, and writing - review and editing of the manuscript. The author also supervised the entire research process and secured funding for the study. Funding This research received no external funding. Data Availability The data that support the findings of this study are available from the author upon reasonable request. Conflict of Interest: The author declares that there is no conflict of interest. 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In 2023 25th International Conference on Advanced Communication Technology (ICACT) (pp. 1414–1423). IEEE. https://doi.org/10.23919/ICACT56868.2023.10079464 Nykyporets SS (2023) Harnessing cloud technologies for foreign language acquisition among masters in energy engineering. Moderní aspekty vědy: Svazek XXXI mezinárodní, 21–56. http://ir.lib.vntu.edu.ua/handle/123456789/37121 Peng J (2015) L2 motivational self-system, attitudes, and affect as predictors of L2 WTC: An imagined community perspective. Asia-Pacific Educ Researcher 24(2):433–443. https://doi.org/10.1007/s40299-014-0195-0 Pentina I, Hancock T, Xie T (2023) Exploring relationship development with social Chatbots: A mixed-method study of replika. Comput Hum Behav 140 Article 107600. https://doi.org/10.1016/j.chb.2022.107600 Qadir J (2022) Engineering education in the era of ChatGPT: Promise and pitfalls of generative AI for education. TechRxiv Preprint. https://doi.org/10.36227/techrxiv.21789434.v1 Rad HS, Alipour R, Jafarpour A (2023) Using artificial intelligence to foster students’ writing feedback literacy, engagement, and outcome: A case of Word-Tune application. Interact Learn Environ 1–21. https://doi.org/10.1080/10494820.2023.2208170 Silitonga LM, Hawanti S, Aziez F, Furqon M, Zain DSM, Anjarani S, Wu TT (2023) The impact of AI chatbot-based learning on students’ motivation in English writing classroom. In International conference on innovative technologies and learning (pp. 542–549). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-40113-8_53 Tai TY, Chen HHJ (2020) The impact of Google Assistant on adolescent EFL learners’ willingness to communicate. Interact Learn Environ 1–18. https://doi.org/10.1080/10494820.2020.1841801 Tambunan ARS, Andayani W, Sari WS, Lubis FK (2022) Investigating EFL students’ linguistic problems using Grammarly as automated writing evaluation feedback Timpe-Laughlin V, Sydorenko T, Daurio P (2020) Using spoken dialogue technology for L2 speaking practice: What do teachers think? Comput Assist Lang Learn 35(5–6):1194–1217. https://doi.org/10.1080/09588221.2020.1774904 Umamah A, Cahyono BY (2022) EFL university students’ use of online resources to facilitate self-regulated writing. Comput Assist Lang Learn 23(1):108–124. http://callej.org/journal/23-1/Umamah-Cahyono2022.pdf Underwood J (2017) Exploring AI language assistants with primary EFL students. In K. Borthwick, L. Bradley, & S. Thouësny (Eds.), CALL in a climate of change: Adapting to turbulent global conditions—Short papers from EUROCALL 2017 (pp. 317–321). 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Libr Hi Tech 40(1):80–97. https://doi.org/10.1108/lht-05-2020-0113 Wei L (2023) Artificial intelligence in language instruction: Impact on English learning achievement, L2 motivation, and self-regulated learning. Front Psychol 14:1261955. https://doi.org/10.3389/fpsyg.2023.1261955 Wiboolyasarin W, Wiboolyasarin K, Suwanwihok K, Jinowat N, Muenjanchoey R (2024) Synergizing collaborative writing and AI feedback: An investigation into enhancing L2 writing proficiency in wiki-based environments. Computers Education: Artif Intell 100228. https://doi.org/10.1016/j.caeai.2024.100228 Wu W-CV, Hsieh C, J. S., Yang JC (2017) Creating an online learning community in a flipped classroom to enhance EFL learners’ oral proficiency. Educational Technol Soc 20(2):142–157. https://www.jstor.org/stable/90002170 Wu W, Zhang B, Li S, Liu H (2022) Exploring factors of the willingness to accept AI-assisted learning environments: An empirical investigation based on the UTAUT model and perceived risk theory. Front Psychol 13:870777. https://doi.org/10.3389/fpsyg.2022.870777 Yang H, Kim H, Lee JH, Shin D (2022) Implementation of an AI chatbot as an English conversation partner in EFL speaking classes. ReCALL 34(3):327–343. https://doi.org/10.1017/S0958344022000039 Yanguas I (2010) Oral computer-mediated interaction between L2 learners: It’s about time. Lang Learn Technol 14(3):72–93. https://doi.org/10.17507/tpls.1002.01 Yashima T (2009) International posture and the ideal L2 self in the Japanese EFL context. In Z. Dörnyei & E. Ushioda (Eds.), Motivation, language identity and the L2 self (pp. 144–163). Multilingual Matters. https://doi.org/10.21832/9781847691293-008 Zhao X (2022) Leveraging artificial intelligence (AI) technology for English writing: Introducing Wordtune as a digital writing assistant for EFL writers. RELC J. https://doi.org/10.1177/00336882221094089 Zhao D (2024) The impact of AI-enhanced natural language processing tools on writing proficiency: An analysis of language precision, content summarization, and creative writing facilitation. Educ Inform Technol 1–32. https://doi.org/10.1007/s10639-024-13145-5 Zhou T, Cao S, Zhou S, Zhang Y, He A (2023) Chinese intermediate English learners outdid ChatGPT in deep cohesion: Evidence from English narrative writing. System. https://doi.org/10.1016/j.system.2023.103141 Zou B, Lyu Q, Han Y, Li Z, Zhang W (2023) Exploring students’ acceptance of an artificial intelligence speech evaluation program for EFL speaking practice: An application of the integrated model of technology acceptance. Comput Assist Lang Learn. https://doi.org/10.1080/09588221.2023.2278608 Additional Declarations The authors declare no competing interests. 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Introduction","content":"\u003cp\u003eThe proliferation of artificial intelligence (AI) tools in the domain of foreign language education has precipitated a paradigm shift in the methodology of writing skill instruction and acquisition. AI-driven interactive writing platforms are transforming the landscape of foreign language education by offering innovative tools to enhance learners' writing skills and autonomy (Zhao, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These platforms are equipped with features such as grammar checkers, real-time writing analysis, and automated feedback systems, which are designed to support students in improving various aspects of their writing, including syntax, vocabulary, grammar, and content (Jeanjaroonsri, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eLeveraging advanced machine-learning algorithms, these platforms provide customized feedback by comparing user-generated text against extensive databases of exemplary and flawed writing samples (Jeanjaroonsri, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Real-time feedback enables learners to identify and rectify errors in an instant, thereby cultivating a more profound comprehension of the fundamental principles that underpin effective writing. AI-driven platforms foster self-directed learning by enabling students to actively engage with their writing tasks and refine their skills independently (Wang et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These platforms are particularly beneficial for foreign language learners, as they are designed to address the unique challenges posed by limited language proficiency. These tools offer user-friendly interfaces and dynamic feedback mechanisms, empowering learners to enhance their writing abilities while building confidence and motivation (Wang, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). A multitude of studies have indicated that these technologies are effecting a transformation in writing instruction and learning, thereby paving the way for more effective, interactive, and tailored educational experiences.\u003c/p\u003e \u003cp\u003eIn recent years, there has been a growing interest in the influence of AI-based writing platforms on learners' writing skills, particularly in the TEFL context. While some research highlights the benefits of these tools in improving students' writing skills (Fitriani, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Wang, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Zhao, 2023), other studies raise concerns about their potential limitations (e.g., Liu et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Lund et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Qadir, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Nevertheless, much of the existing literature has focused on improving grammar and syntax, often neglecting other essential aspects of effective writing.\u003c/p\u003e \u003cp\u003eOnline platforms provide EFL learners with the means to overcome the limitations of the traditional classroom setting, including time limitations, large class sizes, and inadequate individualized attention. These platforms facilitate the provision of customized feedback, foster meaningful communication outside the classroom, and promote continuous enhancement of writing skills through interactive activities. Research indicates that such tools also positively influence learners' willingness to engage in writing tasks, fostering confidence and motivation to communicate their ideas effectively (Jeon, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Pentina et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This willingness, often shaped by contextual factors, plays a critical role in learners' ability to express themselves creatively and confidently (Ayedoun et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRecent advancements in artificial intelligence (AI) technology have led to substantial expansions in the scope of research in the domain of language learning, as evidenced by a substantial body of research (see Araujo \u0026amp; Bol, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Zhou et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Among these innovations, AI-driven writing platforms and chatbot-mediated instruction have emerged as transformative tools for language education. Specifically, chatbots, defined as AI programs designed for real-time interaction, simulate authentic communication scenarios, enabling learners to practice writing skills dynamically and engagingly (Henkel et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The incorporation of naturalness and the alignment of content with learners' contextual needs has been demonstrated to significantly enhance the appeal of writing tasks for EFL students. This pedagogical approach fosters a flexible learning environment, enabling learners to enhance their writing skills unencumbered by constraints related to time and location (Fathi \u0026amp; Rahimi, 2022; Hsu, 2016; Wu et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn the context of English as a foreign language (EFL), the utilisation of chatbots can facilitate a range of opportunities for interaction, including the cultivation of conversational proficiency, the refinement of grammatical competence, and the augmentation of vocabulary (Kim et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Furthermore, they function as a flexible resource for writing-focused tasks, enabling learners to draft texts, seek clarifications, and receive instant feedback generated by AI (Walker \u0026amp; White, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Hsu et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Jeon, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). While preliminary studies have underscored the potential of AI-driven chatbots in language learning (Yang et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), further exploration is necessary to gain a more profound understanding of their broader effects, particularly in enhancing writing abilities and promoting sustained engagement in writing activities (Yanguas, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA growing body of research has emerged that underscores the merits of chatbots in facilitating language acquisition (Kim, 2017; Timpe-Laughlin et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Yang et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, the precise function of these bots in enhancing writing abilities, particularly in domains such as coherence, organisation, and lexical diversity, remains to be comprehensively investigated (Underwood, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Kessler, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This lacuna underscores the urgent necessity for additional research to examine how chatbot interactions can foster greater fluency and precision in writing, as well as the motivational factors that perpetuate learners' engagement with writing tasks.\u003c/p\u003e \u003cp\u003eThis research investigates the role of AI-powered chatbots in fostering an interactive and simulated environment that supports EFL learners in enhancing their writing skills. The investigation focuses on the impact of structured grammar exercises, vocabulary development, and interactive prompts on learners' abilities to organize ideas, sustain fluency, and produce coherent written texts. Furthermore, the study examines learners' attitudes and perceptions regarding the utilization of chatbot-assisted tools for writing tasks. The qualitative analysis illuminates the advantages and disadvantages of AI-driven platforms, highlighting their potential to create an engaging and supportive environment that motivates learners.\u003c/p\u003e \u003cp\u003eBy exploring the intersection of writing skills and learner motivation in chatbot-mediated environments, this study contributes to the growing body of literature on the integration of artificial intelligence (AI) in language education. Practical insights are provided for educators and researchers, advocating for the design of learner-centered writing activities that leverage chatbot technology to align with educational goals and address individual learner needs.\u003c/p\u003e"},{"header":"2. Literature review","content":"\u003ch3\u003e\u003cstrong\u003e2.1\u003c/strong\u003e. \u003cstrong\u003eThe Role of Technology in Enhancing Writing Skills and Engagement\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eAdvancements in technology have significantly influenced the methods used to teach writing in the digital era. Haleem et al. (2022) highlight that incorporating digital tools into educational settings has revolutionized traditional approaches to writing instruction, introducing more dynamic and interactive methodologies. Garlinska et al. (2023) further emphasize that tools like virtual classrooms, online workshops, and cloud-based writing platforms have initiated a substantial shift in teaching practices. These platforms provide functionalities such as instant feedback, collaborative editing, and plagiarism detection. As noted by Nykyporets (2023), these features not only improve students\u0026apos; writing skills but also encourage the development of critical thinking and independent problem-solving skills.\u003c/p\u003e\n\u003cp\u003eTechnology has been shown to have the potential to enrich the writing process by integrating multimodal approaches and promoting digital literacy. It has been demonstrated to facilitate cognitive growth, encourage independent thinking, and support the adoption of effective learning (Chauhan et al., 2023). Digital tools can address the unique needs of struggling writers by leveraging their existing knowledge and aiding in the cognitive processes involved in writing. This is particularly true when employing knowledge transformation strategies that align with writing goals such as topics, genres, and text structure (Rad et al., 2023; Hsu et al., 2023).\u003c/p\u003e\n\u003cp\u003eIn educational settings, there is an increasing interest in the influence of technology integration into writing instruction on outcomes (Ahmed et al., 2024). Effective technology integration involves the incorporation of digital resources, including computers, portable devices, online platforms, and applications, into daily classroom practices (Chang et al., 2021). This method is rooted in the Technological Pedagogical Content Knowledge Framework (TPACKF), which underscores the integration of technology, pedagogy, and subject matter expertise to improve students\u0026apos; writing skills, empowering them to craft more comprehensive and grammatically precise essays (Ahmed et al., 2024).\u003c/p\u003e\n\u003cp\u003eAn expanding body of research has demonstrated the potential for AI-powered platforms, such as chatbots, to significantly enhance learner motivation (Silitonga et al., 2023) and promote active engagement (Yashima, 2009; Carayannopoulos, 2018). In comparison to conventional classroom teaching methods, the utilization of chatbot-driven interactions has been demonstrated to engender a more personalized and dynamic learning experience, thereby fostering greater engagement in the educational process (Guo et al., 2023; Peng, 2015; Yashima, 2009).This heightened engagement has been identified as being instrumental in improving EFL learners\u0026apos; Willingness to Write (WTW) and fostering autonomy, thereby empowering learners to take responsibility for their progress and to persist in meeting their writing goals.\u003c/p\u003e\n\u003cp\u003eFor instance, Lee (2019) emphasized the pivotal role of socio-political factors, including instructional goals, teaching strategies, contextual elements such as community dynamics, and personal attributes such as confidence and anxiety, in shaping engagement in writing in digital spaces outside formal education. In a similar vein, Tai and Chen\u0026apos;s (2020) research examined the utilization of Google Assistant among EFL learners, revealing that it substantially boosted their confidence and inclination to write while concurrently mitigating anxiety.\u003c/p\u003e\n\u003cp\u003eThe integration of AI into educational settings empowers individuals to effectively engage with computers, robots, machines, and various software tools. A notable innovation in AI is the chatbot, which facilitates endless authentic and natural interactions, serving as a versatile conversational partner to support language acquisition (Huang et al., 2022). These interfaces replicate human-like communication, enabling learners to inquire and receive responses naturally. This capacity is especially beneficial for individuals with limited opportunities to practice language production, as it provides them with access to interactions that resemble those with native English speakers, irrespective of time or location (Walker \u0026amp; White, 2013).\u003c/p\u003e\n\u003cp\u003eAI-driven chatbots present a pragmatic solution to the challenges encountered by EFL learners with restricted access to native or non-native speakers. These chatbots furnish a malleable and attainable platform for language practice, empowering learners to partake in meaningful interactions unencumbered by the constraints of time, location, or the availability of human partners. The seamless and user-friendly design of these chatbots ensures consistent opportunities for learners to enhance their communication skills in diverse contexts.These bots offer numerous advantages in EFL teaching and learning, including conducting conversations via text and audio, generating intelligent responses, engaging actively, providing pronunciation feedback, and fostering understanding and insight (Walker \u0026amp; White, 2013). Consequently, EFL learners possess the potential to achieve improved learning outcomes (Walker \u0026amp; White, 2013; Fryer et al., 2020; Huang et al., 2022), benefit from increased convenience and autonomy in their learning processes (Walker \u0026amp; White, 2013), experience reduced anxiety and elevated comfort (Kim, 2016), enhance their engagement and confidence, and overcome psychological and transactional barriers in foreign language acquisition (Men et al., 2022).\u003c/p\u003e\n\u003cp\u003eAI-powered tools and applications have increasingly revolutionized writing instruction by offering tailored learning experiences. As Chaisiri (2023) emphasize, these platforms empower educators to identify individual learners\u0026apos; strengths and areas requiring improvement, allowing them to adjust instructional strategies for optimized outcomes. In a similar vein, Bhutoria (2022) underscores the significance of such technologies in fostering personalized learning, ensuring that students receive support that aligns with their distinct needs and preferences. Furthermore, mobile technologies empower students to engage in public sharing of their work, promoting confidence and skill development through peer feedback (Umamah \u0026amp; Cahyono, 2022). Cahyono et al. (2023) contend that this collaborative approach fosters a supportive learning environment that enhances both individual and group learning experiences. However, as Duncan and Joyner (2022) caution, the adoption of digital writing platforms is not without challenges. These include issues of digital equity, data security, and potential distractions. These challenges must be carefully managed to maximize the benefits of these platforms. These challenges underscore the necessity of a proactive and reflective approach in the development of pedagogical strategies and policies to address the evolving landscape of writing education in the age of artificial intelligence.\u003c/p\u003e\n\u003ch3\u003e2.2.\u0026nbsp;\u003cstrong\u003eThe Role of AI-Driven Dynamic Writing Platforms in Enhancing EFL Learners\u0026rsquo; Writing Skills and Motivation\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eThe utilization of AI-powered interactive writing tools in educational settings has garnered considerable attention for their potential to enhance the writing competencies of English as a Foreign Language (EFL) learners (Freiermuth, 2020; Dogan et al., 2023). These platforms have been demonstrated to improve writing accuracy, style, and overall quality, while concurrently exerting a favourable influence on student motivation and performance. Notable examples of such tools include Grammarly, QuillBot, WordTune, and Jenni, which have been shown to refine written work and foster engagement (Dogan et al., 2023; Freiermuth, 2020).Notwithstanding the aforementioned benefits, concerns have been raised about the potential for overdependence on such tools and their possible negative effects on learners\u0026apos; critical thinking skills (Dogan et al., 2023). Nonetheless, the growing adoption of these technologies underscores their transformative potential in EFL writing education.\u003c/p\u003e\n\u003cp\u003eGrammarly, for instance, offers immediate feedback on grammar, punctuation, spelling, clarity, and engagement, thereby transforming the writing process into an interactive learning experience. As Tambunan et al. (2022) have demonstrated, Grammarly has been shown to improve students\u0026apos; grammatical accuracy, punctuation, and sentence structure. The platform\u0026apos;s capabilities extend beyond mere error detection, as it provides actionable suggestions aimed at enhancing textual coherence and style. This fosters not only skill development but also confidence in learners.\u003c/p\u003e\n\u003cp\u003eQuillBot, a widely recognized tool for its paraphrasing capabilities, assists students in avoiding plagiarism by preserving the fundamental essence of their content. Kurniati and Fithriani (2021) emphasize the tool\u0026apos;s crucial role in improving paraphrasing skills, a fundamental aspect of academic writing.QuillBot\u0026apos;s capacity to streamline complex sentences without compromising their contextual relevance renders it a valuable asset for students engaged in academic assignments or research projects.\u003c/p\u003e\n\u003cp\u003eWordTune, an additional artificial intelligence-based tool, emphasizes the refinement of tone and the execution of stylistic adjustments.Lam and Moorhouse (2022) posit that WordTune facilitates self-assessment and reflection by encouraging users to examine and improve their writing weaknesses. A distinguishing feature of WordTune, according to them, is its capacity to go beyond conventional grammar checkers by offering a deeper examination of stylistic elements. This feature empowers students to explore a more expansive range of tones and expressions, thereby enhancing their writing repertoire.\u003c/p\u003e\n\u003cp\u003eAI-powered tools like Grammarly, QuillBot, and Turnitin have demonstrated remarkable potential in fostering academic integrity(Farrokhnia et al., 2023). These platforms not only enhance grammar and writing style but also assist users in paraphrasing content effectively, reducing the risk of plagiarism while preserving the original meaning.However, the extent to which these tools have achieved a complete elimination of plagiarism is contingent on various factors. These tools assist users in paraphrasing text, correcting grammar, and providing feedback, thereby helping to prevent the unauthorized duplication of others\u0026apos; work. Nevertheless, the onus falls on the user to ensure ethical usage of these tools, as their improper or excessive use can potentially lead to plagiarism.By providing predictive suggestions and content ideas, Jenni helps reduce cognitive load and enhance writing efficiency, particularly for novice writer\u003cspan dir=\"RTL\"\u003e\u0026nbsp;)\u0026nbsp;\u003c/span\u003eMoorhouse (2022) .\u003c/p\u003e\n\u003cp\u003eTo eradicate plagiarism in its entirety, the utilization of artificial intelligence (AI) tools should be centered on fostering originality and promoting proper citation practices. When utilized in the appropriate manner, these AI tools can provide English as a Foreign Language (EFL) learners with customized, adaptive assistance for their writing development (Farrokhnia et al., 2023). By addressing common language difficulties and fostering self-guided learning, these tools contribute to enhancing critical writing abilities and fostering long-term motivation in students. However, future studies must examine the long-term impacts of AI tool use on writing independence and critical thinking to ensure a comprehensive approach to technology-assisted learning.\u003c/p\u003e\n\u003cp\u003eOpenAI\u0026apos;s GPT-3 signifies a substantial advancement in the domain of language modelling, demonstrating sophisticated capabilities in producing text that emulates human language and in comprehending complex linguistic nuances.Its capacity to produce coherent, contextually relevant content makes it an excellent tool for stimulating creativity and fostering critical thinking among students (Mhlanga, 2023). By encouraging experimentation with diverse writing styles and concepts, GPT-3 can facilitate both creative and academic writing endeavors. The collective impact of these AI tools underscores the transformative capacity of artificial intelligence in enhancing students\u0026apos; writing abilities.\u003c/p\u003e\n\u003ch3\u003e2.3. AI-Driven Platforms and Their Multifaceted Impact on EFL Learners\u003c/h3\u003e\n\u003cp\u003eMhlanga(2023) examined the functionality of ChatGPT as a virtual tutor, with a focus on its capacity to facilitate goal setting and provide interactive guidance. Notwithstanding its advantages, they sounded a note of caution about the potential pitfalls of overreliance, which could result in superficial engagement with learning materials. In a similar vein, Mogavi et al. (2024) underscored the potential benefits of personalized learning offered by AI tools, while concurrently highlighting the associated challenges, including diminished critical thinking and concerns over academic integrity. To address these concerns, Ali et al. (2023) have proposed that learners engage in self-assessment to cultivate independence from technological reliance.\u003c/p\u003e\n\u003cp\u003eIn a collaborative study, Wiboolyasarin et al. (2024) demonstrated that artificial intelligence (AI)-assisted corrective feedback significantly augmented second-language (L2) writing competencies among Thai exchange students. The research under discussion highlights the profound impact that such tools can have when applied thoughtfully. In a similar vein, Hsu et al. (2023) observed that ChatGPT led to substantial enhancements in grammar, writing proficiency, and vocabulary acquisition among foreign language learners. Nevertheless, they also cautioned about its possible drawbacks, particularly in terms of its potential to limit creativity and critical thinking skills.\u003c/p\u003e\n\u003cp\u003eSubsequent research by Wei (2023) corroborated the findings, demonstrating that AI-mediated teaching not only enhanced English learners\u0026apos; academic performance but also fortified their motivation and self-regulation skills. Additionally, Karataş et al. (2024b) examined incorporating AI-powered image recognition technology into vocabulary acquisition, reporting reduced anxiety and enhanced knowledge retention, although it did not notably influence self-regulation.\u003c/p\u003e\n\u003cp\u003eVanichvasin et al. (2021) demonstrated that AI tools play a significant role in enhancing EFL learners\u0026apos; grammar proficiency and overall writing performance. They further highlighted the facilitation of increased student engagement in academic writing tasks by AI feedback tools. Nevertheless, Kim (2016) and Vladova et al., (2023) noted that despite students\u0026apos; favorable views on the utility of AI in grammar correction and plagiarism detection, ethical and creative challenges persist.\u003c/p\u003e\n\u003cp\u003eA study by Xu and Wang (2024c) synthesised data from 40 studies, thereby reinforcing the effectiveness of AI-integrated learning tools in improving English learning outcomes. This finding is consistent with the research conducted by Guo and Wang (2024), which identified that AI-enhanced instruction positively impacts learner engagement across a range of cognitive, emotional, and social dimensions. Furthermore, Mozumder et al. (2023) explored the role of AI tools in fostering intrinsic motivation, noting improvements in learner autonomy and critical thinking. However, they also observed that the impact varied based on factors such as nationality and academic discipline. Collectively, these studies underscore the multifaceted benefits and potential challenges of integrating AI technologies into EFL education.\u003c/p\u003e\n\u003cp\u003eIn consideration of the pivotal role of the variables examined, this study endeavors to address the following research inquiries:\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eHow do AI-driven dynamic writing platforms contribute to the improvement of EFL learners\u0026apos; writing skills and motivation compared to traditional methods?\u003c/li\u003e\n \u003cli\u003eWhat are EFL learners\u0026apos; perceptions of the effectiveness of AI-powered writing activities in enhancing their writing skills and fostering motivation?\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"3. Method","content":"\u003ch3\u003e3.1. Research Design\u003c/h3\u003e\n\u003cp\u003eThe present study adopted a sequential explanatory mixed-methods design, utilising a systematic progression from quantitative to qualitative analysis, with the objective of investigating the research questions. The process commenced with the collection and analysis of quantitative data to evaluate how AI-powered interactive writing platforms influenced EFL learners\u0026apos; writing abilities and motivation. Subsequent to this quantitative phase, semi-structured interviews were conducted with participants to gain deeper insights into the findings and enhance understanding of the phenomenon. The combination of these approaches was deemed essential for a comprehensive examination of the impact of technology on various aspects of writing performance, including coherence, vocabulary, grammatical accuracy, and task achievement. Furthermore, it enabled an exploration of the variations in learner motivation and experiences. The integration of quantitative and qualitative methods was a deliberate strategy, with the aim of providing a comprehensive and nuanced analysis that would underpin the study\u0026apos;s research objectives.\u003c/p\u003e\n\u003ch3\u003e3.2. Participants\u003c/h3\u003e\n\u003cp\u003eThe present study focused on a group of 65 undergraduate students specializing in Teaching English as a Foreign Language (TEFL) at the Islamic Azad University, Varamin-Pishva branch. These participants, enrolled in intermediate-level writing courses, were selected using convenience sampling. The participants\u0026apos; ages ranged from 18 to 25, ensuring a relatively homogenous age group. To qualify for participation, students needed to meet specific criteria, including verification of their intermediate English proficiency through placement tests, an absence of prior experience with AI-assisted language tools, and voluntary submission of informed consent forms.\u003c/p\u003e\n\u003cp\u003eParticipants were randomly divided into two groups: 33 students were assigned to the AI-driven interactive writing platform group, while the remaining 32 were placed in the classroom-based writing group. The randomization process was meticulously executed to ensure that the allocation of groups was unbiased and maintained parity between the two cohorts.\u003c/p\u003e\n\u003cp\u003eThe demographic variables were well-balanced across the groups. The AI group included 15 males and 18 females, while the traditional group comprised 14 males and 18 females. On average, both groups reported similar educational backgrounds, with participants having received 5 to 7 years of formal English education. This equitable distribution was instrumental in reducing potential confounding factors related to prior academic exposure.\u003c/p\u003e\n\u003cp\u003eThe study\u0026apos;s rigorous sampling methodology ensured a valid comparison between the two groups, thereby facilitating an effective exploration of the impact of AI-driven interventions on EFL learners\u0026apos; writing skills and motivation within a controlled yet representative educational environment.\u003c/p\u003e\n\u003ch3\u003e3.3. Materials and Instruments\u003c/h3\u003e\n\u003ch4\u003e3.3.1. AI-Powered Writing Platform\u003c/h4\u003e\n\u003cp\u003eThis study employed an artificial intelligence-powered interactive writing platform that has been specifically developed to enhance learners\u0026apos; writing skills and engagement. The platform provided immediate feedback on various writing components, including coherence, vocabulary selection, grammatical correctness, and overall task completion. Furthermore, it offered customized recommendations and corrections, meticulously tailored to address the learners\u0026apos; distinct writing requirements, thus fostering enhanced self-regulation and autonomy in their writing process.\u003c/p\u003e\n\u003ch4\u003e3.3.2. Writing Performance Assessment\u003c/h4\u003e\n\u003cp\u003eThe assessment of the participants\u0026apos; writing skills entailed the implementation of IELTS-style writing tasks, utilized as both pre-tests and post-tests. These tasks were designed to evaluate key aspects of writing performance, including coherence and cohesion, vocabulary usage, grammatical accuracy, and task completion. The evaluation of these components was guided by the IELTS writing band descriptors. Scores for each component ranged from 1 to 9, with the overall score being the average of the four categories. To ensure the reliability and accuracy of the scoring process, two experienced raters independently evaluated the participants\u0026apos; written work. The inter-rater reliability was subsequently calculated using the Pearson correlation coefficient, yielding a substantial score of 0.87, thereby demonstrating the consistency of the evaluations.\u003c/p\u003e\n\u003ch4\u003e3.3.3. Motivation Scale\u003c/h4\u003e\n\u003cp\u003eIn this research, a carefully adapted motivation scale was utilized to evaluate EFL learners\u0026apos; motivation to engage in writing activities. The scale, comprising 20 items rated on a 7-point Likert scale (ranging from 1 = strongly disagree to 7 = strongly agree), measured key motivational aspects such as interest, self-confidence, and independence. Administered in English to correspond with the language used in the AI platform, the scale aimed to track shifts in learner motivation throughout the intervention.\u003c/p\u003e\n\u003cp\u003eThe scale demonstrated strong reliability, with a Cronbach\u0026apos;s alpha of 0.89, indicating robust internal consistency. Despite the limited sample size, which hindered the comprehensive validation of the instruments, the psychometric properties of the scale were substantiated by prior research. A pilot study conducted prior to the main investigation affirmed the appropriateness of these tools for assessing both writing proficiency and motivation. The findings of this preliminary investigation indicated that the instruments consistently yielded meaningful and valid responses, thereby validating their relevance and effectiveness in achieving the objectives of the study.\u003c/p\u003e\n\u003ch4\u003e3.3.4. Semi-Structured Interviews\u003c/h4\u003e\n\u003cp\u003eIn order to gain deeper insights into the manner in which participants interacted with the AI-based interactive writing platform, semi-structured interviews were conducted with nine members of the AI group (see Appendix A). The aim of these interviews was to explore participants\u0026apos; perceptions regarding the platform\u0026apos;s impact on their writing abilities and motivation. The qualitative data gathered through these interviews complemented the quantitative findings, providing a more comprehensive understanding of the results. The interview questions were meticulously crafted to elicit information regarding the underlying factors that contributed to the AI group\u0026apos;s superior performance compared to the group using conventional classroom writing techniques.\u003c/p\u003e\n\u003cp\u003eThe interview protocol was developed through a comprehensive approach, which included an in-depth review of relevant literature, expert consultations, and a pilot phase to ensure the clarity and appropriateness of the questions. The final set of open-ended questions was designed to explore participants\u0026apos; experiences with the platform, their perceived progress in writing skills, and the impact of the AI tool on their motivation. This qualitative method yielded valuable insights into the key factors that contributed to the AI group\u0026apos;s enhanced performance in writing tasks.\u003c/p\u003e\n\u003cp\u003eThe interviews were conducted in English to align with the language used in the AI-driven writing tasks, although participants had the option to switch to Persian if they faced challenges expressing themselves in English. This flexibility helped ensure smooth communication, allowing participants to fully share their experiences. In some cases, participants chose to answer specific questions in Persian, highlighting the importance of accommodating diverse linguistic needs. The interviews lasted between 20 to 35 minutes, offering ample time for thoughtful and detailed responses. To maintain confidentiality, participants were given pseudonyms (e.g., P1, P2, \u0026hellip;, P9), ensuring privacy while safeguarding the integrity of the data.\u003c/p\u003e\n\u003ch3\u003e3.4. Ethical Considerations\u003c/h3\u003e\n\u003cp\u003eThe research was conducted in strict accordance with established ethical standards and received formal approval from the Institutional Review Board (IRB) at Islamic Azad University, Varamin-Pishva Branch. This approval ensured the protection of participants\u0026apos; rights and welfare, with various critical ethical safeguards implemented to uphold these principles throughout the study: \u0026nbsp;\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\u003cstrong\u003eInformed Consent\u003c/strong\u003e\u003cstrong\u003e\u003cspan dir=\"RTL\"\u003e:\u0026nbsp;\u003c/span\u003e\u003c/strong\u003eThe participants were provided with a comprehensive consent form that elucidated the objectives of the research, the methodology employed, and the potential risks and benefits associated with their involvement. Participants were granted sufficient time to meticulously deliberate and arrive at a voluntary decision regarding their involvement.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eConfidentiality and Data Security\u003c/strong\u003e: The anonymity of all participants was preserved, and the data was stored in encrypted systems, accessible only to authorized individuals with appropriate clearance.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eTransparency and Participant Awareness\u003c/strong\u003e:The participants were provided with exhaustive information regarding the potential benefits of the study, including the opportunity to enhance their writing skills and motivation, as well as other available learning resources.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eTo address the ethical concerns associated with the comparative experimental design, which had the potential to result in unequal experiences for the AI and traditional instruction groups, the following steps were taken:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\u003cstrong\u003eBalanced Learning Opportunities:\u003c/strong\u003e Both groups participated in the same duration of instructional sessions and were given access to a wide range of writing exercises and supporting materials to maintain fairness in their educational experiences.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eComprehensive Feedback\u003c/strong\u003e: Following the conclusion of the study, all participants received detailed, personalized assessments of their writing abilities, designed to foster ongoing development irrespective of their group allocation.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eParticipant Support and Autonomy\u003c/strong\u003e: The well-being of the participants was closely monitored throughout the study, with any emerging issues addressed promptly. Additionally, participants were free to withdraw from the study at any time without facing negative consequences.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e3.5. Data Collection Procedures\u003c/h3\u003e\n\u003cp\u003eAt the inception of the study, participants completed a preliminary evaluation to ascertain their initial writing skills and motivation levels. The pretest encompassed a standardized IELTS writing test and a motivation scale. Both groups underwent a 12-week instructional period with identical content, with the exception of the integration of an AI-driven writing platform in the experimental group.\u003c/p\u003e\n\u003ch4\u003e3.5.1. Artificial Intelligence Group\u003c/h4\u003e\n\u003cp\u003eThe participants of the AI group engaged with an interactive AI writing platform designed to enhance their writing skills. Each participant was required to allocate a minimum of 20 minutes daily to utilize the platform and attend two hours of weekly classroom instruction. A monitoring system was implemented to ensure adherence to the prescribed regimen. This system revealed that 85% of participants met the daily requirement, while the remaining 15% engaged for an average of 15 minutes per day.\u003c/p\u003e\n\u003cp\u003eThe platform offered customized, real-time feedback and corrections based on the learners\u0026apos; input. Supplementary resources such as grammar exercises, vocabulary-building tasks, and practice tests supplemented the in-class learning experience.\u003c/p\u003e\n\u003cp\u003eIn the classroom, the AI group engaged in a variety of interactive writing activities, including peer feedback sessions, collaborative projects, and instructor-led writing exercises. These tasks were meticulously designed to cultivate a variety of writing competencies, including coherence, structural integrity, grammatical accuracy, and the effective use of vocabulary. The instructional materials were meticulously designed to be both stimulating and achievable, thereby fostering gradual skill enhancement within a positive and encouraging learning environment.\u003c/p\u003e\n\u003cp\u003eDespite the AI platform\u0026apos;s lack of real-time interaction, it served as a complementary component to classroom activities by offering consistent, individualized practice. The integration of AI-based and classroom-based activities ensured learners had multiple avenues to develop and refine their writing skills.\u003c/p\u003e\n\u003ch4\u003e3.5.2. Classroom-Based Group\u003c/h4\u003e\n\u003cp\u003eThe classroom-based group received an equivalent instructional duration and content as the AI group; however, they did not utilize the AI writing platform. Instead, their instruction was delivered in a traditional classroom setting by the researcher/instructor. The instructional program encompassed structured lessons on grammar, vocabulary development, and writing practice. Participants were expected to allocate two hours of weekly classroom instruction, in addition to homework assignments and quizzes designed to reinforce their writing skills.\u003c/p\u003e\n\u003cp\u003eDuring class, the classroom-based group engaged in similar writing activities as the AI group, including collaborative tasks, guided writing sessions, and peer reviews. They were provided with identical practice materials, including worksheets and exercises, focusing on grammar, coherence, and vocabulary to strengthen their writing abilities.\u003c/p\u003e\n\u003cp\u003eTo account for the differences in task structure, the classroom-based group completed weekly assignments that were more comprehensive and aligned with the daily tasks assigned to the AI group. These assignments were meticulously designed to ensure that the learning objectives remained consistent across both groups, despite the variation in task frequency.\u003c/p\u003e\n\u003cp\u003eAfter the 12-week instructional period, both groups underwent identical posttests, which included a standardized IELTS writing assessment and a motivation scale, to evaluate their progress. The posttests mirrored the pretests to ensure the comparability of results. Furthermore, semi-structured interviews were conducted with the AI group in order to obtain qualitative feedback on their interactions with the platform and its effects on their writing skills and motivation.\u003c/p\u003e\n\u003ch3\u003e3.6. Data analysis\u003c/h3\u003e\n\u003ch4\u003e3.6.1. Quantitative Analysis\u003c/h4\u003e\n\u003cp\u003eIn the analysis of the writing tasks completed by both groups, continuous scores were assigned for both the pretest and posttest. The normality of the data distribution was evaluated using Kolmogorov-Smirnov tests, and given the absence of outliers, parametric statistical techniques were applied for the analysis of the quantitative data. In order to evaluate the influence of the independent variables (AI-mediated vs. classroom-based interactions) on participants\u0026apos; writing skills and motivation to engage in writing tasks, paired sample t-tests were utilized. Furthermore, one-way analyses of covariance (ANCOVAs) were employed to examine group differences in writing performance and writing time (WT). Covariates that were deemed relevant were incorporated into the analysis with a view to controlling for baseline differences, ensuring an equitable comparison.\u003c/p\u003e\n\u003ch4\u003e3.6.2. Qualitative Analysis\u003c/h4\u003e\n\u003cp\u003eThe interview data were transcribed and analysed using Braun and Clarke\u0026apos;s (2012) thematic analysis approach. The process commenced with an initial coding phase, during which key themes related to learners\u0026apos; perceptions and attitudes toward the AI-supported writing course were identified. These themes were then organised into broader categories, each of which was meticulously labelled to reflect the core concepts. To ensure the consistency and reliability of the coding, inter-rater agreement was evaluated using Cohen\u0026apos;s Kappa coefficient. To ensure the robustness of the findings, a randomly selected subset of transcripts was independently coded by both the primary researcher and an EFL expert. The resulting Cohen\u0026apos;s Kappa value of 0.82 indicated substantial agreement between the coders, and any inconsistencies were resolved through collaborative discussions to improve the accuracy of the analysis.\u003c/p\u003e"},{"header":"4. Results","content":"\u003ch3\u003e4.1. Quantitative Analysis\u003c/h3\u003e\n\u003cp\u003eTo assess the mean scores of EFL learners\u0026apos; writing skills before and after the intervention, descriptive statistics were computed, as summarized in \u003cstrong\u003eTable\u003c/strong\u003e \u003cstrong\u003e1\u003c/strong\u003e. It highlights the descriptive statistics for pretest and posttest writing skill scores across both AI-mediated and classroom-based groups.In the group using AI mediation,the mean scores on the pretest were as follows: fluency (5.98), vocabulary (5.45), accuracy (6.18), and overall writing performance (5.41).After the intervention, the posttest means increased to 6.82, 6.63, 6.98, and 6.84, respectively.In contrast, the classroom-based group exhibited initial means of 5.89 (fluency), 5.46 (lexicon), 6.87 (accuracy), and 4.99 (total writing). Following the intervention, the posttest means for these categories increased to 5.99, 5.81, 6.71, and 5.97, respectively.An analysis of the posttest outcomes shows that the AI group exhibited greater advancements in fluency, vocabulary, and total writing scores when compared to the classroom-based group. However, the classroom-based group demonstrated a slightly higher pretest mean in accuracy, though the posttest scores in this category slightly decreased.\u003c/p\u003e\n\u003cp\u003eTo determine whether these changes were statistically significant within the AI group, paired sample t-tests were performed. \u003cstrong\u003eTable 2\u003c/strong\u003e presents the results, demonstrating significant improvements in most writing skill components.The fluency score demonstrated a marked increase, rising from a mean of 6.32 to 6.93, with a significant difference of 0.86 (t = 3.41, p = 0.03). A similar enhancement was observed in the lexicon score, which increased from 5.88 to 6.87, exhibiting a substantial mean difference of 1.65 (t = 6.45, p \u0026lt; 0.01). Accuracy scores also exhibited a significant increase, rising from 7.12 to 7.81, reflecting a mean difference of 0.88 (t = 4.05, p \u0026lt; 0.01).\u003c/p\u003e\n\u003cp\u003eTheresults of the paired-sample t-test, as outlined in \u003cstrong\u003eTable 3\u003c/strong\u003e, demonstrate alterations in mean writing skill scores for the classroom-based group from pretest to posttest. A slight increase in fluency scores was observed, with the mean rising from 5.81 to 5.99, resulting in a mean difference of 0.14, which did not reach statistical significance (t = 0.77, p = 0.45). In contrast, a significant enhancement was observed in lexicon scores, which increased from a mean of 5.28 to 6.42, with a mean difference of 0.34 (t = 3.31, p = 0.03). Although accuracy scores exhibited a modest rise from 6.51 to 6.74, this change was not statistically significant (t = 0.96, p = 0.36).\u003c/p\u003e\n\u003cp\u003eThe findings of the paired-sample t-test demonstrate that the AI-supported group has achieved statistically significant advancements in fluency, lexicon, accuracy, and coherence. In contrast, the classroom-based group has exhibited notable progress solely in the domain of lexicon scores. These findings underscore the efficacy of AI-assisted interventions in promoting comprehensive enhancements in the writing skills of EFL learners.\u003c/p\u003e\n\u003cp\u003eTo further validate these results, a one-way analysis of variance (ANOVA) was performed to examine both the statistical significance (p \u0026lt; 0.05) and the practical impact (effect size or partial eta squared, \u0026eta;p\u0026sup2; \u0026gt; 0.14) of differences between the instructional approaches. According to the criteria established by \u0026eta;p\u0026sup2;, values exceeding 0.14 are indicative of a substantial effect, values ranging from 0.06 to 0.14 denote a moderate effect, and values from 0.01 to 0.06 reflect a negligible effect.\u003c/p\u003e\n\u003cp\u003eAs outlined in \u003cstrong\u003eTable 4\u003c/strong\u003e, a one-way ANCOVA was conducted, with pre-test fluency scores incorporated as covariates. The findings revealed a substantial benefit for the AI-assisted group, which attained higher fluency levels in comparison to the classroom-based group (F(1, 62) = 7.43, p = 0.01, \u0026eta;p\u0026sup2; = 0.15). This effect size is noteworthy and underscores the substantial impact of the AI intervention in enhancing fluency.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5\u0026nbsp;\u003c/strong\u003epresents the findings of a one-way ANCOVA performed to assess lexicon scores, with pretest results serving as covariates. The analysis demonstrated a marked advantage for the AI-assisted group, which achieved significantly higher mean lexicon scores compared to the classroom-based group (F(1, 62) = 29.46, p \u0026lt; 0.01, \u0026eta;p\u0026sup2; = 0.41). This robust effect underscores the significant impact of AI integration on improving learners\u0026apos; lexical proficiency.\u003c/p\u003e\n\u003cp\u003eIn a similar vein, \u003cstrong\u003eTable 6\u003c/strong\u003e presents the findings of the one-way ANCOVA for accuracy. The results of this analysis indicated a significant distinction between the two instructional approaches, as the AI group outperformed the Classroom-Based group in terms of accuracy scores (F(1, 62) = 76.12, p \u0026lt; 0.01, \u0026eta;p\u0026sup2; = 0.61). Additionally, pretest accuracy scores, as covariates, demonstrated a significant impact on the results (F(1, 62) = 518.21, p \u0026lt; 0.01, \u0026eta;p\u0026sup2; = 0.89), highlighting the considerable variance accounted for by prior performance.\u003c/p\u003e\n\u003cp\u003eAs illustrated in \u003cstrong\u003eTable\u003c/strong\u003e \u003cstrong\u003e7\u003c/strong\u003e, the findings from the ANCOVA results (F(1, 62) = 29.07, p \u0026lt; .001, \u0026eta;p\u0026sup2; = .46) demonstrate a substantial effect of group membership on the overall writing scores. The findings collectively underscore those learners in the AI-driven instructional group exhibited significantly higher levels of performance in all domains of writing skills , including fluency and coherence, lexical resource, grammatical accuracy, and pronunciation, when compared with those in the face-to-face setting. Of particular note is the finding that these results were obtained after adjusting for pretest scores, underscoring the efficacy of AI-based interventions in promoting writing skills.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 8\u0026nbsp;\u003c/strong\u003epresents the descriptive statistics for Willingness to Write (WTW) scores in both groups before and after the intervention. In the pre-intervention phase, the AI group reported a higher average WTW score (M = 3.57, SD = 0.71) compared to the classroom-based group (M = 3.26, SD = 0.61). Following the intervention, the AI group demonstrated a substantial increase in their mean score, which rose to 4.16 (SD = 0.81). In contrast, the classroom-based group exhibited a more modest improvement, attaining a mean score of 3.58 (SD = 0.61). These results underscore the considerable impact of AI-driven pedagogical approaches in enhancing engagement in writing tasks among learners.\u003c/p\u003e\n\u003cp\u003eTo further explore this disparity, a one-way ANCOVA was conducted to assess the improvements in WTW between the two instructional methods. As demonstrated in \u003cstrong\u003eTable 9\u003c/strong\u003e, the findings indicated that the AI group exhibited considerably higher posttest WTW scores in comparison to the classroom-based group (F(1, 62) = 29.16, p \u0026lt; 0.001, \u0026eta;p\u0026sup2; = 0.42).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e\u003cstrong\u003e.\u0026nbsp;\u003c/strong\u003e\u003cem\u003eDescriptive Statistics of Writing skills Scores for AI and Classroom-Based Groups\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 31.1953%;\"\u003e\n \u003cp\u003eGroup\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 24.1983%;\"\u003e\n \u003cp\u003eWriting skills\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 4.6647%;\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.2449%;\"\u003e\n \u003cp\u003ePertest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.2274%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 15.4519%;\"\u003e\n \u003cp\u003ePost-test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.0175%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.2449%;\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.2274%;\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.4519%;\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.0175%;\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 31.1953%;\"\u003e\n \u003cp\u003eAI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.1983%;\"\u003e\n \u003cp\u003eFluency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.6647%;\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.2449%;\"\u003e\n \u003cp\u003e5.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.2274%;\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.4519%;\"\u003e\n \u003cp\u003e6.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.0175%;\"\u003e\n \u003cp\u003e1.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 31.1953%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 24.1983%;\"\u003e\n \u003cp\u003eLexicon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.6647%;\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.2449%;\"\u003e\n \u003cp\u003e5.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.2274%;\"\u003e\n \u003cp\u003e.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.4519%;\"\u003e\n \u003cp\u003e6.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.0175%;\"\u003e\n \u003cp\u003e.79\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 31.1953%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 24.1983%;\"\u003e\n \u003cp\u003eAccuracy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.6647%;\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.2449%;\"\u003e\n \u003cp\u003e6.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.2274%;\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.4519%;\"\u003e\n \u003cp\u003e6.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.0175%;\"\u003e\n \u003cp\u003e.94\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 31.1953%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 24.1983%;\"\u003e\n \u003cp\u003eTotal Writing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.6647%;\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.2449%;\"\u003e\n \u003cp\u003e5.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.2274%;\"\u003e\n \u003cp\u003e.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.4519%;\"\u003e\n \u003cp\u003e6.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.0175%;\"\u003e\n \u003cp\u003e.64\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 31.1953%;\"\u003e\n \u003cp\u003eClassroom-Based\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.1983%;\"\u003e\n \u003cp\u003eFluency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.6647%;\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.2449%;\"\u003e\n \u003cp\u003e5.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.2274%;\"\u003e\n \u003cp\u003e.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.4519%;\"\u003e\n \u003cp\u003e5.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.0175%;\"\u003e\n \u003cp\u003e.79\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 31.1953%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 24.1983%;\"\u003e\n \u003cp\u003eLexicon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.6647%;\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.2449%;\"\u003e\n \u003cp\u003e5.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.2274%;\"\u003e\n \u003cp\u003e.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.4519%;\"\u003e\n \u003cp\u003e5.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.0175%;\"\u003e\n \u003cp\u003e.84\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 31.1953%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 24.1983%;\"\u003e\n \u003cp\u003eAccuracy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.6647%;\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.2449%;\"\u003e\n \u003cp\u003e6.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.2274%;\"\u003e\n \u003cp\u003e.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.4519%;\"\u003e\n \u003cp\u003e6.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.0175%;\"\u003e\n \u003cp\u003e.90\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 31.1953%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 24.1983%;\"\u003e\n \u003cp\u003eTotal writing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.6647%;\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.2449%;\"\u003e\n \u003cp\u003e4.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.2274%;\"\u003e\n \u003cp\u003e.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.4519%;\"\u003e\n \u003cp\u003e5.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.0175%;\"\u003e\n \u003cp\u003e.82\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u0026nbsp;\u003c/strong\u003e\u003cem\u003ePaired sample T-test Results for Pretest and post-test writing Skills Scores in the AI Group\u003c/em\u003e\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eWriting skills\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMean (Pertest)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMean (Post-test)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMean Difference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eStd. Deviation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003et-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFluency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLexicon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.\u003cspan dir=\"RTL\"\u003e45\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAccuracy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan dir=\"RTL\"\u003e4\u003c/span\u003e.\u003cspan dir=\"RTL\"\u003e05\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3.\u0026nbsp;\u003c/strong\u003e\u003cem\u003ePaired Sample t-test Results for Pretest and Posttest Writing Skills Scores in the Classroom-Based Group\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eWriting skills\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003emean (Pertest)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMean (Post-test)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMean Difference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eStd. Deviation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003et-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFluency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLexicon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAccuracy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4.\u0026nbsp;\u003c/strong\u003e\u003cem\u003eComparison of Writing Fluency Between the AI Group and Classroom-Based Group\u003c/em\u003e\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSource\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eType III sum of squares\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003edf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMean square\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePartial eta squared\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePre-fluency (covariates)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e22.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGroups\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5.\u0026nbsp;\u003c/strong\u003e\u003cem\u003eComparison of Lexical Scores Between the AI Group and Classroom-Based Group\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSource\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eType III sum of squares\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003edf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMean square\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePartial eta squared\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePre-lexicon (covariates)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e14.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan dir=\"RTL\"\u003e14\u003c/span\u003e.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e72.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.56\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGroups\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cspan dir=\"RTL\"\u003e5\u003c/span\u003e.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e29.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.41\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6.\u0026nbsp;\u003c/strong\u003e\u003cem\u003eComparison of Writing Accuracy Between the AI Group and Classroom-Based Group\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSource\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eType III sum of squares\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003edf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMean square\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSig.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePartial eta squared\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePre-accuracy (covariates)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e33.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e33.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e518.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.89\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGroups\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e76.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.61\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 7.\u0026nbsp;\u003c/strong\u003e\u003cem\u003eComparison of Total Writing Performance Between the AI Group and Classroom-Based Group\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSource\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eType III sum of squares\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eDf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMean square\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSig.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePartial eta squared\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePre-total Writing (covariates)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e81.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGroups\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e29.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.46\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTable 8.\u003c/em\u003e\u003c/strong\u003e\u003cem\u003e\u0026nbsp;Descriptive Statistics of Pretest and Posttest WTW Scores for AI and Classroom-Based Groups\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"648\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGroup\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eStd. Deviation\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eStd. Error Mean\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePre-WTW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAI\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eClassroom-Based\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePost-WTW\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eClassroom-Based\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 9.\u0026nbsp;\u003c/strong\u003e\u003cem\u003eComparison of WTW Scores Between the Two Groups\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSource\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eType III sum of squares\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003edf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMean square\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSig.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePartial eta squared\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePre-WTW (covariates)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e31.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e31.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e186.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.73\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGroups\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e29.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch3\u003e4.2. Qualitative Analysis\u003c/h3\u003e\n\u003cp\u003eIn order to examine the reasons for the enhanced effectiveness of AI-mediated instruction in improving writing skills and motivation in EFL learners when compared to traditional classroom approaches, semi-structured interviews were conducted with nine participants from the AI-supported writing group. Thematic analysis was employed to explore the participants\u0026apos; perspectives on the interactive AI platform, illuminating its perceived benefits and the challenges encountered during its utilization.\u003c/p\u003e\n\u003cp\u003eA recurrent theme that emerged was the personalization afforded by the AI-mediated tools. Participants frequently highlighted how the platforms offered tailored feedback and guidance aligned with their individual proficiency levels and learning needs. Many students expressed appreciation for the ability of the AI to identify specific writing challenges and provide instant, targeted feedback, which facilitated their progress significantly. For instance, one student articulated their experience by stating:\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u0026quot;The AI provided feedback that was customized to my writing level, helping me address my weaknesses more effectively. It also corrected my mistakes promptly, enabling me to make immediate improvements.\u0026quot;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAnother salient theme that emerged pertained to the low-pressure environment engendered by the AI-mediated platforms. Students characterized the virtual interface as a non-judgmental space where they could engage in practice without the concern of criticism or embarrassment. This environment was conducive to enhancing their engagement with writing tasks and fostering their confidence. As one participant articulated,\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u0026quot;Writing with the AI platform felt less intimidating than traditional peer reviews or instructor feedback. I could experiment with different styles and ideas without worrying about making mistakes.\u0026quot;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eLearners indicated that the utilization of AI tools for writing tasks had a considerable impact on enhancing fluency and confidence. It was emphasized by the learners that the diverse writing prompts and interactive exercises offered by these platforms contributed to significant improvements in grammar and vocabulary. Furthermore, the tools were regarded as instrumental in fostering creativity and bolstering self-confidence in their writing abilities. As one participant remarked:\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u0026quot;The AI exercises helped me refine my grammar and vocabulary while also making me more confident in my ability to write fluently and coherently.\u0026quot;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFurthermore, the learners expressed appreciation for the diversity of activities provided by the AI-driven platform. They emphasized that these platforms integrated writing tasks with complementary exercises, such as grammar drills and vocabulary-building activities, which enriched their learning experience. One student offered the following comment:\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cem\u003e\u0026quot;The platform included a range of exercises, from brainstorming and drafting to vocabulary enrichment and editing tasks. This diversity kept the process engaging and helped me improve across multiple dimensions of writing.\u0026quot;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eHowever, not all feedback was unequivocally positive. A subset of learners indicated a predilection for human instructors, attributing the value of nuanced, contextually rich feedback to a quality that AI systems might be deficient in providing. Additionally, participants noted that while AI responses were generally beneficial, occasional inaccuracies or irrelevant suggestions undermined their learning experience. As one participant noted,\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u0026quot;Although the AI tool was useful, it sometimes failed to understand the context of my writing or gave feedback that didn\u0026rsquo;t align with my goals. In such cases, I missed having a teacher\u0026rsquo;s guidance.\u0026quot;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eIn sum, the findings of the present study demonstrate that AI-driven interactive writing platforms have a positive impact on learners by offering personalized feedback, fostering a supportive learning environment, and providing varied and engaging activities. However, it is important to note the limitations of these platforms, which include a lack of contextual depth and occasional errors in AI responses. These limitations underscore the ongoing significance of human interaction in the context of EFL writing instruction.\u003c/p\u003e\n\u003ch4\u003e4.2.1.Qualitative Insights\u003c/h4\u003e\n\u003cp\u003eWhile many EFL learners found the AI-driven platforms beneficial, some expressed a preference for human instructors, citing the distinct advantages of human interaction. These learners noted that human instructors were more engaging, capable of delivering detailed and contextually rich feedback, and adept at creating a realistic communicative environment. Furthermore, they placed a high value on the cultural insights and nuanced context imparted by human instructors, which they believed enriched their overall learning experience. One participant offered the following remark:\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u0026quot;I believe human instructors are better because they provide more detailed feedback and keep the learning process engaging. They create a realistic environment for communication and add cultural context to the language, which AI cannot fully replicate.\u0026quot;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNotwithstanding the merits of the AI-mediated platforms, certain participants identified deficiencies in their functionality. Specifically, participants noted that AI responses were, on occasion, either irrelevant or inaccurate, thereby detracting from the learning experience. For instance, a learner commented:\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u0026quot;The Chatbot isn\u0026rsquo;t always precise. Sometimes, its responses don\u0026rsquo;t make sense or fail to align with the flow of the conversation.\u0026quot;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe themes derived from this qualitative analysis indicate that AI-driven interactive platforms had a predominantly positive impact on learners\u0026apos; attitudes and perceptions. These platforms were particularly appreciated for their ability to provide customized feedback, create stress-free practice environments, and offer a variety of activities that promoted fluency and self-confidence. Moreover, the instantaneous feedback provided by AI was identified as a pivotal benefit, enabling the swift identification and correction of errors.\u003c/p\u003e\n\u003cp\u003eHowever, a segment of learners emphasized the irreplaceable value of human interaction. These individuals noted that human instructors provide more comprehensive feedback and maintain motivation through engaging and dynamic interaction. Furthermore, they acknowledged the limitations of AI tools in handling nuanced or culturally specific contexts and highlighted occasional errors in the AI-generated feedback.\u003c/p\u003e\n\u003cp\u003eIn summary, while AI-mediated platforms offer innovative and effective solutions for language learning, human interaction remains a critical component for addressing the social, cultural, and contextual aspects of language acquisition.\u003c/p\u003e"},{"header":"5. Discussion","content":"\u003cp\u003eThis research, grounded in Engestr\u0026ouml;m\u0026apos;s (1987) activity theory, examined the impact of AI-driven interactive writing platforms on the writing skills and motivation to engage in writing tasks among EFL learners. The findings indicated that these AI-mediated platforms significantly enhanced both aspects. Consistent with these observations, subsequent studies have documented analogous trends. For instance, research by Zhao (202\u003cspan dir=\"RTL\"\u003e2\u003c/span\u003e) and Liand Chan (2024) has underscored the efficacy of AI-based systems in enhancing language skills and instilling confidence in learners. One plausible explanation for these advancements is the supportive and judgment-free environment AI creates, allowing learners to experiment and refine their writing without fear of criticism. This observation is corroborated by Wu and Zhang (2022).\u003c/p\u003e\n\u003cp\u003eIn the current digital era, characterized by the pervasive influence of technology across various domains, including gaming, social interaction, and knowledge acquisition (Huang et al., 2023), there has been a notable increase in the adoption of AI tools by learners for academic purposes. AI-powered writing platforms offer a conducive environment for learners to practice, thereby reducing anxiety about making errors and enhancing their confidence in writing. Research has demonstrated that these tools effectively reduce writing anxiety (Chen et al., 2023) while enhancing coherence, vocabulary, and grammatical precision. Consequently, learners are empowered to continue their education beyond traditional classroom settings. Moreover, Tambunan et al. (2023) underscore the pivotal role of AI platforms in fostering self-directed learning, a notion that finds congruence with the findings of the present study.\u003c/p\u003e\n\u003cp\u003eEngestr\u0026ouml;m\u0026apos;s activity theory offers a valuable framework for comprehending these outcomes. According to this theory, learning occurs within a complex system comprising interconnected elements such as the subject, the object, tools, rules, community, and the division of labor (Engestr\u0026ouml;m, 1987). In the context of this study, the AI-powered writing platform functioned as a mediating instrument, thereby facilitating structured interactions through the provision of automated prompts and tailored feedback.This scaffolding, as posited by Wang et al. (2023), fostered learners\u0026apos; incremental development in writing skills , concurrently enhancing their self-efficacy and motivation.\u003c/p\u003e\n\u003cp\u003eThe division of labor within this system was evident as learners actively engaged with the platform by responding to tasks, while the AI system provided guidance and feedback. The community dimension involved interactions within a broader network of learners using similar platforms, fostering a collaborative and supportive environment. Lio et al., (2023) emphasize that such ecosystems mitigate anxiety and cultivate confidence, a finding that is corroborated by this study. These findings position AI-driven interactive writing platforms as transformative tools in language education, offering accessible and highly effective opportunities for learners to develop writing skills in a supportive, low-stress context.\u003c/p\u003e\n\u003cp\u003eHowever, participants noted that while these platforms offer numerous benefits, they sometimes fail to match the depth of human instruction. In accordance with the observations made by Fryer and Carpenter in 2006, the limitations of AI systems pertain to the difficulty in accurately interpreting complex or nuanced written expressions. This challenge is especially pronounced for novice learners, as AI platforms frequently encounter difficulties in comprehending and responding to their intended meanings. Furthermore, learners identified the absence of authentic human interaction as a crucial element in fostering socio-cognitive and psychological support during writing tasks.\u003c/p\u003e\n\u003cp\u003eThe successful utilization of AI platforms is contingent upon access to adequate technological infrastructure, including reliable devices and high-speed internet, which can pose practical challenges. Prior studies have identified specific limitations of AI-based systems, including limited contextual understanding (Kim et al., 2020), inconsistent accuracy in responses (Lu et al., 2006), and difficulty managing complex inputs (Ayedoun et al., 2015). These findings underscore the necessity to remediate these deficiencies to enhance the efficacy of AI platforms in language learning contexts.\u003c/p\u003e\n\u003cp\u003eWhile AI-driven writing platforms offer substantial advantages for EFL learners, the findings indicate that they should complement, rather than replace, human instruction. A hybrid approach, integrating AI tools with conventional teaching methods, has the potential to generate a more comprehensive and enriching learning experience that addresses both the technological constraints and learners\u0026apos; socio-cognitive needs.\u003c/p\u003e"},{"header":"6. Conclusion","content":"\u003cp\u003eThis study offers substantial evidence of the transformative potential of AI-driven dynamic writing platforms in the context of EFL learning. Through the utilization of AI-mediated tools, which possess adaptive and interactive capabilities, learners demonstrated quantifiable advancements in their writing skills across various dimensions, including fluency, coherence, lexical diversity, and grammatical accuracy. Furthermore, these platforms have been observed to elicit a substantial enhancement in learners\u0026apos; motivation to engage in writing tasks, thereby mitigating the disparity between the acquisition of technical writing skills and the fostering of affective engagement.\u003c/p\u003e\n\u003cp\u003eA distinctive feature of this research is its emphasis on the dual impact of AI-mediated platforms, which not only enhance the technical aspects of writing but also cultivate a positive attitude toward writing as a creative and collaborative process. In contrast to conventional classroom-based methods, the AI-driven model provides customized feedback, real-time adjustments, and dynamic scaffolding, thereby empowering learners to overcome individual challenges and progress at their own pace.\u003c/p\u003e\n\u003cp\u003eFrom a macro perspective, this study contributes to the advancement of the field of computer-assisted language learning (CALL) by demonstrating the potential of artificial intelligence (AI) to enhance linguistic skills and promote learner autonomy. The findings underscore the significance of integrating such technologies into language education, providing educators with novel methodologies for developing more interactive and impactful writing programs.\u003c/p\u003e\n\u003cp\u003eFurthermore, this research underscores the pressing need for further investigation into the evolving role of AI in reshaping the learning experience. By fostering intrinsic motivation and offering personalized learning opportunities, AI platforms signify a shift in EFL education, empowering learners to become active participants in their educational journeys rather than passive recipients of knowledge. Future research should explore ways to further optimize these platforms for diverse educational settings and investigate their long-term effects on writing skills and learner motivation.\u003c/p\u003e\n\u003cp\u003eIn conclusion, AI-powered interactive writing platforms function not only as tools for enhancing language skills but also as catalysts for fostering a more engaged, autonomous, and confident generation of EFL learners. By leveraging the capabilities of AI, educators can transform the language learning experience, rendering it more personalized, motivating, and better suited to the demands of global communication.\u003c/p\u003e\n\u003ch3\u003e6.1. Practical Implications for the EFL Context\u003c/h3\u003e\n\u003cp\u003eThe findings of this study offer valuable insights and practical applications for various stakeholders, including EFL learners, educators, teacher trainers, and researchers. The study underscores the transformative function of AI-driven writing platforms in fostering learners\u0026apos; engagement with writing tasks and enhancing their overall proficiency. These platforms offer a multifaceted and interactive environment for practice, emphasizing key areas such as grammatical accuracy, vocabulary expansion, and the development of well-structured texts. A particularly salient feature of AI-based instruction is its capacity to deliver customized feedback, catering to the distinct requirements of individual learners while fostering a supportive and personalized learning environment.\u003c/p\u003e\n\u003cp\u003eAdditionally, the study underscores the significance of an environment devoid of stress in fostering language development. AI-mediated platforms offer learners the autonomy to progress at their own pace, unencumbered by the immediate pressure of assessment. This approach fosters a sense of confidence and encourages the development of fluency and creativity in written expression. By offering a learner-centered framework, AI-driven platforms have the potential to transform the traditional dynamics of writing instruction, making the process more engaging and effective for EFL learners.\u003c/p\u003e\n\u003cp\u003eMoreover, by circumventing the limitations imposed by time constraints and large class sizes that are characteristic of traditional classroom settings, EFL learners can leverage AI-driven tools for uninterrupted writing practice. These platforms empower learners to extend their writing practice into informal contexts, fostering autonomy in addressing challenges and developing their skills.\u003c/p\u003e\n\u003cp\u003eFor EFL instructors, the integration of AI-driven platforms as supplementary tools in writing courses offers learners additional opportunities to practice and refine their skills. By integrating these AI-driven tools with conventional interactive writing tasks, instructors can foster a more comprehensive and supportive learning environment. Teacher educators and instructors are encouraged to train learners and their peers in the effective use of AI platforms for a variety of writing-related activities.\u003c/p\u003e\n\u003cp\u003eThe findings underscore the necessity for additional research to be conducted on the implementation of AI-driven writing platforms in language education. The development of more advanced systems capable of delivering highly personalized and adaptive feedback has the potential to further enhance their utility for EFL learners. These advancements would contribute to a more profound understanding of how AI-mediated tools can support language acquisition and inspire future innovations in language learning technology.\u003c/p\u003e\n\u003ch3\u003e6.2. Limitations and Recommendations for Future Research\u003c/h3\u003e\n\u003cp\u003eAs with numerous studies in this field, this research encountered several limitations that future studies in EFL could address. Firstly, the relatively small sample size may not provide a full representation of the diverse EFL learner population. In order to enhance the generalizability of the results, subsequent research should endeavor to incorporate larger, more heterogeneous sample groups, potentially examining various demographic factors such as age, proficiency levels, and cultural backgrounds. Furthermore, replicating the study in a range of educational contexts, such as different countries or academic settings, could help confirm the broader applicability of the findings.Secondly, the study\u0026apos;s limited duration constrained the exploration of the long-term implications of AI-based instruction on learners\u0026apos; writing development and motivation. To build on this, future investigations should consider extended study periods, allowing for a deeper exploration of how prolonged exposure to AI-driven platforms influences writing skills, sustained motivation, and learner autonomy over time. Furthermore, examining the cumulative effects of AI interventions in various phases (e.g., short-term vs. long-term engagement) could yield a more comprehensive understanding of their overall impact.\u003c/p\u003e\n\u003cp\u003eA critical constraint in the present study stemmed from the amalgamation of instructional strategies within the experimental group, wherein participants encountered both AI-supported and conventional writing exercises. This methodological detail introduces a potential confounding factor, which complicates the direct attribution of the observed enhancements in writing proficiency and motivation to the AI-driven platforms alone. To address this challenge, future investigations should aim for a more rigorously controlled experimental setup, wherein the precise impact of AI interventions can be isolated from other pedagogical approaches. Such an approach would serve to enhance the internal validity of the research and facilitate a more precise delineation of the unique contributions of AI technologies in fostering writing skills and motivating EFL learners.\u003c/p\u003e\n\u003cp\u003eIt is imperative to acknowledge that this study was conducted within a specific context, which may constrain its generalizability to diverse educational settings.Consequently, subsequent investigations could assess the efficacy of AI-mediated writing platforms across diverse contexts, such as higher education, professional training programs, or multilingual learning environments. This would provide insights into how different settings influence the outcomes of AI-driven writing instruction.\u003c/p\u003e\n\u003cp\u003eThis study focused on a specific AI-driven platform, which may limit the applicability of findings to other tools. Subsequent research endeavors may entail the exploration of a more extensive array of AI-driven writing systems, with the objective of assessing their potential in enhancing writing skills and motivation across a range of learner demographics. By examining various AI tools, researchers can ascertain whether the observed benefits are consistent across platforms or unique to certain systems.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch3\u003eAuthor Contributions\u003c/h3\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eThe author was responsible for the conceptualization, methodology, investigation, writing of the original draft, and writing - review and editing of the manuscript. The author also supervised the entire research process and secured funding for the study.\u003c/p\u003e\n\u003ch3\u003eFunding\u003c/h3\u003e\n\u003cp\u003eThis research received no external funding.\u003c/p\u003e\n\u003ch3\u003eData Availability\u003c/h3\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eThe data that support the findings of this study are available from the author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest:\u0026nbsp;\u003c/strong\u003eThe author declares that there is no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate\u003c/strong\u003e\u003cstrong\u003e\u003cspan dir=\"RTL\"\u003e: \u003c/span\u003e\u003c/strong\u003eInformed consent was obtained from all participants involved in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication:\u0026nbsp;\u003c/strong\u003eThe author consents to the publication of this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of Supporting Documents:\u0026nbsp;\u003c/strong\u003eThe supporting data and materials are available upon request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003estochastic parrots Can language models be too big? 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Comput Assist Lang Learn. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/09588221.2023.2278608\u003c/span\u003e\u003cspan address=\"10.1080/09588221.2023.2278608\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Islamic Azad University of Varamin","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"AI-Driven Writing, Writing skills, Motivation to Write, EFL Learners, Mixed-Methods Study, Technology-Enhanced Language Teaching","lastPublishedDoi":"10.21203/rs.3.rs-5788599/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5788599/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAI-powered platforms for dynamic writing present transformative opportunities to enhance English language acquisition and actively engage learners in writing tasks. However, the role of artificial intelligence in improving writing proficiency and fostering motivation among English as a Foreign Language (EFL) learners remains an area that has yet to be thoroughly investigated. This research endeavor seeks to address the prevailing knowledge gap in the field by exploring the influence of AI-powered writing platforms on the writing skills of EFL learners. The study's objectives encompass the assessment of the impact of these platforms on coherence, vocabulary usage, grammatical accuracy, task accomplishment, and learner motivation. Utilizing a mixed-methods approach, the study examined 65 intermediate EFL students from the Islamic Azad University, Varamin-Pishva branch, who were divided into two groups: one employed AI-powered tools, while the other followed traditional classroom-based writing exercises. Quantitative data were gathered via IELTS-based assessments and motivation scales, while qualitative insights were derived from semi-structured interviews. The findings revealed substantial enhancements in the AI group across all measured dimensions in comparison to the control group. Additionally, the AI group demonstrated a significant surge in motivation levels. Learners in the AI group reported positive attitudes toward AI-based instruction, citing improvements in engagement, autonomy, and confidence in their writing. The platform also fostered greater self-regulation and personalized learning experiences, which participants found effective and enjoyable. The findings emphasize the efficacy of AI-based writing platforms in enhancing linguistic proficiency and motivational levels among EFL learners. The study provides practical insights for incorporating AI technologies into writing instruction, encouraging educators to leverage such tools for more effective and engaging language learning practices.\u003c/p\u003e","manuscriptTitle":"Exploring the Role of AI-Driven Dynamic Writing Platforms in Improving EFL Learners' Writing Skills and Fostering Their Motivation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-09 08:36:39","doi":"10.21203/rs.3.rs-5788599/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8278a919-cff6-4e48-ac6f-5d9f55e0cd0c","owner":[],"postedDate":"January 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":42554615,"name":"Special Education"},{"id":42554616,"name":"Artificial Intelligence and Machine Learning"}],"tags":[],"updatedAt":"2025-01-09T08:36:39+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-09 08:36:39","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5788599","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5788599","identity":"rs-5788599","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}