The Effect of Gemini on EFL Learners' Writing Skills and Motivation

The Effect of Gemini on EFL Learners' Writing Skills and 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 The Effect of Gemini on EFL Learners' Writing Skills and Motivation Miew Luan Ng, Ali Al Ghaithi, Behnam Behforouz This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6428474/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 The present study is intended to assess the impact of Gemini on the writing abilities of English as a Foreign Language (EFL) learners and their motivation. 60 Omani learners were divided randomly into treatment and control groups, each with 30 learners. The two groups were given researcher-made writing pretests, posttests, delayed posttests, and an adapted version of motivation questionnaire to compare students' performance in both variables before and after the treatment in both groups. The instrument's reliability and validity were carefully tested and approved. Both groups got textual correction input from their teachers, but the treatment group had the opportunity to engage with Gemini to receive extra automated feedback for their work. The findings revealed that initially, the control group progressed from the pretest to the posttest of writing but dramatically decreased in the delayed postttest of writing; however, learners in the treatment group outperformed those in the control group on the writing posttest and delayed posttest. Furthermore, it was shown that learners' motivation level was higher than the control group's. The study's findings are valuable for educators, learners, and curriculum creators. AI Feedback Gemini Writing Skills Innovation Motivation Introduction Writing helps students to ponder on a sure thing, synthesize their views, and then present them in a piece of writing for interaction between those who read and the writer of the work. Thus, a writer must utilize language accurately, construct the piece of writing clearly, and identify flaws to prevent misunderstanding by the reader (Bitchener & Ferris, 2012 ; Khadawardi, 2020 ). Some research has expressly stated that writing competence is one of the most challenging language learning and education areas. If the learner's proficiency level is inadequate, the skills involved with writing tasks become noticeably more difficult. The primary issue is whether to ignore or correct the defects in the text (Banaruee, 2016 ; Richards & Renandya, 2002 ). Feedback is generally recognized as a critical instrument for promoting language acquisition, greatly assisting learners in developing their abilities, refining the learning process, and extending their comprehension (Banihashem et al., 2022 ). According to Banaruee and Askari ( 2016 ), the efficiency of feedback systems in improving students' skills is not assured. According to Han ( 2008 ), corrective feedback is a broad word that refers to general suggestions and changes, while mistake repair requires some direct and explicit input. According to Leki ( 2001 ), providing corrective comments on pupils' writing by teachers and pupils to rectify errors is time-consuming in a foreign language setting. Choosing appropriate methods to deliver corrective feedback on writing talents is one component that contributes to this problem. Scholars are urged to apply the most effective feedback approaches to help students with the practical execution, revision, and correction of their writing. Written feedback has been very beneficial in enhancing and raising the correctness of L2 learners' written work. To attain this purpose, instructors must be well-trained in delivering timely and constructive feedback to learners to assist them in reducing the negative feelings associated with getting feedback (Mao & Lee, 2020 ). However, providing individualized and relevant feedback to diverse students may be time-consuming and psychologically draining. Educators often get bored owing to the repetitious process of analyzing individual compositions, but foreign language learners' views on self-growth have a considerable impact on the dynamics of offering and receiving writing feedback (Teng & Teng, 2024 ). To address this demanding circumstance, Artificial Intelligence (AI) may be regarded as a helpful tool, as this fresh and revolutionary technology can significantly reduce instructors' excessive burden and provide tailored feedback to enhance active learning (Kasneci et al., 2023 ). A wide variety of AI solutions have evolved to improve student performance; for example, machine learning algorithms are used to estimate individual grades by identifying particular patterns based on linguistic components in the tutoring information. This makes it easier to evaluate new works that contain similar tasks following such patterns (Ercikan & McCaffrey,2022). Moreover, AI enables real-time feedback, providing students with immediate and targeted advice while optimizing and simplifying administrative procedures to guarantee smooth and effective operations (Amin et al., 2025 ). Previous research has shown that automatic writing feedback (AWF) can accurately evaluate and correctly assess articles equivalent to trained human evaluators (Ramesh & Sanampudi, 2022 ). According to research, dynamic feedback that adapts to students' talents and weaknesses based on AWE ratings may considerably improve writing and modification performances (Fleckenstein et al., 2023 ; Horbach et al., 2022 ) as well as effectiveness (Huang & Wilson, 2021 ). Despite substantial studies on the use of Web 2.0 technologies in L2 investigation, there seems to be little examination of the effectiveness of predictive texts in fostering L2 writing. There are very few empirical studies on text prediction and AI writing instruments. As a result, it is critical to study the potential applications of automated written feedback as an auxiliary tool in the learning environment to improve learners' writing skills (Frankenberg-Garcia, 2020 ). Additionally, despite various studies examining the impacts of instructor or automated feedback on learners' writing abilities, there is a lack of thorough research on the beneficial effects and critical potential of combining both instructor and automated feedback (Yuan & Kim, 2018 ). As a result, this study aimed to assess the effect of employing Gemini as an AI tool to measure its impact on the writing abilities of EFL learners and the learners' motivation in their engagement with AI tools. Therefore, this research will extensively analyze the subsequent two questions: To what extent does the integration of Gemini as an AI-assisted tool improve the writing performance of Omani EFL learners? What effect does the use of Gemini have on the motivation levels of Omani EFL learners toward writing tasks? Literature Review Automated written corrective feedback WCF is corrective feedback on language correctness offered to English students (Bitchener and Knoch, 2010 ; Ellis et al., 2006 ). Recent investigations of English writing and technology have concentrated on using internet-based tools to provide WCF, resulting in the development of AWCF as an innovative writing framework. AWCF is defined as WCF delivered automatically by a web-based system to assist students with language-related concerns in their writing versions. AWCF recognizes learners' blunders and errors in punctuation, spelling, grammar, and writing norms, offers rapid feedback on students' language-related concerns, and allows them enough time to amend their writing drafts. Students are also given accurate and precise metalinguistic clarifications, which assist them in improving their writing ability and help teachers deal with the time constraints of collaborative and process-oriented writing exercises. Furthermore, by offering immediate feedback on writing faults and errors, AWCF assists students in identifying and addressing language-related difficulties more effectively and quickly (Lee, 2017 ). Additionally, the introduction of generative AI, such as ChatGPT and Microsoft Copilot, has raised the requirement that AWF use these advanced big language models (LLM) to give more accurate and comprehensive feedback (Barrot, 2023 ). Automated corrective feedback (ACF) is system-generated feedback on student-written content. Several ACF algorithms are often utilized in language learning (Chen et al., 2016 ; Li et al., 2016 ). ACF has sparked substantial attention among instructors and researchers because of its many educational benefits, including improving language learners' writing quality by recognizing and correcting errors. For example, ACF technology may identify and highlight inconsistencies in written text, allowing students to understand their mistakes easily. In addition, ACF devices provide suggestions for correcting them. Both students and instructors may benefit from adopting ACF tools. For example, ACF tools give quick feedback, allowing for rapid correction of writing mistakes (Li et al., 2016 ). Waer ( 2023 ) used an experimental approach to investigate how AWE affected EFL students' writing anxiety and their grammatical range and correctness. 103 learners of EFL were divided into a treatment and a control group at random using a randomly assigned, true-experimental research method. The treatment group used Cambridge English's Write & Improve internet-based tools to give learners AWE on their writing assignments. To assist students in completing a writing assignment (an essay, report, or letter) correctly, the Write & Improve system assigns each student to a suitable level (beginners A1 and A2, intermediate B1 and B2, and advanced C1 and C2) using the Common European Framework of Reference (CEFR) dimension. A grammar knowledge exam was used to gather the necessary data, and the results showed that learners who received AWE scored better in terms of grammatical correctness and range than those who did not. Wang et al. ( 2013 ) investigated how AWE affected the writing correctness, autonomy, and engagement of students learning EFL. Treatment and control groups of fifty-seven first-year EFL learners were randomly assigned. The treatment group used the online tool CorrectEnglish to assist learners in enhancing their writing using AWE, whereas the control group used the traditional, teacher-centered strategy to improve the pupil's writing ability. Learners' writing styles, proper word use, and grammar patterns are all evaluated by CorrectEnglish. Additionally, this system provides learners with instant feedback on their writing achievement, including content, organization, style, focus, and general achievement in writing. The necessary quantitative information was gathered using a written composition exam and a questionnaire for self-reporting, while the qualitative data was collected via a semi-structured interview. According to the findings of the quantitative analysis, learners who used AWE fared better than those in the non-AWE class in terms of interaction, independence, and writing correctness. The qualitative research also revealed the students' favorable opinions about using AWE to enhance their writing precision and self-directed learning. Saricaoglu and Bilki (2021) investigated how EFL learners used Criterion, a digital AWCF system, in their writing assignments and how much AWCF increased their writing correctness. Criterion provides students with real-time feedback while working on their assignments and after submitting their finished drafts to the site. To assist students in improving their future writing achievement, Criterion also summarises their writing proficiency report, including the number of words, phrases, and mistakes they made. 114 EFL students from five different classrooms consented to participate in the research. The students were urged to submit their written work to Criterion and resolve any writing problems in light of the comments and feedback provided by Criterion. According to the instrument's usage monitoring results and achievement summary report collection, some students refused to utilize Criterion to complete their writing drafts. Nonetheless, the student's participation in AWCF improved their writing correctness and reduced their mistakes in their final copy. Barrot ( 2023 ) used a quasi-experimental study design to investigate how AWCF utilizing Grammarly affected the writing quality and mistakes of 65 students learning English. Ten argumentative essay assignments, ranging from 200 to 300 words, were given to the treatment and control groups. While the control group employed non-AWCF in their writing activities, the treatment students utilized Grammarly to get AWCF for their writing assignments and enhance their writing appropriately. Similar 200–300 words argumentative essay assignments were used to gather the necessary pretest and posttest data. Since Grammarly may offer instantaneous metalinguistic justifications and improve students' observing and autonomous functioning, the findings showed that Grammar-based AWCF enhanced the students' writing fluency. Motivation in ESL/EFL writing When it comes to improving writing abilities, motivation is crucial (Ebadijalal & Moradkhani, 2023 ). Self-confidence, interest, perceived task value, and views are only a few of the interrelated elements that make up the complex concept of motivation (Troia et al., 2013 ). Self-confidence and writing interest are two important elements that might affect young writers' skills (Bai et al., 2022 ). Writing self-confidence has been shown in many research studies to be a significant predictor of writing achievement, with higher levels of self-efficacy linked to improved writing results in addition to increased effort, persistence, and utilization of SRL techniques on tasks related to writing (Rahimi & Fathi, 2022 ; Teng & Zhang, 2020 ). Another essential motivating element that affects students' involvement with writing assignments and the standard of the work they produce is their intrinsic pleasure in writing. According to studies, students driven by genuine enthusiasm often write better and are more likely to stick with a project despite difficulties (Ebadijalal & Moradkhani, 2023 ). To foster enduring personal interest in writing, Renninger and Hidi ( 2022 ) highlighted the significance of contextual interest sparked by particular activities or settings. For instance, Boo et al. ( 2015 ) discovered that EFL students who were given the freedom to choose their themes and were exposed to interesting prompts for writing had improved their ability to write and intrinsic motivation. According to Zimmerman ( 2000 ), more motivated students are more likely to employ self-regulated learning (SRL) methods, and motivation may be increased by using these tactics. According to MacArthur et al. ( 2016 ), self-efficacy and perseverance were higher among ESL students who actively used SRL methods. Similarly, Bai et al. (2024) discovered that among problematic EFL writers in Hong Kong elementary schools, the adoption of SRL strategies was positively correlated with motivation components (interest, self-efficacy, and development mindset). Writing self-confidence and writing SRL techniques both significantly contribute to estimating the level of writing competency among Chinese EFL undergraduates (Sun & Wang, 2020). Song and Song ( 2023 ) used a mixed-methods approach to study the influence of AI-assisted language acquisition on writing abilities and motivation in 50 Chinese EFL learners. The project was designed with a control group getting conventional training and a treatment group using AI technologies, notably ChatGPT. The results indicated that the AI-assisted group demonstrated substantial increases in writing abilities and increased writing motivation among treatment group participants. Suciati et al. ( 2024 ) investigated the effectiveness of AI-assisted technologies, especially ChatGPT, in increasing enthusiasm and inspiration in English writing among 72 Indonesian language learners. Researchers compared conventional interactive learning approaches to ChatGPT-supported exercises in a controlled experimental setting. The results revealed substantial increases in both enthusiasm and involvement within the ChatGPT group, emphasizing AI technologies' potential to enhance educational experiences by promoting a more dynamic and customized setting for learning. Tajik ( 2025 ) evaluated the effect of AI-powered dynamic writing systems on EFL students' writing abilities and motivation. The research included 65 intermediate learners separated into two groups: one employing artificial intelligence (AI) resources and the other employing conventional techniques. The results revealed considerable gains in writing abilities and improved motivation in the AI group, showing the platforms' usefulness in improving writing competency and student motivation. Silitonga et al. ( 2023 ) examined the effect of AI chatbot-based acquiring on pupil inspiration in English writing courses. The research used a mixed-methods approach, with 73 undergraduates separated into two groups: a control group receiving conventional training and a treatment group receiving AI chatbots, ChatGPT. The results showed that the AI chatbot-based group had much higher writing motivation than the control group, implying that artificial intelligence instruments such as ChatGPT may successfully boost learners' enthusiasm and drive them to learn English writing. Method Participants The population being investigated included 50 Omani EFL learners randomly allocated to control and experimental groups, 20 learners. The students' mother tongue was Arabic, and their ages were between 18 and 19. The university's assessments revealed that these students had pre-intermediate English language proficiency. The Foundation Department has no clear criteria for rigorously controlling the participants' ages and genders. The participants in the experience group received input from both Gemini and the instructor to clarify Gemini's remarks, while the control group received feedback solely from their instructor. The General Foundation Program (GFP) is a one-year program in three consecutive 12-week semesters that prepares the students for their specialization, focusing on English, Math, and Computing Skills modules, following Oman's national curriculum. Admission to college in Oman requires completion of the GFP program. Instrumentation The necessary data was gathered using the following tools. Writing Pretest, Posttest, and Delayed Posttest Because the primary goal of this research was to assess the influence of Gemini on the learners' writing abilities, a writing pretest was conducted to ensure all the learners have similar writing skills. Later on, a posttest was performed due to the comparative nature of the study, and to measure the impact of treatment on the experimental group. Finally, to measure the role of Gemini in long-term knowledge retention, another delayed posttest was conducted on both groups of students. Each writing assignment requires learners to write 100 words regarding a compare and contrast essay. The standards for both assessments were Task Achievement, Organization, Grammar, and Vocabulary. Each exam had a total score of 20 points, with 5 scores indicated for every single category. Table 1 below shows the questions selected as pretest, posttest, and delayed posttest. Table 1 The Pretest, Posttest, and the Delayed Posttest of Writing Pretest: Online Learning vs. Classroom Learning Instructions : Write a 100-word paragraph comparing and contrasting online learning and classroom learning. Discuss at least one similarity and one difference. Use linking words, however , on the other hand , and both . Posttest: City Life vs. Village Life Instructions : In about 100 words, compare life in a city and a village. Mention one way they are similar and at least one way they are different. Make sure your writing is clear and organized. Delayed Posttest: Reading Books vs. Watching Movies Instructions : Write a short paragraph (100 words) comparing and contrasting reading books and watching movies. Describe one similarity and one difference. Use comparison words like in contrast , likewise , unlike , and while . Gemini AI-powered Google created Gemini, an AI-powered writing facilitator that can help in writing jobs. While it was initially designed for programming, its language processing features make it an ideal tool for educational settings, especially for assisting student writing growth. Gemini identifies grammatical errors, awkward phrasing, and syntactical issues, providing immediate corrections with explanations that facilitate learning. It offers recommendations to improve clarity, coherence, and conciseness, aligning with academic writing conventions and disciplinary expectations. The assistant learns from user interactions and adjusts its recommendations based on previous edits and preferences, becoming increasingly personalized with continued use. Gemini can modify its output to match various discourse styles, from formal academic writing to creative narratives, helping students develop genre awareness and flexibility. Gemini is accessible via a separate internet interface rather than texting apps. Using the web platform rather than a separate app provides multiple appealing benefits for students. There is no need to install extra software since Gemini works with any standard web browser; the writing aid service is instantly accessible from a familiar online environment. This technique is especially beneficial for students who use various devices and may encounter compatibility concerns with specific programs. The link to the web platform uses students' existing digital activities since they commonly utilize browsers for academic research and writing, making it simple for them to include Gemini into their educational workflow. The web interface's ability to work across several devices and operating systems further supports its adoption as the distribution platform. Eliminating additional registration steps reduces potential barriers, allowing students to access the service immediately with their existing Google accounts. Furthermore, this strategy is resource-efficient; students may access the service using their existing internet connections without needing unique app-based data allocations, eliminating the need for further financial investment in new software or specialized apps. The web-based solution also suits the academic environment's requirement for bigger screen sizes when dealing with lengthy papers, resulting in a more thorough writing experience than would be available via chat services. Writing Motivation Scale The motivation questionnaire was developed by Bai et al. ( 2022 ), from which the statements were collected, and the modifications were applied to prepare them to precisely measure learners' motivation in using AI tools to receive feedback. The Motivation section included writing self-efficacy (5 statements) and interest (4 statements). Students had 15 minutes to complete the questionnaire; the statements were translated into Arabic to help the process. Arranged according to the 5-point Likert Scale, the statements included 5 (strongly agree), 4 (agree), 3 (neutral), 2 (disagree), and 1 (strongly disagree). A pilot study was conducted to assess the dependability of the questionnaire with 25 Omani EFL students inside the same university and English proficiency level before the main round of research. .835 for the dependability index might be seen as rather dependable. Three PhD candidates in Applied Linguistics with significant professional expertise evaluated the questionnaire to guarantee that the Arabic version of the scale followed comparable language standards, structure, and style. An impartial moderator then evaluated the poll to ensure its accuracy using inquiry moderation and the application of intellectual and cultural values. Procedures This research was carried out during the second semester of 2025–2026 at one of Oman's universities. After acquiring all necessary clearances from authorities and students, writing and motivation pretests were performed to verify that the learner's writing skills were consistent. The treatment duration was set to three weeks. Before applying the treatment, one of the researchers led a training workshop for the group receiving treatment in the initial seven days to teach them how to use Gemini to obtain feedback on their work. The study's investigator taught the students to use particular cues to elicit input from the Gemini. To complete the research in three weeks, the researcher used the entire two hours of English courses for four days while the students attended a four-hour daily English session. The total number of lectures was twelve, and writing classes were held four days a week, and each instruction lasted 2 hours. During the introductory writing lesson, the treatment and control groups were given a two-hour description of how the essays should be structured. The lecturer then issued a task for both groups of students to work on outside of class. The students were expected to submit the homework the next day. The experimental group's students were then expected to examine the first draft employing the Gemini using the procedures provided by the investigator. The updated work had to be provided via email or Microsoft Teams to give the teacher more clarity and rationale during future writing sessions. The class teacher commented using Microsoft Word's "Track Changes" function. To maintain the natural structure of the feedback approach, the instructor was asked not to limit comments on language or content-related issues. The Gemini program and website were used for e-feedback, and students were asked to send their essays as messages to Gemini after implementing the precise instructions presented by the investigator throughout the training course. The pupils got comments on their writing drafts, which they might use as a guideline to edit their work before sending it to the instructor by email or Microsoft for final input. At the same time, the control group got feedback from the instructor using the traditional method throughout all of the classes. The twelve writing activities were updated after the third week. Following the study period, both groups completed posttests to compare their results. In addition, the posttest of motivation scale was supplied to both groups to assess the level of writing motivation when intelligent technology was used as an intermediary in the English language acquisition process. Data Analysis This section investigates the collected data by applying statistical analysis using SPSS 27.0 to the results of student scores in the writing tests and the motivation scale. The first step before selecting a test of comparison was to measure the condition of the data distribution to choose a suitable parametric or nonparametric test. Table 2 below shows the results of the Kolmogorov-Smirnov Normality Test in 3 sets of writing tasks. Table 2 The Results of the Normality Test for Pretest, Posttest, and Delayed Posttest in Both Groups groups Kolmogorov-Smirnov Statistics df Sig. pretest control .163 25 .084 experiment .118 25 .200 posttest control .142 25 .200 experiment .150 25 .149 delayed posttest control .154 25 .129 experiment .200 25 .011 Most of the data sets' results in Table 2 showed that the normality assumption was satisfied. Specifically, the control and experimental groups showed regularly distributed scores in the pretest (p = .084 and p = .200, respectively) and posttest phases (p = .200 and p = .149, respectively). The control group's delayed posttest scores also followed a normal distribution (p = .129). On the other hand, the experimental group's delayed posttest results revealed a notable departure from normality (p = .011), suggesting a nonnormal distribution. This result implies that although most comparisons could call for parametric testing, one should be careful when looking at the delayed posttest scores of the experimental group, as nonparametric tests could be more appropriate. Since the data sets for the control group were expected, a parametric test of the Paired-Sample Test was conducted to determine the performance of students within the control group from pretest to posttest to delayed posttest, and the results can be seen in Table 3 below. Table 3 shows that the comparison between the pretest and posttest scores demonstrates a statistically significant improvement, with a mean difference of -2.68 (p < .001), demonstrating that the subjects performed substantially better following the intervention. On the other hand, the pretest and delayed posttest comparison showed no statistically significant difference (mean difference = -0.08, t(24) = -0.153, p = .880), suggesting that the improvements seen in the posttest were not maintained over time. Conversely, the comparison between the posttest and the delayed posttest showed a statistically significant decrease (mean difference = 2.60, t (24) = 4.572, p < .001), suggesting a loss in performance following the first improvement. These findings imply that although the program produced quick improvement, the impact was lessened by the time of the postponed posttest. Table 4 below shows learners' performance in the experimental group in all the 3 sets of writing tests. Since the students of the experimental group in the delayed posttest showed a nonnormal distribution of data, a nonparametric sign test was selected for this comparison. Table 4 The Results of the Sign Test for the Pretest, Posttest, and Delayed Posttest in the Experimental Group posttest - pretest delayedposttest - pretest delayedposttest - posttest Exact Sig. (2-tailed) .001 .001 .003 Using the binomial distribution, the Sign Test findings in Table 4 show statistically significant variations across all three comparisons. Indicating a substantial increase in performance after the intervention, the posttest scores were much higher than the pretest scores (Exact Sig. (2-tailed) < .001). Likewise, the postponed posttest results revealed a statistically notable increase over the pretest results (Exact Sig. (2-tailed) < .001), implying that the noted improvements persisted with time. Moreover, the difference between the posttest and delayed posttests produced a statistically significant finding (Exact Sig. (2-tailed) = .003), suggesting a substantial shift between these two phases. These results show that the program had a consistent and statistically significant impact on student performance. Table 5 below shows the Marginal Homogeneity Test to provide more detailed information on the experimental group results. Table 5 The Marginal Homogeneity Test pretest & posttest posttest & delayedposttest Distinct Values 12 7 Off-Diagonal Cases 25 23 Observed MH Statistic 271.000 365.000 Mean MH Statistic 335.000 381.500 Std. Deviation of MH Statistic 13.928 5.123 Std. MH Statistic -4.595 -3.220 Asymp. Sig. (2-tailed) .000 .001 The outcomes of Table 5 confirm even more the statistically significant variations seen throughout the evaluation rounds. The test showed an observed MH statistic of 271.000 when comparing the pretest and posttest, significantly lower than the mean MH statistic of 335.000, producing a normalized MH statistic of -4.595. This discrepancy led to an asymptotic significance value of .000, suggesting a notable variation in answer distributions between the pretest and posttest phases. Likewise, the posttest and delayed posttest comparison revealed an apparent MH statistic of 365.000 vs a mean MH statistic of 381.500, resulting in a normalized MH statistic of -3.220. .001's related asymptotic significance value verifies a statistically significant change in response patterns between the two test events. Based on 12 and 7 different values in the two comparisons and from 25 and 23 off-diagonal situations, these results support the notion that participants' performance changed significantly over time. The Marginal Homogeneity Test, therefore, supports the reading that the noted variations were not caused by chance. The final statistical analysis of the writing compares both groups on the assigned writing tasks. Table 6 below shows the findings of ANOVA in 3 sets of writing tasks. Table 6 The Results of Writing Tasks in the Pretest, Posttest, and Delayed Posttest between Both Group Sum of Squares df Mean Square F Sig. Effect size pretest Between Groups .080 1 .080 .027 .870 0.00057 Within Groups 141.200 48 2.942 Total 141.280 49 posttest Between Groups 69.620 1 69.620 22.739 .000 0.3214 Within Groups 146.960 48 3.062 Total 216.580 49 delayed posttest Between Groups 492.980 1 492.980 148.787 .000 0.7558 Within Groups 159.040 48 3.313 Total 652.020 49 Table 6 shows that at the pretest phase, there was no statistically significant difference between the groups, F(1, 48) = 0.027, p = .870, with a small effect size (η² = 0.00057), suggesting that the participants started from an equal baseline. But in the posttest, a statistically significant difference appeared, F (1, 48) = 22.739, p < .001, with a considerable effect size (η² = 0.3214). This suggests a significant influence on learning results, as almost 32% of the variation in posttest scores could be ascribed to the intervention. With an extraordinarily high effect size (η²= 0.7558), the delayed posttest showed a statistically significant difference, F(1, 48) = 148.787, p < .001. Reflecting its considerable and lasting impact, the intervention explains over 76% of the variation in delayed posttest results. The second part of this section investigates the performance of students in both groups based on the motivation scale. Before selecting the appropriate inferential test, the data distribution condition was measured using the Shapiro-Wild Nomrlaity Test. Table 7 below shows the results of the data distribution. Table 7 The Results of the Normality Test in Pretest and Posttest of the Self-Efficacy groups Shapiro-Wilk Statistics df Sig. pretotal control .887 25 .010 experiment .838 25 .001 posttotal control .887 25 .010 experiment .876 25 .006 Table 7 revealed that the total self-efficacy score distribution was far from normal across all groups and time points. In the pretest, the control group (W = 0.887, p = .010) and the experimental group (W = 0.838, p = .001) revealed nonnormal distributions. Similarly, during the posttest, the control group (W = 0.887, p = .010) and experimental group (W = 0.876, p = .006) exhibited significant deviation from normality. This result suggests that nonparametric tests are preferable in investigating differences between self-efficacy scores. Table 8 below shows the results of the Mann-Whitney U Test in comparing the pretest and posttest of both groups. Table 8 The Results of Comparison between Two Groups in Pretest and Posttest of Self-Efficacy Time Group N Mean Rank Sum of Ranks Mann–Whitney U Wilcoxon W Z Asymp. Sig. (2-tailed) Pretest Control 25 30.98 774.50 175.500 500.500 -2.817 .005 Pretest Experimental 25 20.02 500.50 Posttest Control 25 13.00 325.00 0.000 325.000 -6.139 < .001 Posttest Experimental 25 38.00 950.00 Table 8 shows significant differences between the self-efficacy scores of the control and experimental groups in both instances. In the pretest, the control group had higher self-efficacy, as seen by a greater mean rank (30.98) compared to that of the experimental group (20.02), with the difference being statistically significant (U = 175.500, Z = -2.817, p = .005). However, this was reversed in the posttest, when the experimental group scored a much larger mean rank (38.00) than the control group (13.00). This differed significantly (U = 0.000, Z = -6.139, p < .001), indicating a significant increase in the experimental group's self-efficacy following treatment, with the control group having no comparable increase. These findings validate the intervention's positive influence on learners' writing self-efficacy. The statement of self-efficacy was further measured to understand students' choices better. Table 9 below shows the results of these statistics. Table 9 The Descriptive Statistics of Self-Efficacy Statements within the Experimental Group poststatement1 poststatement2 poststatement3 poststatement4 poststatement5 N Valid 25 25 25 25 25 Missing 0 0 0 0 0 Mean 4.8400 4.8000 4.7200 4.5200 4.6800 Median 5.0000 5.0000 5.0000 5.0000 5.0000 Std. Deviation .37417 .40825 .45826 .50990 .47610 Table 9 shows that the highest mean score was associated with "I am good at English writing" (M = 4.84), where participants felt most confident about their writing skills. Similarly, strong agreement was reported for "I am confident in my English writing ability" (M = 4.80) and "I believe I have the ability to learn how to write English compositions well" (M = 4.72), reflecting strong confidence in both current ability and capacity for improvement. Although the sentence "I am confident that I can learn to write good English compositions" (M = 4.52) yielded the lowest mean, it also expresses a very high confidence level. Perceived effort as a causal factor was also affirmed in "When I work hard, I am sure I can produce a good piece of writing" (M = 4.68), affirming the role of persistence. The medians for all statements were 5.00, once more confirming the participants' strong agreement. The relatively low standard deviations (0.37 to 0.51) imply a high level of consensus in the responses. The second part of the motivation questionnaire was about the writing interest with 4 statements. Before conducting the appropriate comparison test, the normality of the data for this part was measured, and Table 10 below shows the results of the Shapiro-Wilk Normality Test. Table 10 The Results of the Normality Test in Writing Interest of both Groups in Pretest and Posttest groups Shapiro-Wilk Statistic df Sig. pretotal control .929 25 .084 experiment .942 25 .165 posttotal control .913 25 .036 experiment .811 25 .000 Table 10 shows that the pretest scores of the control group (W = .929, p = .084) and the pretest scores of the experimental group (W = .942, p = .165) were not significantly different from normality, which suggests that the normality assumption was sufficiently met for both groups before the intervention. However, for the control group scores, the posttest resulted in a marginally but statistically significant deviation from normality (W = .913, p = .036), while for the experimental group scores, the posttest indicated a large and statistically significant deviation from a normal distribution (W = .811, p < .001). The nonnormal distribution in the experimental group possibly reflects the high score concentration consistent with the written interest enhancement seen after the intervention. The results support using nonparametric tests for analysis in the following steps. Therefore, a Wald-Wolfowitz Runs Test was conducted to compare the performance of both groups' writing interest performance, and the results can be seen in Table 11 . Table 11 The Results of Writing Interest between the Groups in Pretest and Posttest Number of Runs Z Asymp. Sig. (1-tailed) pretotal Minimum Possible 8 -5.144 .000 Maximum Possible 35 2.572 .995 posttotal Exact Number of Runs 2 -6.859 .000 Table 11 revealed no statistical difference between the control and experimental groups in the pretest scores on writing interest. While the smallest number of runs (8) yielded a statistically significant result (Z = -5.144, p < .001), the most crucial number of runs (35) yielded a non-significant result (Z = 2.572, p = .995). This difference means that the pretest score distributions were generally similar for both groups, as in the planned experimental design. Posttest findings indicated a statistically significant difference between the two groups. The number of runs earned was 2, and the very considerable test statistics (Z = -6.859, p < .001) indicated that the experimental group had a statistically higher writing interest than the control group. The results confirm the success of the intervention applied to the experimental group. Their descriptive statistics were analysed to understand better the statements and their situations within the experimental group. Table 12 below shows the findings of the statements. Table 12 The Descriptive Statistics of Experimental Group in the Posttest of Writing Interest poststatement1 poststatement2 poststatement3 poststatement4 N Valid 25 25 25 25 Missing 0 0 0 0 Mean 4.6000 4.4400 4.6800 4.3600 Median 5.0000 4.0000 5.0000 4.0000 Std. Deviation .50000 .50662 .47610 .48990 Table 12 shows that the highest mean is for the statement "Writing English compositions makes me happy" (M = 4.68, SD = 0.48). This suggests that the activity provoked high emotional activation. Similarly, "I enjoy writing English compositions" received a high mean (M = 4.60, SD = 0.50), validating the increased pleasure of writing after the intervention. The other two statements, "English writing is an interesting activity" and "Writing English compositions makes me satisfied", also reported high means (M = 4.44 and M = 4.36 respectively), indicating that the students enjoyed the activity as being both mentally stimulating and self-gratifying. For all the items, the standard deviations were low at 0.48 to 0.51, indicating a uniform agreement pattern among the participants. Discussion The present study aimed at two main objectives in using Gemini, an interactive AI chatbot, to assess the influence of automated corrective feedback on EFL students' writing ability; additionally, it tried to assess EFL students' writing motivation after employing Gemini in their writing process. For this reason, 60 Omani English learners were randomly distributed into two groups: a treatment group and a control group of thirty learners. The treatment group received corrective feedback on their writing from the instructor and the Gemini, but the control group obtained general in-class feedback from their instructor. The data analysis in the writing skills showed that the control group increased their scores in the posttest, which could be the result of the in-class feedback; however, their writing scores decreased significantly in the delayed posttest. In contrast, the experimental group's performance followed an increasing pattern from pretest to posttest to delayed posttest, which could result from the AI tool. The comparison of both groups showed that the experimental group performed significantly better in posttest and delayed posttest than their counterparts in the control group. Moreover, further examination of the findings revealed that after the treatment, the writing motivation scale investigation revealed a significant change between the pretest and posttest in the treatment group. In the self-efficacy, although the control group showed higher efficiency in the pretest, the posttest results found that the experimental group significantly outperformed the control group. Finally, the writing interest of the students in both groups, which were similar in the pretest, showed that the experimental group performed better than the control group in the posttest. Following the use of Gemini as a facilitator for learning, the students' motivation levels improved dramatically. The treatment group's better writing posttest achievement than the control group might be attributed to the teacher and Gemini's dual-feedback system. This comprehensive technique enabled the experimental group to get automated corrective feedback that was timely and uniform in terms of grammar, vocabulary, and structure. The AI chatbot enabled learners to revise their work several times, increasing their involvement in the writing process. Unlike conventional instructor-only feedback, which is sometimes delayed and less engaging, the Gemini provided an on-the-spot educational experience where students could immediately remedy their errors. Gemini's collaborative and student-centered design may explain the notable increase in writing motivation among students in the treatment group. Engaging with the chatbot empowered students to take complete ownership of their education and allowed them to obtain feedback and make adjustments without continual teacher supervision. Developing this writing motivation requires confidence and motivation, which were generated by self-editing and thinking back on the writing process. According to the writing motivation scale findings, students in the treatment group actively participated in the feedback loop, which increased their feeling of ownership over their progress. This participation component was absent from the control group, which depended only on the teacher's advice and thus may have learned more passively. Self-directed learning and conventional feedback techniques were connected by this innovative use of AI as a tutor; the treatment group's motivation was noticeably increased. The study's findings align with several other investigations that have similarly shown how AI support tools improve students' writing. According to Song and Song ( 2023 ), students who used AI-assisted learning showed significant improvements in their writing skills and motivation compared to the control group. The therapy group did better in several writing-related areas, such as vocabulary, grammar, coherence, and organization. Similarly, Marzuki et al. (2023) found that AI writing tools improved students' work, especially concerning written content quality and structure. In another similar study, Hawanti and Zubaydulloevna ( 2023 ) found that AI-assisted instruction provided prompt corrections, enabling learners to pick things up faster and boost their confidence and writing skills. This lessens the tension brought on by ordinary classroom circumstances. In another study with similar findings, Hwang et al. ( 2023 ) revealed that the students who used the AI system produced far better writing, particularly in the coherence and contextualization of the topic. The feedback system was very beneficial when it came to helping students write better under actual conditions. Some studies have shown results consistent with the second study's aim, specifically that learners have greater writing motivation when using an AI assistance instrument in their writing courses. Silitonga et al. ( 2023 ) investigated the impact of AI chatbot-based acquisition on student motivation in English writing classes. The findings demonstrated that the AI chatbot-based group had substantially greater writing motivation than the control group, showing that artificial intelligence tools such as ChatGPT may improve learners' passion and desire to learn English writing. In a similar study, Chen and Gong ( 2025 ) found that AI-assisted learning increases learners' motivation in academic writing by fostering an encouraging atmosphere for learning and speeding up information acquisition. Similarly, Tajik ( 2025 ) examined how AI-powered dynamic writing platforms affected EFL pupils' motivation and writing abilities. According to the findings, the artificial intelligence (AI) group's writing abilities and motivation significantly improved, demonstrating the platforms' ability to raise learner motivation and writing competency. In another study with similar results, Chan et al. ( 2024 ) examined the effects of generative AI-based feedback, especially ChatGPT, on university students' essay-writing skills, revision results, motivation, and engagement. Learners who received Artificial-generated input throughout the revision process reported better levels of motivation and engagement, according to the research, suggesting that AI support tools might improve learners' writing motivation and learning processes.. Conclusion The current research focuses on analyzing students' capacity to acquire writing abilities and writing motivation by incorporating intelligent technology into the learning setting, namely the conversational chatbot of Gemini. This thirty-day research included 60 Omani EFL students. Although both the control and treatment groups got corrected textual comments from their instructors, the treatment group utilized Gemini as an additional tool in their educational process. The results revealed that the treatment group performed better than the control group in the posttest and delayed posttest. Furthermore, the customized writing motivation scale findings showed that learners' writing motivation in the treatment group rose significantly following the treatment period. This might be linked to the use of Gemini. The study's findings are useful for instructors, students, and curriculum designers. The Gemini is a strong tool that may help instructors improve their teaching approaches. This AI-powered solution would allow instructors to provide individualized and continuing feedback to learners, even outside lesson times, without considerably increasing their burden. This artificial intelligence application enables students to practice and improve their writing talents autonomously. This writing incentive encourages an active learning strategy where learners take charge of themselves by identifying mistakes, seeking assistance, and accessing information quickly. Incorporating these technologies into the curriculum enables developers to build more interactive and efficient learning settings that satisfy the technological demands of today's learners. The observed increase in students' writing abilities and motivation underscores the need to incorporate AI as a key component into language courses, ensuring that programs stay creative and sensitive to various learner requirements. Furthermore, incorporating AI technologies allows for a blended learning paradigm that combines conventional pedagogical techniques with technological advancements to boost learning results. This research contains various limitations that must be addressed thoroughly. The intervention was short, lasting about a month. Such a little time may have been insufficient to assess the long-term effects of employing the Gemini chatbot on the ability of learners to write and measure motivation. In addition, the sample size was small, consisting of 60 Omani EFL students; hence, the generalizability of the results is limited. The findings may not apply to learners from diverse cultural or academic backgrounds or to those with differing levels of English proficiency. Thirdly, although the study found that the Gemini chatbot improved writing ability and motivation, it did not investigate whether particular processes or qualities of the chatbot led to this result. It's unclear if the benefits were attributable to the quality of the chatbot feedback, the interactive possibility, or its ease of use. An in-depth examination of chatbot functionalities and their impact on educational results might be significantly more instructive. The study's results and scope lead to suggestions for further research. Future studies should examine the long-term impact of AI technology like the Gemini chatbot on language learning outcomes, such as whether the reported increases in writing competence and motivation are maintained over time. Furthermore, future studies might look at the usefulness of AI technology for various levels of language competency and student groupings. Examining whether comparable results can be found among beginning, intermediate, and advanced students from various cultural and language backgrounds would be fascinating. Third, subsequent research may examine the comparative efficacy of different AI systems in language learning. Researchers might, for example, compare the performance of Gemini to other artificially intelligent platforms in discovering the aspects that most successfully increase skill development and motivation to write. Furthermore, research efforts may center on incorporating AI technology into other components of language acquisition, such as reading comprehension, listening abilities, or speaking competency. Examining how AI may increase language abilities would give a more comprehensive knowledge of its influence in EFL environments. Declarations Author contributions All authors equally made significant contributions to the work discussed in this study. Funding The researchers are aware of the Open Access Policy for this journal. There is no funding from any organization, and the researchers will cover the cost. Ethical approval and consent to participate Before initiating this work, ethical permission was secured from the Sohar University, Oman. All study protocols conformed to ethical considerations. Participants were approved of the research before deciding to engage in the study. They supplied their written informed permission before the initiation of the study. Data Availability This is to certify that the data is available on the Figshare website using the following link (https://figshare.com/s/fbd5001bca9f27f5b51f). All the Authors contributed equally and honestly in this article. Conflict of Interest The authors clearly state that there is no conflict of interest in this research study. References Amin, M. R. M., Ismail, I., & Sivakumaran, V. M. (2025). Revolutionizing education with Artificial Intelligence (AI)? Challenges, and implications for open and distance learning (ODL). Social Sciences & Humanities Open, 11 , 01308. https://doi.org/10.1016/j.ssaho.2025.101308 Bai, B., Guo, W., & Wang, C. (2022). Relationships between struggling EFL writers' motivation, self-regulated learning (SRL), and writing competence in Hong Kong primary schools. Applied Linguistics Review , 15 (1), 135–159. https://doi.org/10.1515/applirev-2020-0131 Bai, B., Wang, J., & Zhou, H. (2022). 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In Handbook of self-regulation (pp. 13–39). Elsevier. https://doi.org/10.1007/s11423-017-9538-x Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6428474","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":444781706,"identity":"8348d38a-44d6-470f-a793-4d77d84da41c","order_by":0,"name":"Miew Luan Ng","email":"","orcid":"","institution":"INTI International University","correspondingAuthor":false,"prefix":"","firstName":"Miew","middleName":"Luan","lastName":"Ng","suffix":""},{"id":444781707,"identity":"2ed90bee-bbaf-43e9-b817-ad7b3724a1b4","order_by":1,"name":"Ali Al Ghaithi","email":"","orcid":"","institution":"Sohar University","correspondingAuthor":false,"prefix":"","firstName":"Ali","middleName":"Al","lastName":"Ghaithi","suffix":""},{"id":444781708,"identity":"09b8563c-1cf7-400c-b5e0-229f398d5f1c","order_by":2,"name":"Behnam Behforouz","email":"data:image/png;base64,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","orcid":"","institution":"University of Technology and Applied Sciences","correspondingAuthor":true,"prefix":"","firstName":"Behnam","middleName":"","lastName":"Behforouz","suffix":""}],"badges":[],"createdAt":"2025-04-11 12:53:12","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6428474/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6428474/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":91516838,"identity":"87e8e5df-668b-4fe7-9c1a-fd53c5e25fd9","added_by":"auto","created_at":"2025-09-17 09:24:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":972950,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6428474/v1/e223c725-9be6-49a6-97ec-765832210835.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Effect of Gemini on EFL Learners' Writing Skills and Motivation","fulltext":[{"header":"Introduction","content":"\u003cp\u003eWriting helps students to ponder on a sure thing, synthesize their views, and then present them in a piece of writing for interaction between those who read and the writer of the work. Thus, a writer must utilize language accurately, construct the piece of writing clearly, and identify flaws to prevent misunderstanding by the reader (Bitchener \u0026amp; Ferris, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Khadawardi, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Some research has expressly stated that writing competence is one of the most challenging language learning and education areas. If the learner's proficiency level is inadequate, the skills involved with writing tasks become noticeably more difficult. The primary issue is whether to ignore or correct the defects in the text (Banaruee, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Richards \u0026amp; Renandya, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Feedback is generally recognized as a critical instrument for promoting language acquisition, greatly assisting learners in developing their abilities, refining the learning process, and extending their comprehension (Banihashem et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). According to Banaruee and Askari (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), the efficiency of feedback systems in improving students' skills is not assured. According to Han (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), corrective feedback is a broad word that refers to general suggestions and changes, while mistake repair requires some direct and explicit input. According to Leki (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2001\u003c/span\u003e), providing corrective comments on pupils' writing by teachers and pupils to rectify errors is time-consuming in a foreign language setting. Choosing appropriate methods to deliver corrective feedback on writing talents is one component that contributes to this problem. Scholars are urged to apply the most effective feedback approaches to help students with the practical execution, revision, and correction of their writing. Written feedback has been very beneficial in enhancing and raising the correctness of L2 learners' written work. To attain this purpose, instructors must be well-trained in delivering timely and constructive feedback to learners to assist them in reducing the negative feelings associated with getting feedback (Mao \u0026amp; Lee, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHowever, providing individualized and relevant feedback to diverse students may be time-consuming and psychologically draining. Educators often get bored owing to the repetitious process of analyzing individual compositions, but foreign language learners' views on self-growth have a considerable impact on the dynamics of offering and receiving writing feedback (Teng \u0026amp; Teng, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). To address this demanding circumstance, Artificial Intelligence (AI) may be regarded as a helpful tool, as this fresh and revolutionary technology can significantly reduce instructors' excessive burden and provide tailored feedback to enhance active learning (Kasneci et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA wide variety of AI solutions have evolved to improve student performance; for example, machine learning algorithms are used to estimate individual grades by identifying particular patterns based on linguistic components in the tutoring information. This makes it easier to evaluate new works that contain similar tasks following such patterns (Ercikan \u0026amp; McCaffrey,2022). Moreover, AI enables real-time feedback, providing students with immediate and targeted advice while optimizing and simplifying administrative procedures to guarantee smooth and effective operations (Amin et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Previous research has shown that automatic writing feedback (AWF) can accurately evaluate and correctly assess articles equivalent to trained human evaluators (Ramesh \u0026amp; Sanampudi, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). According to research, dynamic feedback that adapts to students' talents and weaknesses based on AWE ratings may considerably improve writing and modification performances (Fleckenstein et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Horbach et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) as well as effectiveness (Huang \u0026amp; Wilson, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite substantial studies on the use of Web 2.0 technologies in L2 investigation, there seems to be little examination of the effectiveness of predictive texts in fostering L2 writing. There are very few empirical studies on text prediction and AI writing instruments. As a result, it is critical to study the potential applications of automated written feedback as an auxiliary tool in the learning environment to improve learners' writing skills (Frankenberg-Garcia, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Additionally, despite various studies examining the impacts of instructor or automated feedback on learners' writing abilities, there is a lack of thorough research on the beneficial effects and critical potential of combining both instructor and automated feedback (Yuan \u0026amp; Kim, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). As a result, this study aimed to assess the effect of employing Gemini as an AI tool to measure its impact on the writing abilities of EFL learners and the learners' motivation in their engagement with AI tools. Therefore, this research will extensively analyze the subsequent two questions:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eTo what extent does the integration of Gemini as an AI-assisted tool improve the writing performance of Omani EFL learners?\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eWhat effect does the use of Gemini have on the motivation levels of Omani EFL learners toward writing tasks?\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e"},{"header":"Literature Review","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eAutomated written corrective feedback\u003c/h2\u003e \u003cp\u003eWCF is corrective feedback on language correctness offered to English students (Bitchener and Knoch, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Ellis et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Recent investigations of English writing and technology have concentrated on using internet-based tools to provide WCF, resulting in the development of AWCF as an innovative writing framework. AWCF is defined as WCF delivered automatically by a web-based system to assist students with language-related concerns in their writing versions. AWCF recognizes learners' blunders and errors in punctuation, spelling, grammar, and writing norms, offers rapid feedback on students' language-related concerns, and allows them enough time to amend their writing drafts. Students are also given accurate and precise metalinguistic clarifications, which assist them in improving their writing ability and help teachers deal with the time constraints of collaborative and process-oriented writing exercises. Furthermore, by offering immediate feedback on writing faults and errors, AWCF assists students in identifying and addressing language-related difficulties more effectively and quickly (Lee, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Additionally, the introduction of generative AI, such as ChatGPT and Microsoft Copilot, has raised the requirement that AWF use these advanced big language models (LLM) to give more accurate and comprehensive feedback (Barrot, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAutomated corrective feedback (ACF) is system-generated feedback on student-written content. Several ACF algorithms are often utilized in language learning (Chen et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). ACF has sparked substantial attention among instructors and researchers because of its many educational benefits, including improving language learners' writing quality by recognizing and correcting errors. For example, ACF technology may identify and highlight inconsistencies in written text, allowing students to understand their mistakes easily. In addition, ACF devices provide suggestions for correcting them. Both students and instructors may benefit from adopting ACF tools. For example, ACF tools give quick feedback, allowing for rapid correction of writing mistakes (Li et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWaer (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) used an experimental approach to investigate how AWE affected EFL students' writing anxiety and their grammatical range and correctness. 103 learners of EFL were divided into a treatment and a control group at random using a randomly assigned, true-experimental research method. The treatment group used Cambridge English's Write \u0026amp; Improve internet-based tools to give learners AWE on their writing assignments. To assist students in completing a writing assignment (an essay, report, or letter) correctly, the Write \u0026amp; Improve system assigns each student to a suitable level (beginners A1 and A2, intermediate B1 and B2, and advanced C1 and C2) using the Common European Framework of Reference (CEFR) dimension. A grammar knowledge exam was used to gather the necessary data, and the results showed that learners who received AWE scored better in terms of grammatical correctness and range than those who did not.\u003c/p\u003e \u003cp\u003eWang et al. (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) investigated how AWE affected the writing correctness, autonomy, and engagement of students learning EFL. Treatment and control groups of fifty-seven first-year EFL learners were randomly assigned. The treatment group used the online tool CorrectEnglish to assist learners in enhancing their writing using AWE, whereas the control group used the traditional, teacher-centered strategy to improve the pupil's writing ability. Learners' writing styles, proper word use, and grammar patterns are all evaluated by CorrectEnglish. Additionally, this system provides learners with instant feedback on their writing achievement, including content, organization, style, focus, and general achievement in writing. The necessary quantitative information was gathered using a written composition exam and a questionnaire for self-reporting, while the qualitative data was collected via a semi-structured interview. According to the findings of the quantitative analysis, learners who used AWE fared better than those in the non-AWE class in terms of interaction, independence, and writing correctness. The qualitative research also revealed the students' favorable opinions about using AWE to enhance their writing precision and self-directed learning.\u003c/p\u003e \u003cp\u003eSaricaoglu and Bilki (2021) investigated how EFL learners used Criterion, a digital AWCF system, in their writing assignments and how much AWCF increased their writing correctness. Criterion provides students with real-time feedback while working on their assignments and after submitting their finished drafts to the site. To assist students in improving their future writing achievement, Criterion also summarises their writing proficiency report, including the number of words, phrases, and mistakes they made. 114 EFL students from five different classrooms consented to participate in the research. The students were urged to submit their written work to Criterion and resolve any writing problems in light of the comments and feedback provided by Criterion. According to the instrument's usage monitoring results and achievement summary report collection, some students refused to utilize Criterion to complete their writing drafts. Nonetheless, the student's participation in AWCF improved their writing correctness and reduced their mistakes in their final copy.\u003c/p\u003e \u003cp\u003eBarrot (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) used a quasi-experimental study design to investigate how AWCF utilizing Grammarly affected the writing quality and mistakes of 65 students learning English. Ten argumentative essay assignments, ranging from 200 to 300 words, were given to the treatment and control groups. While the control group employed non-AWCF in their writing activities, the treatment students utilized Grammarly to get AWCF for their writing assignments and enhance their writing appropriately. Similar 200\u0026ndash;300 words argumentative essay assignments were used to gather the necessary pretest and posttest data. Since Grammarly may offer instantaneous metalinguistic justifications and improve students' observing and autonomous functioning, the findings showed that Grammar-based AWCF enhanced the students' writing fluency.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMotivation in ESL/EFL writing\u003c/h3\u003e\n\u003cp\u003eWhen it comes to improving writing abilities, motivation is crucial (Ebadijalal \u0026amp; Moradkhani, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Self-confidence, interest, perceived task value, and views are only a few of the interrelated elements that make up the complex concept of motivation (Troia et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Self-confidence and writing interest are two important elements that might affect young writers' skills (Bai et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Writing self-confidence has been shown in many research studies to be a significant predictor of writing achievement, with higher levels of self-efficacy linked to improved writing results in addition to increased effort, persistence, and utilization of SRL techniques on tasks related to writing (Rahimi \u0026amp; Fathi, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Teng \u0026amp; Zhang, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Another essential motivating element that affects students' involvement with writing assignments and the standard of the work they produce is their intrinsic pleasure in writing. According to studies, students driven by genuine enthusiasm often write better and are more likely to stick with a project despite difficulties (Ebadijalal \u0026amp; Moradkhani, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). To foster enduring personal interest in writing, Renninger and Hidi (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) highlighted the significance of contextual interest sparked by particular activities or settings. For instance, Boo et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) discovered that EFL students who were given the freedom to choose their themes and were exposed to interesting prompts for writing had improved their ability to write and intrinsic motivation. According to Zimmerman (\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2000\u003c/span\u003e), more motivated students are more likely to employ self-regulated learning (SRL) methods, and motivation may be increased by using these tactics. According to MacArthur et al. (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), self-efficacy and perseverance were higher among ESL students who actively used SRL methods. Similarly, Bai et al. (2024) discovered that among problematic EFL writers in Hong Kong elementary schools, the adoption of SRL strategies was positively correlated with motivation components (interest, self-efficacy, and development mindset). Writing self-confidence and writing SRL techniques both significantly contribute to estimating the level of writing competency among Chinese EFL undergraduates (Sun \u0026amp; Wang, 2020).\u003c/p\u003e \u003cp\u003eSong and Song (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) used a mixed-methods approach to study the influence of AI-assisted language acquisition on writing abilities and motivation in 50 Chinese EFL learners. The project was designed with a control group getting conventional training and a treatment group using AI technologies, notably ChatGPT. The results indicated that the AI-assisted group demonstrated substantial increases in writing abilities and increased writing motivation among treatment group participants. Suciati et al. (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) investigated the effectiveness of AI-assisted technologies, especially ChatGPT, in increasing enthusiasm and inspiration in English writing among 72 Indonesian language learners. Researchers compared conventional interactive learning approaches to ChatGPT-supported exercises in a controlled experimental setting. The results revealed substantial increases in both enthusiasm and involvement within the ChatGPT group, emphasizing AI technologies' potential to enhance educational experiences by promoting a more dynamic and customized setting for learning. Tajik (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) evaluated the effect of AI-powered dynamic writing systems on EFL students' writing abilities and motivation. The research included 65 intermediate learners separated into two groups: one employing artificial intelligence (AI) resources and the other employing conventional techniques. The results revealed considerable gains in writing abilities and improved motivation in the AI group, showing the platforms' usefulness in improving writing competency and student motivation. Silitonga et al. (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) examined the effect of AI chatbot-based acquiring on pupil inspiration in English writing courses. The research used a mixed-methods approach, with 73 undergraduates separated into two groups: a control group receiving conventional training and a treatment group receiving AI chatbots, ChatGPT. The results showed that the AI chatbot-based group had much higher writing motivation than the control group, implying that artificial intelligence instruments such as ChatGPT may successfully boost learners' enthusiasm and drive them to learn English writing.\u003c/p\u003e"},{"header":"Method","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eThe population being investigated included 50 Omani EFL learners randomly allocated to control and experimental groups, 20 learners. The students' mother tongue was Arabic, and their ages were between 18 and 19. The university's assessments revealed that these students had pre-intermediate English language proficiency. The Foundation Department has no clear criteria for rigorously controlling the participants' ages and genders. The participants in the experience group received input from both Gemini and the instructor to clarify Gemini's remarks, while the control group received feedback solely from their instructor.\u003c/p\u003e \u003cp\u003eThe General Foundation Program (GFP) is a one-year program in three consecutive 12-week semesters that prepares the students for their specialization, focusing on English, Math, and Computing Skills modules, following Oman's national curriculum. Admission to college in Oman requires completion of the GFP program.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eInstrumentation\u003c/h3\u003e\n\u003cp\u003eThe necessary data was gathered using the following tools.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eWriting Pretest, Posttest, and Delayed Posttest\u003c/h2\u003e \u003cp\u003eBecause the primary goal of this research was to assess the influence of Gemini on the learners' writing abilities, a writing pretest was conducted to ensure all the learners have similar writing skills. Later on, a posttest was performed due to the comparative nature of the study, and to measure the impact of treatment on the experimental group. Finally, to measure the role of Gemini in long-term knowledge retention, another delayed posttest was conducted on both groups of students. Each writing assignment requires learners to write 100 words regarding a compare and contrast essay. The standards for both assessments were Task Achievement, Organization, Grammar, and Vocabulary. Each exam had a total score of 20 points, with 5 scores indicated for every single category. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e below shows the questions selected as pretest, posttest, and delayed posttest.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eThe Pretest, Posttest, and the Delayed Posttest of Writing\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"1\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePretest: Online Learning vs. Classroom Learning Instructions\u003c/b\u003e:\u003c/p\u003e \u003cp\u003eWrite a 100-word paragraph comparing and contrasting online learning and classroom learning. Discuss at least one similarity and one difference. Use linking words, \u003cem\u003ehowever\u003c/em\u003e, \u003cem\u003eon the other hand\u003c/em\u003e, and \u003cem\u003eboth\u003c/em\u003e.\u003c/p\u003e \u003cp\u003e\u003cb\u003ePosttest: City Life vs. Village Life\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eInstructions\u003c/b\u003e:\u003c/p\u003e \u003cp\u003eIn about 100 words, compare life in a city and a village. Mention one way they are similar and at least one way they are different. Make sure your writing is clear and organized.\u003c/p\u003e \u003cp\u003e\u003cb\u003eDelayed Posttest: Reading Books vs. Watching Movies\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eInstructions\u003c/b\u003e:\u003c/p\u003e \u003cp\u003eWrite a short paragraph (100 words) comparing and contrasting reading books and watching movies. Describe one similarity and one difference. Use comparison words like \u003cem\u003ein contrast\u003c/em\u003e, \u003cem\u003elikewise\u003c/em\u003e, \u003cem\u003eunlike\u003c/em\u003e, and \u003cem\u003ewhile\u003c/em\u003e.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eGemini AI-powered\u003c/h2\u003e \u003cp\u003eGoogle created Gemini, an AI-powered writing facilitator that can help in writing jobs. While it was initially designed for programming, its language processing features make it an ideal tool for educational settings, especially for assisting student writing growth. Gemini identifies grammatical errors, awkward phrasing, and syntactical issues, providing immediate corrections with explanations that facilitate learning. It offers recommendations to improve clarity, coherence, and conciseness, aligning with academic writing conventions and disciplinary expectations. The assistant learns from user interactions and adjusts its recommendations based on previous edits and preferences, becoming increasingly personalized with continued use. Gemini can modify its output to match various discourse styles, from formal academic writing to creative narratives, helping students develop genre awareness and flexibility.\u003c/p\u003e \u003cp\u003eGemini is accessible via a separate internet interface rather than texting apps. Using the web platform rather than a separate app provides multiple appealing benefits for students. There is no need to install extra software since Gemini works with any standard web browser; the writing aid service is instantly accessible from a familiar online environment. This technique is especially beneficial for students who use various devices and may encounter compatibility concerns with specific programs. The link to the web platform uses students' existing digital activities since they commonly utilize browsers for academic research and writing, making it simple for them to include Gemini into their educational workflow. The web interface's ability to work across several devices and operating systems further supports its adoption as the distribution platform. Eliminating additional registration steps reduces potential barriers, allowing students to access the service immediately with their existing Google accounts. Furthermore, this strategy is resource-efficient; students may access the service using their existing internet connections without needing unique app-based data allocations, eliminating the need for further financial investment in new software or specialized apps. The web-based solution also suits the academic environment's requirement for bigger screen sizes when dealing with lengthy papers, resulting in a more thorough writing experience than would be available via chat services.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eWriting Motivation Scale\u003c/h2\u003e \u003cp\u003eThe motivation questionnaire was developed by Bai et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), from which the statements were collected, and the modifications were applied to prepare them to precisely measure learners' motivation in using AI tools to receive feedback. The Motivation section included writing self-efficacy (5 statements) and interest (4 statements). Students had 15 minutes to complete the questionnaire; the statements were translated into Arabic to help the process. Arranged according to the 5-point Likert Scale, the statements included 5 (strongly agree), 4 (agree), 3 (neutral), 2 (disagree), and 1 (strongly disagree). A pilot study was conducted to assess the dependability of the questionnaire with 25 Omani EFL students inside the same university and English proficiency level before the main round of research. .835 for the dependability index might be seen as rather dependable. Three PhD candidates in Applied Linguistics with significant professional expertise evaluated the questionnaire to guarantee that the Arabic version of the scale followed comparable language standards, structure, and style. An impartial moderator then evaluated the poll to ensure its accuracy using inquiry moderation and the application of intellectual and cultural values.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eProcedures\u003c/h2\u003e \u003cp\u003eThis research was carried out during the second semester of 2025\u0026ndash;2026 at one of Oman's universities. After acquiring all necessary clearances from authorities and students, writing and motivation pretests were performed to verify that the learner's writing skills were consistent. The treatment duration was set to three weeks. Before applying the treatment, one of the researchers led a training workshop for the group receiving treatment in the initial seven days to teach them how to use Gemini to obtain feedback on their work. The study's investigator taught the students to use particular cues to elicit input from the Gemini. To complete the research in three weeks, the researcher used the entire two hours of English courses for four days while the students attended a four-hour daily English session. The total number of lectures was twelve, and writing classes were held four days a week, and each instruction lasted 2 hours. During the introductory writing lesson, the treatment and control groups were given a two-hour description of how the essays should be structured. The lecturer then issued a task for both groups of students to work on outside of class. The students were expected to submit the homework the next day.\u003c/p\u003e \u003cp\u003eThe experimental group's students were then expected to examine the first draft employing the Gemini using the procedures provided by the investigator. The updated work had to be provided via email or Microsoft Teams to give the teacher more clarity and rationale during future writing sessions. The class teacher commented using Microsoft Word's \"Track Changes\" function. To maintain the natural structure of the feedback approach, the instructor was asked not to limit comments on language or content-related issues. The Gemini program and website were used for e-feedback, and students were asked to send their essays as messages to Gemini after implementing the precise instructions presented by the investigator throughout the training course. The pupils got comments on their writing drafts, which they might use as a guideline to edit their work before sending it to the instructor by email or Microsoft for final input. At the same time, the control group got feedback from the instructor using the traditional method throughout all of the classes. The twelve writing activities were updated after the third week. Following the study period, both groups completed posttests to compare their results. In addition, the posttest of motivation scale was supplied to both groups to assess the level of writing motivation when intelligent technology was used as an intermediary in the English language acquisition process.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eData Analysis\u003c/h2\u003e \u003cp\u003eThis section investigates the collected data by applying statistical analysis using SPSS 27.0 to the results of student scores in the writing tests and the motivation scale. The first step before selecting a test of comparison was to measure the condition of the data distribution to choose a suitable parametric or nonparametric test. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e below shows the results of the Kolmogorov-Smirnov Normality Test in 3 sets of writing tasks.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eThe Results of the Normality Test for Pretest, Posttest, and Delayed Posttest in Both Groups\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003egroups\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eKolmogorov-Smirnov\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStatistics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSig.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003epretest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003econtrol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.084\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eexperiment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.200\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eposttest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003econtrol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.200\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eexperiment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.149\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003edelayed posttest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003econtrol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.129\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eexperiment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eMost of the data sets' results in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e showed that the normality assumption was satisfied. Specifically, the control and experimental groups showed regularly distributed scores in the pretest (p\u0026thinsp;=\u0026thinsp;.084 and p\u0026thinsp;=\u0026thinsp;.200, respectively) and posttest phases (p\u0026thinsp;=\u0026thinsp;.200 and p\u0026thinsp;=\u0026thinsp;.149, respectively). The control group's delayed posttest scores also followed a normal distribution (p\u0026thinsp;=\u0026thinsp;.129). On the other hand, the experimental group's delayed posttest results revealed a notable departure from normality (p\u0026thinsp;=\u0026thinsp;.011), suggesting a nonnormal distribution. This result implies that although most comparisons could call for parametric testing, one should be careful when looking at the delayed posttest scores of the experimental group, as nonparametric tests could be more appropriate. Since the data sets for the control group were expected, a parametric test of the Paired-Sample Test was conducted to determine the performance of students within the control group from pretest to posttest to delayed posttest, and the results can be seen in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e below.\u003c/p\u003e \u003cp\u003e\u003cimg 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\" height=\"231\" width=\"584\"\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows that the comparison between the pretest and posttest scores demonstrates a statistically significant improvement, with a mean difference of -2.68 (p\u0026thinsp;\u0026lt;\u0026thinsp;.001), demonstrating that the subjects performed substantially better following the intervention. On the other hand, the pretest and delayed posttest comparison showed no statistically significant difference (mean difference = -0.08, t(24) = -0.153, p\u0026thinsp;=\u0026thinsp;.880), suggesting that the improvements seen in the posttest were not maintained over time. Conversely, the comparison between the posttest and the delayed posttest showed a statistically significant decrease (mean difference\u0026thinsp;=\u0026thinsp;2.60, t (24)\u0026thinsp;=\u0026thinsp;4.572, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), suggesting a loss in performance following the first improvement. These findings imply that although the program produced quick improvement, the impact was lessened by the time of the postponed posttest. Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e below shows learners' performance in the experimental group in all the 3 sets of writing tests. Since the students of the experimental group in the delayed posttest showed a nonnormal distribution of data, a nonparametric sign test was selected for this comparison.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eThe Results of the Sign Test for the Pretest, Posttest, and Delayed Posttest in the Experimental Group\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eposttest - pretest\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003edelayedposttest - pretest\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003edelayedposttest - posttest\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExact Sig. (2-tailed)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eUsing the binomial distribution, the Sign Test findings in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e show statistically significant variations across all three comparisons. Indicating a substantial increase in performance after the intervention, the posttest scores were much higher than the pretest scores (Exact Sig. (2-tailed)\u0026thinsp;\u0026lt;\u0026thinsp;.001). Likewise, the postponed posttest results revealed a statistically notable increase over the pretest results (Exact Sig. (2-tailed)\u0026thinsp;\u0026lt;\u0026thinsp;.001), implying that the noted improvements persisted with time. Moreover, the difference between the posttest and delayed posttests produced a statistically significant finding (Exact Sig. (2-tailed)\u0026thinsp;=\u0026thinsp;.003), suggesting a substantial shift between these two phases. These results show that the program had a consistent and statistically significant impact on student performance. Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e below shows the Marginal Homogeneity Test to provide more detailed information on the experimental group results.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eThe Marginal Homogeneity Test\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003epretest \u0026amp; posttest\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eposttest \u0026amp; delayedposttest\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistinct Values\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOff-Diagonal Cases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObserved MH Statistic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e271.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e365.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean MH Statistic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e335.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e381.500\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStd. Deviation of MH Statistic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.928\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.123\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStd. MH Statistic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-4.595\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-3.220\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsymp. Sig. (2-tailed)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe outcomes of Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e confirm even more the statistically significant variations seen throughout the evaluation rounds. The test showed an observed MH statistic of 271.000 when comparing the pretest and posttest, significantly lower than the mean MH statistic of 335.000, producing a normalized MH statistic of -4.595. This discrepancy led to an asymptotic significance value of .000, suggesting a notable variation in answer distributions between the pretest and posttest phases. Likewise, the posttest and delayed posttest comparison revealed an apparent MH statistic of 365.000 vs a mean MH statistic of 381.500, resulting in a normalized MH statistic of -3.220. .001's related asymptotic significance value verifies a statistically significant change in response patterns between the two test events. Based on 12 and 7 different values in the two comparisons and from 25 and 23 off-diagonal situations, these results support the notion that participants' performance changed significantly over time. The Marginal Homogeneity Test, therefore, supports the reading that the noted variations were not caused by chance. The final statistical analysis of the writing compares both groups on the assigned writing tasks. Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e below shows the findings of ANOVA in 3 sets of writing tasks.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eThe Results of Writing Tasks in the Pretest, Posttest, and Delayed Posttest between Both Group\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSum of Squares\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMean Square\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSig.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eEffect size\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003epretest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBetween Groups\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.870\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.00057\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWithin Groups\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e141.200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.942\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e141.280\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eposttest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBetween Groups\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69.620\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e69.620\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22.739\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.3214\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWithin Groups\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e146.960\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e216.580\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003edelayed posttest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBetween Groups\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e492.980\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e492.980\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e148.787\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.7558\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWithin Groups\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e159.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.313\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e652.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e shows that at the pretest phase, there was no statistically significant difference between the groups, F(1, 48)\u0026thinsp;=\u0026thinsp;0.027, p\u0026thinsp;=\u0026thinsp;.870, with a small effect size (η\u0026sup2; = 0.00057), suggesting that the participants started from an equal baseline. But in the posttest, a statistically significant difference appeared, F (1, 48)\u0026thinsp;=\u0026thinsp;22.739, p\u0026thinsp;\u0026lt;\u0026thinsp;.001, with a considerable effect size (η\u0026sup2; = 0.3214). This suggests a significant influence on learning results, as almost 32% of the variation in posttest scores could be ascribed to the intervention. With an extraordinarily high effect size (η\u0026sup2;= 0.7558), the delayed posttest showed a statistically significant difference, F(1, 48)\u0026thinsp;=\u0026thinsp;148.787, p\u0026thinsp;\u0026lt;\u0026thinsp;.001. Reflecting its considerable and lasting impact, the intervention explains over 76% of the variation in delayed posttest results.\u003c/p\u003e \u003cp\u003eThe second part of this section investigates the performance of students in both groups based on the motivation scale. Before selecting the appropriate inferential test, the data distribution condition was measured using the Shapiro-Wild Nomrlaity Test. Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e below shows the results of the data distribution.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eThe Results of the Normality Test in Pretest and Posttest of the Self-Efficacy\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003egroups\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eShapiro-Wilk\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStatistics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSig.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003epretotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003econtrol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.887\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eexperiment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.838\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eposttotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003econtrol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.887\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eexperiment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.876\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e revealed that the total self-efficacy score distribution was far from normal across all groups and time points. In the pretest, the control group (W\u0026thinsp;=\u0026thinsp;0.887, p\u0026thinsp;=\u0026thinsp;.010) and the experimental group (W\u0026thinsp;=\u0026thinsp;0.838, p\u0026thinsp;=\u0026thinsp;.001) revealed nonnormal distributions. Similarly, during the posttest, the control group (W\u0026thinsp;=\u0026thinsp;0.887, p\u0026thinsp;=\u0026thinsp;.010) and experimental group (W\u0026thinsp;=\u0026thinsp;0.876, p\u0026thinsp;=\u0026thinsp;.006) exhibited significant deviation from normality. This result suggests that nonparametric tests are preferable in investigating differences between self-efficacy scores. Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e below shows the results of the Mann-Whitney U Test in comparing the pretest and posttest of both groups.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eThe Results of Comparison between Two Groups in Pretest and Posttest of Self-Efficacy\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean Rank\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSum of Ranks\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMann\u0026ndash;Whitney U\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eWilcoxon W\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eZ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eAsymp. Sig. (2-tailed)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePretest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e774.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e175.500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e500.500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-2.817\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePretest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExperimental\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e500.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePosttest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e325.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e325.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-6.139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePosttest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExperimental\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e950.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e shows significant differences between the self-efficacy scores of the control and experimental groups in both instances. In the pretest, the control group had higher self-efficacy, as seen by a greater mean rank (30.98) compared to that of the experimental group (20.02), with the difference being statistically significant (U\u0026thinsp;=\u0026thinsp;175.500, Z = -2.817, p\u0026thinsp;=\u0026thinsp;.005). However, this was reversed in the posttest, when the experimental group scored a much larger mean rank (38.00) than the control group (13.00). This differed significantly (U\u0026thinsp;=\u0026thinsp;0.000, Z = -6.139, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), indicating a significant increase in the experimental group's self-efficacy following treatment, with the control group having no comparable increase. These findings validate the intervention's positive influence on learners' writing self-efficacy. The statement of self-efficacy was further measured to understand students' choices better. Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e below shows the results of these statistics.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eThe Descriptive Statistics of Self-Efficacy Statements within the Experimental Group\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003epoststatement1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003epoststatement2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003epoststatement3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003epoststatement4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003epoststatement5\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eValid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.8400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.8000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.7200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.5200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.6800\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMedian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.0000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eStd. Deviation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.37417\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.40825\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.45826\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.50990\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.47610\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e shows that the highest mean score was associated with \"I am good at English writing\" (M\u0026thinsp;=\u0026thinsp;4.84), where participants felt most confident about their writing skills. Similarly, strong agreement was reported for \"I am confident in my English writing ability\" (M\u0026thinsp;=\u0026thinsp;4.80) and \"I believe I have the ability to learn how to write English compositions well\" (M\u0026thinsp;=\u0026thinsp;4.72), reflecting strong confidence in both current ability and capacity for improvement. Although the sentence \"I am confident that I can learn to write good English compositions\" (M\u0026thinsp;=\u0026thinsp;4.52) yielded the lowest mean, it also expresses a very high confidence level. Perceived effort as a causal factor was also affirmed in \"When I work hard, I am sure I can produce a good piece of writing\" (M\u0026thinsp;=\u0026thinsp;4.68), affirming the role of persistence. The medians for all statements were 5.00, once more confirming the participants' strong agreement. The relatively low standard deviations (0.37 to 0.51) imply a high level of consensus in the responses.\u003c/p\u003e \u003cp\u003eThe second part of the motivation questionnaire was about the writing interest with 4 statements. Before conducting the appropriate comparison test, the normality of the data for this part was measured, and Table\u0026nbsp;\u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e10\u003c/span\u003e below shows the results of the Shapiro-Wilk Normality Test.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab10\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 10\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eThe Results of the Normality Test in Writing Interest of both Groups in Pretest and Posttest\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003egroups\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eShapiro-Wilk\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStatistic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSig.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003epretotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003econtrol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.929\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.084\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eexperiment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.942\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.165\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eposttotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003econtrol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.913\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.036\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eexperiment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.811\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e10\u003c/span\u003e shows that the pretest scores of the control group (W\u0026thinsp;=\u0026thinsp;.929, p\u0026thinsp;=\u0026thinsp;.084) and the pretest scores of the experimental group (W\u0026thinsp;=\u0026thinsp;.942, p\u0026thinsp;=\u0026thinsp;.165) were not significantly different from normality, which suggests that the normality assumption was sufficiently met for both groups before the intervention. However, for the control group scores, the posttest resulted in a marginally but statistically significant deviation from normality (W\u0026thinsp;=\u0026thinsp;.913, p\u0026thinsp;=\u0026thinsp;.036), while for the experimental group scores, the posttest indicated a large and statistically significant deviation from a normal distribution (W\u0026thinsp;=\u0026thinsp;.811, p\u0026thinsp;\u0026lt;\u0026thinsp;.001). The nonnormal distribution in the experimental group possibly reflects the high score concentration consistent with the written interest enhancement seen after the intervention. The results support using nonparametric tests for analysis in the following steps. Therefore, a Wald-Wolfowitz Runs Test was conducted to compare the performance of both groups' writing interest performance, and the results can be seen in Table\u0026nbsp;\u003cspan refid=\"Tab11\" class=\"InternalRef\"\u003e11\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab11\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 11\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eThe Results of Writing Interest between the Groups in Pretest and Posttest\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber of Runs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eZ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAsymp. Sig. (1-tailed)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003epretotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMinimum Possible\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-5.144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMaximum Possible\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.572\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.995\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eposttotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExact Number of Runs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-6.859\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab11\" class=\"InternalRef\"\u003e11\u003c/span\u003e revealed no statistical difference between the control and experimental groups in the pretest scores on writing interest. While the smallest number of runs (8) yielded a statistically significant result (Z = -5.144, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), the most crucial number of runs (35) yielded a non-significant result (Z\u0026thinsp;=\u0026thinsp;2.572, p\u0026thinsp;=\u0026thinsp;.995). This difference means that the pretest score distributions were generally similar for both groups, as in the planned experimental design. Posttest findings indicated a statistically significant difference between the two groups. The number of runs earned was 2, and the very considerable test statistics (Z = -6.859, p\u0026thinsp;\u0026lt;\u0026thinsp;.001) indicated that the experimental group had a statistically higher writing interest than the control group. The results confirm the success of the intervention applied to the experimental group. Their descriptive statistics were analysed to understand better the statements and their situations within the experimental group. Table\u0026nbsp;\u003cspan refid=\"Tab12\" class=\"InternalRef\"\u003e12\u003c/span\u003e below shows the findings of the statements.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab12\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 12\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eThe Descriptive Statistics of Experimental Group in the Posttest of Writing Interest\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003epoststatement1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003epoststatement2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003epoststatement3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003epoststatement4\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eValid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.6000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.4400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.6800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.3600\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMedian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.0000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eStd. Deviation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.50000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.50662\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.47610\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.48990\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab12\" class=\"InternalRef\"\u003e12\u003c/span\u003e shows that the highest mean is for the statement \"Writing English compositions makes me happy\" (M\u0026thinsp;=\u0026thinsp;4.68, SD\u0026thinsp;=\u0026thinsp;0.48). This suggests that the activity provoked high emotional activation. Similarly, \"I enjoy writing English compositions\" received a high mean (M\u0026thinsp;=\u0026thinsp;4.60, SD\u0026thinsp;=\u0026thinsp;0.50), validating the increased pleasure of writing after the intervention. The other two statements, \"English writing is an interesting activity\" and \"Writing English compositions makes me satisfied\", also reported high means (M\u0026thinsp;=\u0026thinsp;4.44 and M\u0026thinsp;=\u0026thinsp;4.36 respectively), indicating that the students enjoyed the activity as being both mentally stimulating and self-gratifying. For all the items, the standard deviations were low at 0.48 to 0.51, indicating a uniform agreement pattern among the participants.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe present study aimed at two main objectives in using Gemini, an interactive AI chatbot, to assess the influence of automated corrective feedback on EFL students' writing ability; additionally, it tried to assess EFL students' writing motivation after employing Gemini in their writing process. For this reason, 60 Omani English learners were randomly distributed into two groups: a treatment group and a control group of thirty learners. The treatment group received corrective feedback on their writing from the instructor and the Gemini, but the control group obtained general in-class feedback from their instructor. The data analysis in the writing skills showed that the control group increased their scores in the posttest, which could be the result of the in-class feedback; however, their writing scores decreased significantly in the delayed posttest. In contrast, the experimental group's performance followed an increasing pattern from pretest to posttest to delayed posttest, which could result from the AI tool. The comparison of both groups showed that the experimental group performed significantly better in posttest and delayed posttest than their counterparts in the control group. Moreover, further examination of the findings revealed that after the treatment, the writing motivation scale investigation revealed a significant change between the pretest and posttest in the treatment group. In the self-efficacy, although the control group showed higher efficiency in the pretest, the posttest results found that the experimental group significantly outperformed the control group. Finally, the writing interest of the students in both groups, which were similar in the pretest, showed that the experimental group performed better than the control group in the posttest. Following the use of Gemini as a facilitator for learning, the students' motivation levels improved dramatically.\u003c/p\u003e \u003cp\u003eThe treatment group's better writing posttest achievement than the control group might be attributed to the teacher and Gemini's dual-feedback system. This comprehensive technique enabled the experimental group to get automated corrective feedback that was timely and uniform in terms of grammar, vocabulary, and structure. The AI chatbot enabled learners to revise their work several times, increasing their involvement in the writing process. Unlike conventional instructor-only feedback, which is sometimes delayed and less engaging, the Gemini provided an on-the-spot educational experience where students could immediately remedy their errors.\u003c/p\u003e \u003cp\u003eGemini's collaborative and student-centered design may explain the notable increase in writing motivation among students in the treatment group. Engaging with the chatbot empowered students to take complete ownership of their education and allowed them to obtain feedback and make adjustments without continual teacher supervision. Developing this writing motivation requires confidence and motivation, which were generated by self-editing and thinking back on the writing process. According to the writing motivation scale findings, students in the treatment group actively participated in the feedback loop, which increased their feeling of ownership over their progress. This participation component was absent from the control group, which depended only on the teacher's advice and thus may have learned more passively. Self-directed learning and conventional feedback techniques were connected by this innovative use of AI as a tutor; the treatment group's motivation was noticeably increased.\u003c/p\u003e \u003cp\u003eThe study's findings align with several other investigations that have similarly shown how AI support tools improve students' writing. According to Song and Song (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), students who used AI-assisted learning showed significant improvements in their writing skills and motivation compared to the control group. The therapy group did better in several writing-related areas, such as vocabulary, grammar, coherence, and organization. Similarly, Marzuki et al. (2023) found that AI writing tools improved students' work, especially concerning written content quality and structure. In another similar study, Hawanti and Zubaydulloevna (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) found that AI-assisted instruction provided prompt corrections, enabling learners to pick things up faster and boost their confidence and writing skills. This lessens the tension brought on by ordinary classroom circumstances. In another study with similar findings, Hwang et al. (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) revealed that the students who used the AI system produced far better writing, particularly in the coherence and contextualization of the topic. The feedback system was very beneficial when it came to helping students write better under actual conditions.\u003c/p\u003e \u003cp\u003eSome studies have shown results consistent with the second study's aim, specifically that learners have greater writing motivation when using an AI assistance instrument in their writing courses. Silitonga et al. (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) investigated the impact of AI chatbot-based acquisition on student motivation in English writing classes. The findings demonstrated that the AI chatbot-based group had substantially greater writing motivation than the control group, showing that artificial intelligence tools such as ChatGPT may improve learners' passion and desire to learn English writing. In a similar study, Chen and Gong (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) found that AI-assisted learning increases learners' motivation in academic writing by fostering an encouraging atmosphere for learning and speeding up information acquisition. Similarly, Tajik (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) examined how AI-powered dynamic writing platforms affected EFL pupils' motivation and writing abilities. According to the findings, the artificial intelligence (AI) group's writing abilities and motivation significantly improved, demonstrating the platforms' ability to raise learner motivation and writing competency. In another study with similar results, Chan et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) examined the effects of generative AI-based feedback, especially ChatGPT, on university students' essay-writing skills, revision results, motivation, and engagement. Learners who received Artificial-generated input throughout the revision process reported better levels of motivation and engagement, according to the research, suggesting that AI support tools might improve learners' writing motivation and learning processes..\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe current research focuses on analyzing students' capacity to acquire writing abilities and writing motivation by incorporating intelligent technology into the learning setting, namely the conversational chatbot of Gemini. This thirty-day research included 60 Omani EFL students. Although both the control and treatment groups got corrected textual comments from their instructors, the treatment group utilized Gemini as an additional tool in their educational process. The results revealed that the treatment group performed better than the control group in the posttest and delayed posttest. Furthermore, the customized writing motivation scale findings showed that learners' writing motivation in the treatment group rose significantly following the treatment period. This might be linked to the use of Gemini.\u003c/p\u003e \u003cp\u003eThe study's findings are useful for instructors, students, and curriculum designers. The Gemini is a strong tool that may help instructors improve their teaching approaches. This AI-powered solution would allow instructors to provide individualized and continuing feedback to learners, even outside lesson times, without considerably increasing their burden. This artificial intelligence application enables students to practice and improve their writing talents autonomously. This writing incentive encourages an active learning strategy where learners take charge of themselves by identifying mistakes, seeking assistance, and accessing information quickly. Incorporating these technologies into the curriculum enables developers to build more interactive and efficient learning settings that satisfy the technological demands of today's learners. The observed increase in students' writing abilities and motivation underscores the need to incorporate AI as a key component into language courses, ensuring that programs stay creative and sensitive to various learner requirements. Furthermore, incorporating AI technologies allows for a blended learning paradigm that combines conventional pedagogical techniques with technological advancements to boost learning results.\u003c/p\u003e \u003cp\u003eThis research contains various limitations that must be addressed thoroughly. The intervention was short, lasting about a month. Such a little time may have been insufficient to assess the long-term effects of employing the Gemini chatbot on the ability of learners to write and measure motivation. In addition, the sample size was small, consisting of 60 Omani EFL students; hence, the generalizability of the results is limited. The findings may not apply to learners from diverse cultural or academic backgrounds or to those with differing levels of English proficiency. Thirdly, although the study found that the Gemini chatbot improved writing ability and motivation, it did not investigate whether particular processes or qualities of the chatbot led to this result. It's unclear if the benefits were attributable to the quality of the chatbot feedback, the interactive possibility, or its ease of use. An in-depth examination of chatbot functionalities and their impact on educational results might be significantly more instructive.\u003c/p\u003e \u003cp\u003eThe study's results and scope lead to suggestions for further research. Future studies should examine the long-term impact of AI technology like the Gemini chatbot on language learning outcomes, such as whether the reported increases in writing competence and motivation are maintained over time. Furthermore, future studies might look at the usefulness of AI technology for various levels of language competency and student groupings. Examining whether comparable results can be found among beginning, intermediate, and advanced students from various cultural and language backgrounds would be fascinating. Third, subsequent research may examine the comparative efficacy of different AI systems in language learning. Researchers might, for example, compare the performance of Gemini to other artificially intelligent platforms in discovering the aspects that most successfully increase skill development and motivation to write. Furthermore, research efforts may center on incorporating AI technology into other components of language acquisition, such as reading comprehension, listening abilities, or speaking competency. Examining how AI may increase language abilities would give a more comprehensive knowledge of its influence in EFL environments.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors equally made significant contributions to the work discussed in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe researchers are aware of the Open Access Policy for this journal. There is no funding from any organization, and the researchers will cover the cost.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBefore initiating this work, ethical permission was secured from the Sohar University, Oman. All study protocols conformed to ethical considerations. Participants were approved of the research before deciding to engage in the study. They supplied their written informed permission before the initiation of the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis is to certify that the data is available on the Figshare website using the following link (https://figshare.com/s/fbd5001bca9f27f5b51f). All the Authors contributed equally and honestly in this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors clearly state that there is no conflict of interest in this research study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAmin, M. R. M., Ismail, I., \u0026amp; Sivakumaran, V. M. (2025). Revolutionizing education with Artificial Intelligence (AI)? 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Elsevier. https://doi.org/10.1007/s11423-017-9538-x\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"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 Feedback, Gemini, Writing Skills, Innovation, Motivation","lastPublishedDoi":"10.21203/rs.3.rs-6428474/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6428474/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe present study is intended to assess the impact of Gemini on the writing abilities of English as a Foreign Language (EFL) learners and their motivation. 60 Omani learners were divided randomly into treatment and control groups, each with 30 learners. The two groups were given researcher-made writing pretests, posttests, delayed posttests, and an adapted version of motivation questionnaire to compare students' performance in both variables before and after the treatment in both groups. The instrument's reliability and validity were carefully tested and approved. Both groups got textual correction input from their teachers, but the treatment group had the opportunity to engage with Gemini to receive extra automated feedback for their work. The findings revealed that initially, the control group progressed from the pretest to the posttest of writing but dramatically decreased in the delayed postttest of writing; however, learners in the treatment group outperformed those in the control group on the writing posttest and delayed posttest. Furthermore, it was shown that learners' motivation level was higher than the control group's. The study's findings are valuable for educators, learners, and curriculum creators.\u003c/p\u003e","manuscriptTitle":"The Effect of Gemini on EFL Learners' Writing Skills and Motivation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-28 06:02:22","doi":"10.21203/rs.3.rs-6428474/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":"7ddeb3e0-970f-4750-ac66-1010ea8a1a51","owner":[],"postedDate":"April 28th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-17T09:24:01+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-28 06:02:22","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6428474","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6428474","identity":"rs-6428474","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}