The Effectiveness of the AI-Based Pragmatic Model in Improving English Creative Writing Skills Among Third-Year High School Students in Riyadh

The Effectiveness of the AI-Based Pragmatic Model in Improving English Creative Writing Skills Among Third-Year High School Students in Riyadh | 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 Article The Effectiveness of the AI-Based Pragmatic Model in Improving English Creative Writing Skills Among Third-Year High School Students in Riyadh Ibrahim Algarni, Ali Aljodea This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6445869/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 This study aimed to evaluate the effectiveness of the AI-based deliberative model in enhancing creative writing skills in English among third-year high school students in Riyadh. A quasi-experimental approach was adopted, with participants divided into an experimental group that used the deliberative model and a control group that followed traditional methods. Students’ creative writing performance was assessed through a standardized test administered before and after the intervention, while data on the challenges encountered during the implementation of the model were also collected. The results revealed that students who used the AI-based deliberative model demonstrated significant improvements in all aspects of creative writing, including originality and creativity, organization and coherence, language use, critical and analytical thinking, and fluency and flexibility, compared to their peers in the control group. Statistical analyses indicated significant differences favoring the experimental group, confirming the model’s effectiveness in fostering creative writing. Regarding challenges, students reported difficulties in aligning AI-generated feedback with their personal writing style, understanding AI outputs, and technical issues. However, these obstacles did not hinder their overall progress. The study recommends integrating AI-assisted writing tools into creative writing curricula and providing structured training for students to optimize their use of AI in writing development. Social science/Education Social science/Science technology and society Introduction Language is an ever-evolving system shaped by linguistic, technological, and social influences (Durkin, 2014). Writing, as a fundamental skill, plays a vital role in communication, education, and professional discourse, adapting continuously to shifts in linguistic conventions and stylistic norms (Baron, 2024 ). With the advancement of technology, particularly in Artificial Intelligence (AI) and Natural Language Processing (NLP), new tools have emerged to support and refine writing practices (Ippolito et al., 2022 ). AI-powered applications such as Grammarly and ChatGPT offer grammar correction, stylistic enhancement, and text refinement, making them widely adopted for improving coherence and readability. Unlike traditional grammar checkers, these tools utilize machine learning algorithms to suggest rewording, simplify structures, and optimize text flow, thereby enhancing written communication. Despite their benefits, AI-driven writing assistants raise concerns regarding their impact on cognitive engagement, creative expression, and independent writing proficiency. While these technologies facilitate clarity and conciseness, they may also encourage a standardized style that prioritizes efficiency over linguistic complexity (Rudnicka, 2025 ). Grammarly, for instance, tends to favor direct phrasing, whereas ChatGPT generates text based on established linguistic patterns, potentially shaping users’ stylistic choices. As reliance on such tools grows, it becomes imperative to examine whether they enhance writing skills or contribute to a decline in critical thinking and self-editing abilities (Ippolito et al., 2022 ). This study explores the influence of AI-powered writing tools on contemporary writing practices, with a particular focus on their effects on sentence structure, stylistic tendencies, and the balance between conciseness and creative depth. By analyzing how these technologies modify written texts, the research aims to determine their role in shaping modern language use and writing development. Through this investigation, the study contributes to the broader discourse on AI’s impact on linguistic evolution and writing proficiency in the digital era.. Research Problem The integration of artificial intelligence into education has gained increasing attention due to its potential to enhance students’ creative writing skills. Creative writing is a multifaceted skill that requires originality and creativity, coherence and organization, language proficiency, critical and analytical thinking, as well as fluency and flexibility (Bereiter & Scardamalia, 1987 ). However, students learning English as a Foreign Language often struggle to develop these skills due to limited exposure to authentic linguistic input and the lack of immediate, constructive feedback (Graham, Hebert, & Harris, 2015). The AI-based deliberative model has emerged as an innovative tool designed to support students in improving their creative writing abilities by offering personalized, real-time feedback tailored to their individual needs. This model enables students to refine their writing, organize their ideas more effectively, and enhance their linguistic accuracy (Kumar & Rose, 2011 ). However, further empirical research is needed to evaluate the effectiveness of the AI-based deliberative model in secondary education settings, particularly in understanding the challenges students may face when using AI-assisted writing tools. Given this problem, this study seeks to address the following: How effective is the AI-based pragmatic model in enhancing the creative writing skills of third-year high school students in Riyadh? What are the differences in creative writing proficiency in English between students who use the AI-based deliberative model and their peers who do not? What challenges do third-year secondary students face when applying the AI-based deliberative model in creative writing in English? Research Hypotheses The AI-based pragmatic model is effective in enhancing the creative writing skills of third-year high school students. There is a statistically significant difference between students who use the pragmatic model and those who do not, in favor of the former group. Students face certain specific challenges while using the pragmatic model; however, these challenges do not hinder the improvement of their creative writing. Research Objectives To measure the effectiveness of the pragmatic model in improving creative writing skills. To compare the performance of students who use the pragmatic model with those who do not. To identify the challenges students, encounter while applying the pragmatic model. Significance of the Study Theoretical Significance: Enriching the educational literature on the use of artificial intelligence in English language teaching. Contributing to existing knowledge about pragmatic models and their impact on creative writing. Practical Significance: Presenting an innovative teaching model that educators can utilize to enhance creative writing skills. Assisting students in developing their writing abilities using modern tools. Guiding educational policymakers toward integrating AI into the curriculum. Research Methodology : The quasi-experimental approach. Research Instruments: 1.A standardized test to measure the level of creative writing before and after implementing the model. 2.A questionnaire to assess the effectiveness of the pragmatic model and the challenges students face. Study Delimitations Subject Delimitation : The study is confined to examining the effectiveness of the pragmatic model in improving creative writing. Population Delimitation: Third-year high school students. Geographical Delimitation: High schools in Riyadh. Time Delimitation: The 2024–2025 academic year. Research terms 1.Generative Artificial Intelligence (Generative AI): Conceptual Definition of Generative AI: Generative AI refers to a branch of artificial intelligence designed to autonomously generate new content, including text, images, music, and videos. It utilizes advanced deep learning models, such as Generative Adversarial Networks (GANs), Transformer-based architectures (e.g., GPT models), and Variational Autoencoders (VAEs). These models analyze large datasets, identify patterns, and produce human-like content with contextual accuracy and coherence (Ahmed, Okba, & Harous, 2024). Operational Definition of Generative AI in This Study: In this study, Generative AI refers to AI-based writing tools such as Grammarly and ChatGPT, which assist students in enhancing their creative writing skills. The research evaluates how these tools influence originality, text coherence, and idea generation, examining their role in improving students’ ability to create structured and imaginative compositions. 2 Creative Writing Conceptual Definition of Creative Writing: Creative writing is the artistic process of developing narratives, poetry, and other literary works that emphasize originality, expressive depth, and structured storytelling. Unlike traditional academic writing, creative writing prioritizes imagination, character development, and emotional engagement, allowing writers to convey ideas and emotions in unique and thought-provoking ways (Shanahan & Clarke, 2023). Operational Definition of Creative Writing in This Study: In this study, creative writing refers to students’ ability to produce original literary compositions that demonstrate authenticity, coherence, and inventiveness. These writings are assessed based on originality, structural organization, depth of content, and the ability to develop novel ideas and articulate them effectively. The study examines how Grammarly and ChatGPT influence students’ creative writing performance, focusing on their impact on originality and text coherence Theoretical Framework and Previous Studies First Theme: 1.1The Evolution of Generative Artificial Intelligence : 1.Early Foundations (1940s - 1950s). The concept of artificial intelligence (AI) began taking shape in the 1940s when Warren McCulloch and Walter Pitts proposed a mathematical model for artificial neurons, laying the groundwork for neural networks (McCulloch & Pitts, 1943). A few years later, Alan Turing published his seminal paper Computing Machinery and Intelligence, in which he introduced the Turing Test to assess a machine’s ability to exhibit intelligent behavior comparable to humans (Turing, 1950). 2.Formative Years and the Birth of AI (1956 - 1970s). In 1956, the Dartmouth Conference officially marked the birth of artificial intelligence as an academic field, led by John McCarthy, Marvin Minsky, Nathaniel Rochester, and Claude Shannon (McCarthy et al., 1955). During the 1960s, expert systems such as DENDRAL, designed for chemical analysis, and MYCIN, developed for medical diagnosis, demonstrated AI’s potential in specialized fields (Feigenbaum & Buchanan, 1971; Shortliffe, 1976). 3.Challenges and Renewed Interest (1970s - 1980s). Despite early optimism, AI research faced significant setbacks in the 1970s due to unmet expectations and funding cuts, leading to what became known as the “AI winter” (McCarthy et al., 1955). However, the 1980s saw a resurgence in AI development, fueled by increased computational power and the continued success of expert systems in real-world applications (Feigenbaum & Buchanan, 1971). 4.The Emergence of Deep Learning (1990s - 2010s). By the 1990s, advances in machine learning algorithms and computing resources enabled more sophisticated AI models. However, it wasn’t until 2014 that Ian Goodfellow and his colleagues introduced Generative Adversarial Networks (GANs), a breakthrough approach to generating realistic data through adversarial training between two neural networks (Goodfellow et al., 2014). GANs played a crucial role in revolutionizing image synthesis, video generation, and other AI-generated content. 5.The Rise of Large Language Models (2018 - Present). The late 2010s marked the rise of large-scale generative AI models, beginning with OpenAI’s GPT-1 in 2018, which showcased the power of unsupervised pre-training for natural language processing (Radford et al., 2018). This was followed by GPT-2 in 2019, which demonstrated a remarkable ability to generate coherent and contextually relevant text (Radford et al., 2019). The introduction of GPT-3 in 2020 represented a significant leap, with its 175 billion parameters enabling it to perform complex language tasks without requiring task-specific training (Brown et al., 2020). More recently, text-to-image generative models such as DALL·E have expanded AI’s creative potential by allowing users to generate high-quality images from text descriptions (Ramesh et al., 2021). 1.2 Previous Studies on Generative AI: 1. Ferrag et al. (2023): This study explored the role of Generative AI in cybersecurity, particularly in detecting cyber threats within 6G-enabled IoT networks. The findings indicated that Generative AI models achieved 95% accuracy in identifying cyber threats, demonstrating their potential in automating cybersecurity processes and improving predictive threat detection. 2.Gupta et al. (2023): This research investigated the ethical concerns and security risks associated with Generative AI, highlighting issues such as privacy breaches, misinformation, and academic integrity violations. The study recommended the implementation of ethical guidelines and regulatory frameworks to ensure the responsible use of AI technologies in academic and professional settings. 3. Ahmed et al. (2024): This systematic review examined the applications of Generative AI in cybersecurity automation, focusing on its advantages and limitations. The study emphasized the need for balancing AI-driven automation with ethical considerations to prevent misuse and ensure transparency in AI-generated outputs. 1.3 AI-Powered Writing Tools: Grammarly and ChatGPT: The Role of Grammarly and ChatGPT in Writing Development Grammarly and ChatGPT are among the most widely used AI-powered writing tools, offering users real-time assistance in refining their writing. -Grammarly serves as a proofreading and grammar correction tool, helping writers enhance clarity, coherence, and grammatical accuracy. -ChatGPT, on the other hand, provides text expansion, contextual suggestions, and automated feedback, allowing users to develop richer content and improve text fluency. 1.4 Previous Studies on Grammarly and ChatGPT: 1.Calma et al. (2022): This study examined Grammarly’s impact as an instructional tool in university-level writing. The results showed that Grammarly effectively reduced grammar and sentence structure errors; however, students who depended solely on Grammarly without instructor feedback showed limited improvement in critical thinking and argument development. 2.Tambunan et al. (2022): This longitudinal study investigated Grammarly’s influence on EFL (English as a Foreign Language) students’ writing skills. The findings revealed that while Grammarly significantly improved grammatical accuracy, excessive reliance hindered students’ ability to self-correct their writing without AI assistance. 3. Liu et al. (2024): This study analyzed the impact of ChatGPT on creativity in writing. The research found that while ChatGPT enhanced idea generation and text coherence, prolonged use led to repetitive patterns and reduced originality, affecting students’ ability to produce truly creative and unique content. Second Theme: 2 Creative Writing: 2.1 Importance Writing Skills: “ Creative writing fosters imagination and allows students to express their thoughts and feelings in a unique way, enhancing their engagement and confidence.” (New Educ, 2023). 2.2Creative Writing Skills: Creative thinking is a field that explores the cognitive processes involved in producing and interpreting language. Many educators, including Abanmi (2018), Abdul-Bari (2014), and Al-Sharif (2020), have identified several key skills associated with creative thinking. The researcher summarizes them as follows: 1. Originality and Creativity: The ability to generate new and unique ideas while expressing them in innovative and unconventional ways (Abanmi, 2018). 2. Organization and Coherence: The skill of structuring ideas and information in a logical and cohesive manner, ensuring clarity and ease of comprehension for the reader (Abdul-Bari, 2014).. 3. Language Use: Mastery of linguistic rules and the effective use of vocabulary and sentence structures to communicate ideas with precision and clarity, while incorporating rhetorical and stylistic elements (Al-Sharif, 2020). 4. Critical and Analytical Thinking: The ability to objectively analyze and evaluate information and ideas,leading to logical conclusions supported by evidence (Abanmi, 2018). 5.Fluency and Flexibility: Fluency refers to the capacity to produce a large volume of ideas or written content within a short period, whereas flexibility involves adapting and varying these ideas by changing approaches or writing styles as needed (Abdul-Bari, 2014).. 2.3 Previous Studies on Creative Writing: 1.Gomez-Rodriguez & Williams (2023): This study compared human-written creative texts with AI-generated narratives, finding that while AI-generated texts maintained structural coherence, they lacked emotional depth and originality. 2. Shanahan & Clarke (2023): This research assessed the role of AI in creative storytelling, concluding that AI-assisted writing improved organization and fluency but struggled to convey deeper themes and emotional complexity. 3. Chakrabarty et al. (2023): This study analyzed AI’s ability to generate original narratives, revealing that while AI models excel at fluency and coherence, they lack the personal nuances and creativity inherent in human writing. Research Procedures Study Population: The study population consisted of 29,435 senior high school students in Riyadh, as reported by the Department of Education in the Riyadh Educational Administration for the academic year 1446 AH. Study Sample: Participants in the current study were divided into two phases: 1.Phase One: This phase involved a pilot sample, referring to the participants on whom the researcher initially administered the study instruments to evaluate their psychometric properties. This sample comprised 22 senior high school students from Riyadh, recruited from Prince Saud bin Abdulaziz High School, with ages ranging between 18 and 19 years. The researcher relied on this group to determine the psychometric characteristics of the Creative Writing Skills Test. Phase Two: This phase represents the main sample, consisting of 90 senior high school students from Riyadh, recruited from Abdulaziz Al-Kho waiter High School, and divided into 45 students representing the [group]. The experimental group consisted of 45 senior high school students aged between 18 and 19 years, while the control group comprised 45 senior high school students within the same age range. Research Instruments First: Creative Writing Skills Test (Developed by the Researcher) The current test aims to assess creative writing skills, specifically originality and creativity, organization and coherence, language usage, critical and analytical thinking, as well as fluency and flexibility among senior high school students. Steps in Test Development: The test was constructed through several steps until its final version was reached, as follows: A. Reviewing definitions of creative writing skills as presented in theoretical frameworks, as well as previous studies and research that highlighted the importance of these skills. B. Examining various Arabic and foreign tests designed to measure creative writing skills, such as the Torrance Tests of Creative Thinking (TTCT) and the Test of Written English (TWE). C. Operationally defining the concept of creative writing skills and formulating questions for each skill based on a review of the theoretical frameworks and previous studies concerning the indicators of creative writing skills. During the formulation and construction of the test questions, the researcher adhered to the following guidelines : The questions should be appropriate for the level of the sample students (senior high school students in Riyadh). They should be clear, simple, concise, and not compound, representing a single idea. They must be directly related to the subject of measurement and the specific skill being assessed. Answer Key for the Test Questions: The researcher developed an answer key for the Creative Writing Skills Test. This key was meticulously designed to ensure objectivity and consistency in evaluating the participants' responses. It is based on clear criteria that include the quality of the idea, content coherence, language style, textual cohesion, as well as grammatical and spelling accuracy. Each criterion is assigned a specific score according to a rating scale that reflects different levels of performance. The evaluation is conducted manually following these standards to ensure an accurate assessment and measurement of the actual improvement in students' creative writing skills. Psychometric Properties of the Creative Writing Skills Test: First: Validity: The researcher evaluated the test’s validity based on three types of validity, as follows: A. Expert Validity: The test, in its initial version comprising four questions, was presented to a group of five expert educators specializing in curriculum and teaching methods. They were requested to provide feedback on the test's validity regarding the clarity of its instructions, the accuracy of the wording of the test items, the appropriateness of each question for the skill it intended to measure, the test’s overall representation of the target construct, and the suitability of the questions for senior high school students in Riyadh. Additionally, they were allowed to suggest other appropriate modifications. The experts' agreement rates on the evaluation criteria ranged between 80.0% and 100%, with an average agreement rate of 90.0%. This high level of consensus supports the validity of the test for measuring the intended constructs. Consequently, the linguistic phrasing of some questions was adjusted based on the experts’ recommendations, and the experts’ agreement rate was considered an indicator of the test's validity, thereby instilling confidence in the results obtained from its administration to the sample. B. Criterion-Related Validity: The researcher also assessed the test’s validity using criterion-related validity by employing the Mann–Whitney U test. This analysis aimed to determine the significance of the differences between high- and low-performing students in both specific skills and the overall score on the Creative Writing Skills Test among the pilot sample of senior high school students in Riyadh. The following table illustrates these findings: Table (1): Results of the Mann-Whitney Test Indicating Significant Differences Between the Mean Rank Scores of the High and Low Groups on the Creative Writing Skills Test Variable Group N Mean Rank Sum of Ranks U Z Value p-value Originality and Creativity High 6 9.5 57 0 -3.083 0.002 Low 6 3.5 21 Organization and Coherence High 6 9.42 56.5 0.5 -2.827 0.005 Low 6 3.58 21.5 Language Usage High 6 9.33 56 1 -2.766 0.006 Low 6 3.67 22 Critical and Analytical Thinking High 6 9.5 57 0 -2.934 0.003 Low 6 3.5 21 Fluency and Flexibility High 6 9.5 57 0 -2.918 0.004 Low 6 3.5 21 Overall Test Score High 6 9.5 57 0.000 -2.892 0.004 Low 6 3.5 21 It is evident from the previous table that the Z-values are statistically significant at a level below 0.05 for all test domains and the overall score, with values of -3.083, -2.827, -2.766, -2.934, -2.918, and − 2.892, respectively. This indicates that statistically significant differences exist between the low and high groups in all the test skills and the overall score in favor of the high group. Such findings demonstrate that the Creative Writing Skills Test possesses discriminative power in distinguishing between low and high performers, thereby instilling confidence in the test’s validity. C. Internal Consistency Validity of the Test After the expert review, the researcher administered the test in the field using data from the pilot sample of 22 senior high school students. The internal consistency validity was determined by calculating the square root of the reliability coefficient (Al-Sayed, 2006 , p. 402). The degree of internal consistency is presented in the following table: Table (2) – Internal Consistency Validity Results of the Creative Writing Skills Test (N = 22) Senior High School Students It is evident from Table (2) that the square root values of the reliability coefficients ranged from 0.874 to 0.947, approaching one. This confirms the validity of the Creative Writing Skills Test. Secondly: Calculation of Internal Consistency This was carried out by computing the correlation coefficient between the score of each skill and the overall test score using the pilot sample of 22 senior high school students in Riyadh. The following table (Table (2)) presents the correlation coefficients between each skill score and the overall test score. Table (3) – Correlation Coefficients between Each Skill Score and the Overall Test Score (N = 22) Senior High School Students ** : Significant at the 0.01 level *: Significant at the 0.05 level The table (3) shows that the correlation coefficients between creative writing skills and the total test score range between (0.669–0.846), all of which are statistically acceptable values. This confirms the internal consistency of the test. Third: Reliability The researcher calculated the reliability of the Creative Writing Skills Test using the Kuder-Richardson method, as it is suitable for tests with dichotomous scoring (i.e., where answers are scored as either 0 or 1) (Abu Hatab, Othman, & Sadiq, 2008, p. 152). The following table () presents the reliability coefficients for each skill in the test and the total score: It is evident from Table () that the correlation coefficients between creative writing skills and the total score range between (0.669–0.846), all of which are statistically significant values, indicating the internal consistency of the test. Table (4): Reliability Coefficients for the Domains and Total Score of the Creative Writing Skills Test (N = 22) Third-Year Secondary Students No Skills and Overall Test Score Reliability Coefficient 1 Originality and Creativity 0.876 2 Organization and Coherence 0.764 3 Language Use/Usage 0.891 4 Critical and Analytical Thinking 0.787 5 Fluency and Flexibility 0.799 Overall Test Score 0.898 It is evident from Table (4) that the overall reliability coefficient for creative writing skills is high, reaching (0.898) for the total test items. The reliability of the domain's ranges between (0.764) as a minimum and (0.891) as a maximum, indicating that the test has a high degree of reliability and can be trusted for field application in the research. Description of the Final Version of the Test: The Post-Test Objective: The post-test aims to measure the effectiveness of the dialogic model based on artificial intelligence in enhancing creative writing skills among third-year secondary students. The test includes a set of essay questions designed to assess elements of creative writing, such as idea development, organization, style, and linguistic coherence. Answers will be manually graded according to strict criteria to ensure objectivity, with a specified time limit for responses. Participants' answers will be kept strictly confidential and used exclusively for scientific research purposes. Determining the Appropriate Time for the Creative Writing Skills Test: The researcher determined the suitable duration for answering the Creative Writing Skills Test by calculating the time taken by each student in the pilot sample. The average time required to complete the test was then computed. The researcher concluded that the appropriate time to answer the test questions is 35 minutes, with an additional 5 minutes for instructions, making the total test duration 40 minutes, approximately equivalent to a class period. Second: Questionnaire for Verifying the Effectiveness of the Experimental Treatment (Prepared by the Researcher) The researcher developed a questionnaire to verify the effectiveness of the experimental treatment. The purpose of this questionnaire is to gather students’ opinions regarding the suitability of the program for them, the benefits gained, and to ensure that the implementation process of the dialogic model based on artificial intelligence achieves its intended objectives. Description of the Questionnaire: The initial version of the questionnaire consists of (20) statements aimed at understanding students' opinions on the training procedures included in the dialogic model based on artificial intelligence for developing creative writing skills, the suitability of various activities and stimuli for students, the extent to which trainees have benefited from the dialogic model based on artificial intelligence, and the degree of improvement in students' skills at the end of the implementation of the dialogic model based on artificial intelligence. The researcher ensured that the questionnaire items were formulated in a way that made the vocabulary appropriate for the sample population, while also being clear, simple, and concise. Additionally, the items were designed to align with the procedures of the dialogic model based on artificial intelligence. Time Required for Completing the Questionnaire: There is no specific time limit for completing this questionnaire. Responses are recorded by placing a checkmark (✔) under one of the following options: Strongly Agree, Agree, Neutral, Disagree, Strongly Disagree. Validity of the Questionnaire: The researcher verified the validity of the questionnaire using expert judgment validity. The initial version of the questionnaire, consisting of 20 statements, was reviewed by a panel of five experts specializing in curricula and teaching methods. The agreement percentages among the experts on the evaluation criteria ranged from 80–100%, which are considered high and acceptable. This indicates confidence in the reliability of the results that can be obtained upon implementing the questionnaire. Controlling Extraneous Variables: The researcher controlled extraneous variables that could potentially influence the dependent variable. The independent variable in this study is the dialogic model based on artificial intelligence, while the dependent variable is creative writing skills. Below is a list of key extraneous variables identified through theoretical frameworks and previous studies, which may impact the dependent variable: chronological age, intelligence, socio-economic level, gender, and pre-test measurement of creative writing skills. A. Chronological Age: The chronological age of participants in the current study ranges between 18 and 19 years. To control for this variable, participants above 19 years old were excluded. To ensure equivalence in age distribution between the experimental and control groups, the researcher tested for significant differences in participants' ages using the T-Test. The following table presents the results. Table (5): T-Value and Its Statistical Significance for the Differences Between the Experimental and Control Groups in the Chronological Age Variable No Group N Mean Standard Deviation t-value Significance Level 1 Experimental 45 18.25 0.539 -1.68 0.128 2 Control 45 18.43 0.604 Not significant The previous table demonstrates that the difference between the experimental and control groups in the chronological age variable is not statistically significant. The mean age for the experimental group was (18.25) years with a standard deviation of (0.539), while the mean age for the control group was (18.43) years with a standard deviation of (0.604). The T-value was (1.680), which is not statistically significant, indicating that both groups are equivalent in terms of chronological age. B. Gender The research sample consisted only of male students to eliminate the potential effect of gender on the dependent variable. Additionally, this selection facilitated easier interaction and training in creative writing skills through the dialogic model based on artificial intelligence. C. Intelligence The researcher considered intelligence as a potential extraneous variable that could influence creative writing skills. Therefore, intelligence levels were controlled in both the experimental and control groups by administering the Verbal Intelligence Test (prepared by Jaber & Omar, 2007). The researcher then analyzed the significance of differences between the two groups using the T-Test, and the following table presents the results. Table (6): T-Value and Its Statistical Significance for the Differences Between the Experimental and Control Groups in the Intelligence Variable No Group N Mean Standard Deviation t-value Significance Level 1 Experimental 45 71.343 6.165 0.723- 0.714 2 Control 45 72.031 7.544 Not significant The previous table demonstrates that the difference between the experimental and control groups in the intelligence variable is not statistically significant. The mean intelligence score for the experimental group was (71.343) with a standard deviation of (6.165), while the mean intelligence score for the control group was (72.031) with a standard deviation of (7.544). This indicates that both groups are equivalent in terms of intelligence. D. Socio-Economic Level: The sample was selected from a single school, Abdulaziz Al-Khuwaiter Secondary School in Riyadh. This ensured that both the experimental and control groups came from the same geographical area, making their socio-economic level as similar as possible. The researcher found that there was a reasonable level of homogeneity in the economic and social status of the participants. E. Pre-Test Measurement of Creative Writing Skills: To ensure equivalence between the experimental and control groups, the researcher administered the Creative Writing Skills Test as a pre-test. The T-value was calculated to determine whether there were significant differences between the mean scores of the two groups in creative writing skills before the experimental treatment. The following table presents the results. Table (7): Means, Standard Deviations, T-Value, and Significance Level for the Experimental and Control Groups in the Pre-Test Measurement of Creative Writing Skills Skills and Total Score Group N Mean Standard Deviation t-value Significance Level Originality and Creativity Experimental 45 4.2667 1.78885 -0.653 0.515 Control 45 4.5333 2.07364 Skills in Presenting a Claim or Idea Experimental 45 4.2667 2.14688 0.416 0.679 Control 45 4.0889 1.90481 Language Use Experimental 45 3.7556 1.97893 0.443 0.659 Control 45 3.5778 1.82768 Skills and Total Score Group N Mean Standard Deviation t-value Significance Level Critical and Analytical Thinking Experimental 45 4.1333 2.13839 -0.379 0.705 Control 45 4.3111 2.30437 Fluency and Flexibility Experimental 45 3.6222 1.55635 0.433 0.666 Control 45 3.4889 1.35885 Total Test Score Experimental 45 20.0444 7.17304 0.032 0.974 Control 45 20 5.78792 Findings on Pre-Test Equivalence in Creative Writing Skills The previous table indicates that the T-value was not statistically significant between the experimental and control groups in the overall score of creative writing skills as well as in each individual skill. This confirms that both groups were equivalent in the pre-test measurement of creative writing skills. Research Steps and Procedures : Reviewing previous studies related to the research topic. Examining prior research that focused on the dialogic model based on artificial intelligence and its role in developing creative writing skills among third-year secondary students. Designing and constructing the Creative Writing Skills Test, followed by validation by a panel of experts. Designing the dialogic model based on artificial intelligence, and presenting it to a group of experts for evaluation. Selecting the research sample and assigning participants to experimental and control groups. Teaching the dialogic model based on artificial intelligence to the experimental group, while the control group follows the traditional method. Administering the post-test to both experimental and control groups. Conducting statistical analysis of the collected data. Deriving the research findings. Interpreting and discussing the results in light of the research questions and objectives. Providing a set of recommendations and suggestions based on the research findings. Statistical Methods: To verify the validity of the study's hypotheses, the researcher employed the following statistical methods: Pearson Correlation Coefficient – Used to determine the correlation between each test question and the total score of the Creative Writing Skills Test. Mann-Whitney Test – Used to examine differences between high and low scorers on the Creative Writing Skills Test (validity through extreme group comparison). Frequencies and Percentages – Used to analyze the responses of the experimental group on the questionnaire verifying the effectiveness of the experimental treatment. Independent Samples T-Test – Used to test the statistical significance of the differences between the mean post-test scores of the experimental and control groups in creative writing skills. Paired Samples T-Test – Used to assess the statistical significance of the differences between the pre-test and post-test mean scores of the experimental group in creative writing skills. Eta-Squared (η²) – Used to measure the effect size of the experimental treatment (the dialogic model based on artificial intelligence) on the dependent variable (creative writing skills) among third-year secondary students in Riyadh. Research Findings : Findings Related to the First Research Question : The first research question states: "What is the effectiveness of the experimental treatment (the dialogic model based on artificial intelligence) based on the results of the questionnaire verifying the effectiveness of the experimental treatment?" To answer this question, the researcher calculated the percentages of responses from the experimental group for each statement in the questionnaire. The following table presents the percentage distributions of the experimental group’s responses to each statement in the questionnaire for verifying the effectiveness of the experimental treatment. Table (8): Percentage Distributions of the Experimental Group’s Responses to Each Statement in the Questionnaire for Verifying the Effectiveness of the Experimental Treatment Statement No. Strongly Agree Agree Neutral Disagree Strongly Disagree No 45 % No 45 % No 45 % No 45 % No 30 % 1 32 71.11% 10 16.67% 2 4.44% 1 2.22% --- --- 2 40 88.89% 4 8.89% --- --- --- --- --- --- 3 33 73.33% 9 20.00% 1 2.22% 2 4.44% --- --- 4 29 64.44% 11 24.44% 3 6.67% 2 4.44% --- --- 5 30 66.67% 12 26.67% 3 6.67% --- --- --- --- 6 34 75.56% 6 13.33% 4 8.89% 1 2.22% --- --- 7 42 93.33% 1 2.22% 1 2.22% 1 2.22% --- --- 8 32 71.11% 9 20.00% 2 4.44% --- --- 2 4.44% 9 34 75.56% 8 17.78% 2 4.44% 1 2.22% --- --- 10 30 66.67% 13 28.89% --- --- 2 4.44% --- --- 11 35 77.78% 7 15.55% 3 6.67% --- --- --- --- 12 33 73.33% 7 15.55% 4 8.89% 1 2.22% --- --- 13 31 68.89% 10 22.22% 2 4.44% 2 4.44% --- --- 14 41 91.11% 4 8.89% --- --- --- --- --- --- 15 38 84.44% 4 8.89% 1 2.22% 2 4.44% --- --- 16 32 71.11% 8 17.78% 5 11.11% --- --- --- --- 17 35 77.78% 10 22.22% --- --- --- --- --- --- 18 43 95.56% 2 4.44% --- --- --- --- --- --- 19 41 91.11% 2 4.44% 1 2.22% --- --- 1 2.22% 20 29 64.44% 12 13.33% 2 4.44% 2 4.44% --- --- The previous table shows that the percentage distribution of responses from the experimental group to the questionnaire items was as follows: those who responded with "Strongly Agree" ranged between (64.44% − 95.56%), those who responded with "Agree" ranged between (2.22% − 28.89%), those who responded with "Neutral" ranged between (2.22% − 11.11%), those who responded with "Disagree" ranged between (2.22% − 4.44%), and those who responded with "Strongly Disagree" ranged between (2.22% − 4.44%). This indicates that the implementation of the dialogic model successfully achieved its objectives, was suitable for students, and benefited them. It also highlights the variety of stimuli included in the model and the appropriateness of the activities and tasks it incorporated for third-year secondary students. Regarding the findings related to the second research question , which states: "What is the effectiveness of the dialogic model based on artificial intelligence in developing creative writing skills among third-year secondary students in Riyadh?" the researcher formulated the following two hypotheses: -"There are no statistically significant differences at the significance level (α ≤ 0.05) between the mean scores of the experimental group students and the control group students in the post-test of creative writing skills." -"There are no statistically significant differences at the significance level (α ≤ 0.05) between the mean scores of the pre-test and post-test for the experimental group in the creative writing skills test." To test the validity of the first hypothesis related to the previous research question, the researcher calculated the T-value for independent samples to examine the differences between the mean scores of students in the experimental and control groups in the post-test of creative writing skills. The results are presented in the following table: Table (9): Means, Standard Deviations, T-Value, and Significance Level for the Experimental and Control Groups in the Post-Test of Creative Writing Skills and Total Score : No. Creative Writing Skills Group N Mean Standard Deviation t-value Significance Level 1 Originality and Creativity Experimental 45 7.244 1.227 4.367 0 Control 45 5.888 1.681 2 Organization and Coherence Experimental 45 6.933 1.483 3.945 0 Control 45 5.711 1.455 3 Language Use Experimental 45 7.022 1.499 4.208 0 Control 45 5.733 1.404 4 Critical and Analytical Thinking Experimental 45 6.955 1.757 3.573 0 Control 45 5.8 1.272 Fluency and Flexibility Experimental 45 6.688 1.916 3.355 0 Control 45 5.533 1.289 Total Test Score Experimental 45 34.844 6.56 4.81 0 Control 45 28.666 5.584 The previous table shows that the T-values for the creative writing skills (Authenticity and Creativity, Organization and Coherence, Language Usage, Critical and Analytical Thinking, Fluency and Flexibility), as well as the total score of the Creative Writing Skills Test, were 4.367, 3.945, 4.208, 3.573, 3.355, and 4.810, respectively. These values are statistically significant at a level lower than (0.01), indicating the presence of statistically significant differences between the experimental and control groups in post-test creative writing skills in favor of the experimental group. By examining the mean scores for the five skills and the total creative writing skills score, it was evident that they were higher in favor of the experimental group. As a result, the alternative hypothesis is accepted, and the null hypothesis is rejected. Effect Size of the Experimental Treatment (Dialogic Model Based on Artificial Intelligence) on the Dependent Variable (Creative Writing Skills) To further evaluate the impact of the dialogic model, the effect size of the experimental treatment on creative writing skills was calculated using Eta-squared (η²). The following section presents the findings related to the magnitude of the effect. The researcher used Eta-squared (η²) to measure the effect size of the experimental treatment (the dialogic model based on artificial intelligence) on the dependent variable (creative writing skills). The researcher calculated the Eta-squared (η²) value based on the T-value. According to Murad ( 2000 : 246), an effect size that explains approximately 0.02 of the total variance indicates a small effect, while an effect that explains 0.06 of the total variance indicates a moderate effect, whereas an effect that explains approximately 0.15 or more indicates a large effect (Murad, 2000 , p. 246 ). Table (10) Eta-Squared (η²) Value and the Effect Size of the Dialogic Model Based on Artificial Intelligence in Developing Creative Writing Skills. No. Creative Writing Skills t-value Degrees of Freedom η² Effect Size 1 Originality and Creativity 4.37 88 0.178 Large 2 Organization and Coherence 3.95 88 0.15 Large 3 Language Use 4.21 88 0.167 Large 4 Critical and Analytical Thinking 3.57 88 0.127 Medium 5 Fluency and Flexibility 3.36 88 0.113 Medium Total Score of Creative Writing Skills 4.81 88 0.208 Large The table (10) illustrates that the Eta-squared (η²) value for: - The effect size of the dialogic model based on artificial intelligence on creative writing skills reached (0.178), (0.150), (0.167), and (0.208) for three skills which are authenticity and creativity, organization and coherence, and language usage, along with the total score, indicating a large effect size. For the remaining two skills, which are critical and analytical thinking, and fluency and flexibility, the Eta-squared (η²) values were (0.127) and (0.113), indicating a moderate effect size. To test the validity of the second hypothesis related to the previous research question, which states: "There are no statistically significant differences at the significance level (α ≤ 0.05) between the mean scores of the pre-test and post-test for the experimental group in the creative writing skills test," the paired samples T-test was used to examine the differences between the mean scores of the pre-test and post-test for the experimental group in the creative writing skills test. The results are presented in the following table: Table (11): Means, Standard Deviations, T-Value, and Statistical Significance of the Differences Between the Pre-Test and Post-Test Mean Scores for the Experimental Group in Creative Writing Skills Among Third-Year Secondary Students Variable Measurement N Mean Standard Deviation Mean Differences Standard Deviation of Differences t-value Significance Level Originality and Creativity Pre-test 45 4.266 1.788 -2.97778 1.40598 -14.208 0 Post-test 45 7.244 1.227 Organization and Coherence Pre-test 45 4.266 2.146 -2.66667 1.4771 -12.111 0 Post-test 45 6.933 1.483 Language Use Pre-test 45 3.755 1.978 -3.26667 1.85129 -11.837 0 Post-test 45 7.022 1.499 Critical and Analytical Thinking Pre-test 45 4.133 2.138 -2.82222 1.35326 -13.99 0 Post-test 45 6.955 1.757 Fluency and Flexibility Pre-test 45 3.622 1.556 -3.06667 1.38826 -14.818 0 Post-test 45 6.688 1.916 Total Score of the Creative Writing Skills Scale Pre-test 45 20.044 7.173 -14.8 3.8055 -26.089 0 Post-test 45 34.844 6.56 The table (11) shows that the T-values for creative writing skills and the total score of the Creative Writing Skills Test among third-year secondary students were (-14.208), (-12.111), (-11.837), (-13.990), (-14.818), and (-26.089), respectively. These values are statistically significant at a level lower than (0.01), indicating the presence of statistically significant differences between the pre-test and post-test scores for the experimental group in both individual skills and the total score of the Creative Writing Skills Test. By examining the mean scores in the individual skills and the total creative writing skills score, it is evident that the difference favors the post-test scores, meaning that the current hypothesis was not confirmed. As a result, the null hypothesis is rejected, and the alternative hypothesis is accepted. This confirms the effectiveness of the dialogic model based on artificial intelligence in developing creative writing skills among third-year secondary students. Findings Related to the Third Research Question The third research question states: "What are the challenges that third-year secondary students may face while applying the dialogic model based on artificial intelligence in creative writing in English?" To answer this question, the researcher calculated the frequencies and percentages of the responses from the experimental group participants. The results are presented in the following table: Table (12): Challenges Faced by Third-Year Secondary Students While Applying the Dialogic Model (N = 45 students) Percentage (%) Frequency Variable Categories Variable 26.70% 12 Difficulty in understanding AI outputs Challenges that high school seniors may face while applying the transactional model 22.20% 10 Incompatibility of feedback with my personal writing style 11.10% 5 Time required to interact with the model 40% 18 Technical issues in using AI tools 100% 45 Total The table (12) shows that 26.7% of the sample faced difficulty in understanding AI-generated outputs, 22.2% experienced issues with the mismatch between AI feedback and their personal writing style, 11.1% found the time required to interact with the model challenging, and 40.0% encountered technical difficulties while using AI tools. This indicates that the majority of the participants faced technical issues when using AI-based tools. Declarations Ethical Considerations and Data Availability Ethical Approval This study received ethical approval from the Permanent Committee for Research Ethics at King Saud University (Ref No: KSU-HE-24-1133), granted on December 17, 2024. In addition, approval was obtained from the General Administration of Education in Riyadh, Ministry of Education, Saudi Arabia (Letter No. 4300777452), dated 17/11/1445H. The official approval authorized the implementation of the study among third-year high school students, including the use of both a questionnaire and a writing test. Written informed consent from the students’ parents or legal guardians was obtained on 18/11/1445H, prior to the administration of any research instruments. All research procedures adhered strictly to ethical guidelines issued by the relevant institutional and national authorities. The entire research process was conducted in accordance with the ethical standards of the Declaration of Helsinki (1964) and its subsequent amendments. Informed Consent Prior to participation, informed consent was obtained from all students and their guardians. Participants were assured that their responses would remain confidential and used solely for academic research purposes. Data Availability Statement The datasets generated and analyzed during the current study are not publicly available due to participant confidentiality agreements but are available from the corresponding author upon reasonable request Research Summary This study aimed to investigate the effectiveness of the dialogic model based on artificial intelligence in enhancing creative writing skills among third-year secondary students in Riyadh. Given the importance of creative writing in developing linguistic and cognitive skills, a quasi-experimental design was applied to two groups: an experimental group, which received training using the dialogic model, and a control group, which followed traditional methods. The findings revealed that the experimental group outperformed the control group in developing authenticity and creativity, organization and coherence, fluency, critical thinking, and language usage, confirming the model’s effectiveness in improving creative writing skills. Additionally, the study identified technical and procedural challenges encountered by students during implementation, emphasizing the need for continuous technical support and adaptation of AI tools to meet learners’ needs. Recommendations: 1.Enhancing the application of AI in developing creative writing skills: Educational institutions should adopt AI-based strategies to enhance creative writing skills, emphasizing interactive learning environments. Incorporating AI into curricula: It is essential to develop curricula that integrate AI for improving writing skills while training teachers on innovative teaching strategies. 2.Conducting in-depth studies on individual differences in student responses: Further research is recommended to understand how individual differences, such as cognitive styles and linguistic proficiency, impact students' ability to benefit from AI. 3.Designing teacher training programs: Training programs should be developed to equip teachers with the knowledge and skills to integrate AI in creative writing instruction, enhancing the learning experience for students. Integrating linguistic analysis with AI: AI-powered text analysis tools should be combined with traditional assessments to improve the accuracy of measuring students' progress in writing skills. 4.Addressing technical challenges associated with AI use: Since many students face technical difficulties when using AI tools, continuous technical support should be provided, along with the development of more user-friendly and interactive interfaces. 5.Developing adaptive feedback mechanisms: AI feedback mechanisms should be improved to better align with students' individual writing styles, making the learning process more personalized and effective. Suggestions: 1.Expanding the scope of research to include larger and more diverse samples: Future studies should be conducted on larger student samples in different educational environments to ensure broader generalizability of the results. 2.Studying the impact of different types of artificial intelligence: Comparative studies between various AI models are recommended to determine which is most effective in enhancing students' creative writing skills. 3.Developing more accurate assessment tools: It is suggested to design advanced AI-based evaluation tools to assess students' progress in creative writing skills with greater objectivity and precision. 4.Analyzing the impact of AI use on students' motivation: Research should focus on the relationship between AI utilization and students’ motivation levels in learning creative writing, contributing to the development of more engaging teaching strategies. 5.Examining the impact of AI on creative aspects of writing: Studies should investigate how AI affects students’ critical and creative thinking to ensure they do not become passive recipients but instead develop their ability to generate original ideas. Conclusion This study highlights the growing role of artificial intelligence in enhancing academic skills, particularly in the field of creative writing. The findings confirmed the effectiveness of the dialogic model in improving students' performance, while also identifying certain technical challenges that require innovative solutions. Based on these results, it is recommended to expand future research, develop AI tools that are more adaptive to students' needs, and integrate these technologies more extensively into educational curricula to maximize their potential in enhancing creative writing skills. References Abdul-Bari M (2014) Fundamentals of Creative and Functional Writing. Dar Al-Fikr Al-Arabi Abanmi A (2018) Creative Thinking in Language Production and Interpretation. King Saud University Abu Hatab F, Othman S, Sadeq A (2008) Psychological assessment. Anglo Egyptian Library Abu Hatab F, Suleiman A (1973) Torrance test of creative thinking: Verbal and figural forms. Dar Al-Nahda Al-Arabia Al-Sayed FB (2006) Statistical psychology and human mind measurement. Dar Al-Fikr Al-Arabi Al-Sharif K (2020) Developing Writing Skills through Cognitive Strategies. Dar Safa for Publishing and Distribution Baron N (2024) Naomi Baron, linguist: 'With AI, the way of writing will be simpler and more homogeneous'. El País. Retrieved from https://elpais.com/proyecto-tendencias/2024-11-29/naomi-baron-linguista-con-la-ia-la-forma-de-escribir-sera-mas-simple-y-homogenea.html Bereiter C, Scardamalia M (1987) The Psychology of Written Composition. Routledge Brown TB, Mann B, Ryder N, Subbiah M, Kaplan JD, Dhariwal P, Amodei D (2020) Language models are few-shot learners. arXiv preprint arXiv:2005.14165. Retrieved from https://arxiv.org/abs/2005.14165 Educational Testing Service (ETS) (1995) Test of Written English (TWE) test guide: Sample topics, rating criteria, and scored essay samples. Educational Testing Service Feigenbaum EA, Buchanan BG (1971) DENDRAL and Meta-DENDRAL: Their applications dimension. Artif Intell 11(1–2):5–24 Ferrag AM, Debbah M, Al-Hawawreh M (2023) Generative AI for cyber threat-hunting in 6G-enabled IoT networks. arXiv e-prints. Retrieved from https://arxiv.org/abs/2306.10582 Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Bengio Y (2014) Generative adversarial nets. In Advances in neural information processing systems (pp. 2672–2680). Retrieved from https://arxiv.org/abs/1406.2661 Gupta M et al (2023) From ChatGPT to Threat GPT: Impact of generative AI in cybersecurity and privacy. IEEE Access 11:15689–15703. https://doi.org/10.1109/ACCESS.2023.3245678 Ippolito D, Yuan A, Coenen A, Burnam S (2022) Creative Writing with an AI-Powered Writing Assistant: Perspectives from Professional Writers. 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Retrieved from https://openai.com/research/language-unsupervised Radford A, Wu J, Child R, Luan D, Amodei D, Sutskever I (2019) Language models are unsupervised multitask learners. OpenAI. Retrieved from https://openai.com/research/language-models-are-unsupervised-multitask-learners Ramesh A, Pavlov M, Goh G, Gray S, Voss C, Radford A, Sutskever I (2021) Zero-shot text-to-image generation. In International Conference on Machine Learning (pp. 8821–8831). Retrieved from https://arxiv.org/abs/2102.12092 Rudnicka K (2025) Can Grammarly and ChatGPT accelerate language change? AI-powered technologies and their impact on the English language: wordiness vs. conciseness. arXiv preprint arXiv:2502.04324. Retrieved from https://arxiv.org/abs/2502.04324 Shortliffe EH (1976) MYCIN: Computer-based medical consultations. Elsevier. Retrieved from https://www.sciencedirect.com/science/article/pii/B9780125091003500089 Shanahan M, Clarke C (2023) Evaluating large language model creativity from a literary perspective. arXiv:2312.03746 . Retrieved from https://arxiv.org/abs/2312.03746 Turing AM (1950) Computing machinery and intelligence. Mind, 59(236), 433–460. Retrieved from https://academic.oup.com/mind/article/LIX/236/433/986238 Zinkevich NA, Ledeneva TV (2021) Using Grammarly to enhance students’ academic writing skills. Prof Discourse Communication 3(4):51–63. https://doi.org/10.24833/2687-0126-2021-3-4-51-63 New Educ (2023), April 10 Creative writing in English language subject through project-based learning in elementary stage. Retrieved from https://www.new-educ.com/creative-writing-english-language Additional Declarations No competing interests reported. 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Writing, as a fundamental skill, plays a vital role in communication, education, and professional discourse, adapting continuously to shifts in linguistic conventions and stylistic norms (Baron, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). With the advancement of technology, particularly in Artificial Intelligence (AI) and Natural Language Processing (NLP), new tools have emerged to support and refine writing practices (Ippolito et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). AI-powered applications such as Grammarly and ChatGPT offer grammar correction, stylistic enhancement, and text refinement, making them widely adopted for improving coherence and readability. Unlike traditional grammar checkers, these tools utilize machine learning algorithms to suggest rewording, simplify structures, and optimize text flow, thereby enhancing written communication.\u003c/p\u003e \u003cp\u003eDespite their benefits, AI-driven writing assistants raise concerns regarding their impact on cognitive engagement, creative expression, and independent writing proficiency. While these technologies facilitate clarity and conciseness, they may also encourage a standardized style that prioritizes efficiency over linguistic complexity (Rudnicka, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Grammarly, for instance, tends to favor direct phrasing, whereas ChatGPT generates text based on established linguistic patterns, potentially shaping users\u0026rsquo; stylistic choices. As reliance on such tools grows, it becomes imperative to examine whether they enhance writing skills or contribute to a decline in critical thinking and self-editing abilities (Ippolito et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis study explores the influence of AI-powered writing tools on contemporary writing practices, with a particular focus on their effects on sentence structure, stylistic tendencies, and the balance between conciseness and creative depth. By analyzing how these technologies modify written texts, the research aims to determine their role in shaping modern language use and writing development. Through this investigation, the study contributes to the broader discourse on AI\u0026rsquo;s impact on linguistic evolution and writing proficiency in the digital era..\u003c/p\u003e"},{"header":"Research Problem","content":"\u003cp\u003eThe integration of artificial intelligence into education has gained increasing attention due to its potential to enhance students’ creative writing skills. Creative writing is a multifaceted skill that requires originality and creativity, coherence and organization, language proficiency, critical and analytical thinking, as well as fluency and flexibility (Bereiter \u0026amp; Scardamalia, \u003cspan class=\"CitationRef\"\u003e1987\u003c/span\u003e). However, students learning English as a Foreign Language often struggle to develop these skills due to limited exposure to authentic linguistic input and the lack of immediate, constructive feedback (Graham, Hebert, \u0026amp; Harris, 2015).\u003c/p\u003e\n\u003cp\u003eThe AI-based deliberative model has emerged as an innovative tool designed to support students in improving their creative writing abilities by offering personalized, real-time feedback tailored to their individual needs. This model enables students to refine their writing, organize their ideas more effectively, and enhance their linguistic accuracy (Kumar \u0026amp; Rose, \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e). However, further empirical research is needed to evaluate the effectiveness of the AI-based deliberative model in secondary education settings, particularly in understanding the challenges students may face when using AI-assisted writing tools.\u003c/p\u003e\n\u003cp\u003eGiven this problem, this study seeks to address the following:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eHow effective is the AI-based pragmatic model in enhancing the creative writing skills of third-year high school students in Riyadh?\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eWhat are the differences in creative writing proficiency in English between students who use the AI-based deliberative model and their peers who do not?\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eWhat challenges do third-year secondary students face when applying the AI-based deliberative model in creative writing in English?\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n"},{"header":"Research Hypotheses","content":"\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eThe AI-based pragmatic model is effective in enhancing the creative writing skills of third-year high school students.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eThere is a statistically significant difference between students who use the pragmatic model and those who do not, in favor of the former group.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eStudents face certain specific challenges while using the pragmatic model; however, these challenges do not hinder the improvement of their creative writing.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Research Objectives","content":"\u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eTo measure the effectiveness of the pragmatic model in improving creative writing skills.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eTo compare the performance of students who use the pragmatic model with those who do not.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eTo identify the challenges students, encounter while applying the pragmatic model.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e"},{"header":"Significance of the Study","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eTheoretical Significance:\u003c/h2\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eEnriching the educational literature on the use of artificial intelligence in English language teaching.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eContributing to existing knowledge about pragmatic models and their impact on creative writing.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePractical Significance:\u003c/h3\u003e\n\u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003ePresenting an innovative teaching model that educators can utilize to enhance creative writing skills.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eAssisting students in developing their writing abilities using modern tools.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eGuiding educational policymakers toward integrating AI into the curriculum.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eResearch Methodology :\u003c/h2\u003e \u003cp\u003eThe quasi-experimental approach.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eResearch Instruments:\u003c/h3\u003e\n\u003cp\u003e1.A standardized test to measure the level of creative\u003c/p\u003e \u003cp\u003ewriting before and after implementing the model.\u003c/p\u003e \u003cp\u003e2.A questionnaire to assess the effectiveness of the pragmatic model and the challenges students face.\u003c/p\u003e"},{"header":"Study Delimitations","content":"\u003cp\u003e\u003cstrong\u003eSubject Delimitation\u003c/strong\u003e:\u0026nbsp;The study is confined to examining the effectiveness of the pragmatic model in improving creative writing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePopulation Delimitation:\u003c/strong\u003e Third-year high school students.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGeographical Delimitation:\u003c/strong\u003e High schools in Riyadh.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTime Delimitation:\u003c/strong\u003e The 2024–2025 academic year.\u003c/p\u003e"},{"header":"Research terms","content":"\u003cp\u003e\u003cstrong\u003e1.Generative Artificial Intelligence (Generative AI):\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConceptual Definition of Generative AI:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGenerative AI refers to a branch of artificial intelligence designed to autonomously generate new content, including text, images, music, and videos. It utilizes advanced deep learning models, such as Generative Adversarial Networks (GANs), Transformer-based architectures (e.g., GPT models), and Variational Autoencoders (VAEs). These models analyze large datasets, identify patterns, and produce human-like content with contextual accuracy and coherence (Ahmed, Okba, \u0026amp; Harous, 2024).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOperational Definition of Generative AI in This Study:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, Generative AI refers to AI-based writing tools such as Grammarly and ChatGPT, which assist students in enhancing their creative writing skills. The research evaluates how these tools influence originality, text coherence, and idea generation, examining their role in improving students\u0026rsquo; ability to create structured and imaginative compositions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2 Creative Writing\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConceptual Definition of Creative Writing:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCreative writing is the artistic process of developing narratives, poetry, and other literary works that emphasize originality, expressive depth, and structured storytelling. Unlike traditional academic writing, creative writing prioritizes imagination, character development, and emotional engagement, allowing writers to convey ideas and emotions in unique and thought-provoking ways (Shanahan \u0026amp; Clarke, 2023).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOperational Definition of Creative Writing in This Study:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, creative writing refers to students\u0026rsquo; ability to produce original literary compositions that demonstrate authenticity, coherence, and inventiveness. These writings are assessed based on originality, structural organization, depth of content, and the ability to develop novel ideas and articulate them effectively. The study examines how Grammarly and ChatGPT influence students\u0026rsquo; creative writing performance, focusing on their impact on originality and text coherence\u003c/p\u003e"},{"header":"Theoretical Framework and Previous Studies","content":"\u003cp\u003e\u003cstrong\u003eFirst Theme:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.1The Evolution of Generative Artificial Intelligence :\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e1.Early Foundations (1940s - 1950s).\u003c/p\u003e\n\u003cp\u003eThe concept of artificial intelligence (AI) began taking shape in the 1940s when Warren McCulloch and Walter Pitts proposed a mathematical model for artificial neurons, laying the groundwork for neural networks (McCulloch \u0026amp; Pitts, 1943). A few years later, Alan Turing published his seminal paper Computing Machinery and Intelligence, in which he introduced the Turing Test to assess a machine’s ability to exhibit intelligent behavior comparable to humans (Turing, 1950).\u003c/p\u003e\n\u003cp\u003e2.Formative Years and the Birth of AI (1956 - 1970s).\u003c/p\u003e\n\u003cp\u003eIn 1956, the Dartmouth Conference officially marked the birth of artificial intelligence as an academic field, led by John McCarthy, Marvin Minsky, Nathaniel Rochester, and Claude Shannon (McCarthy et al., 1955). During the 1960s, expert systems such as DENDRAL, designed for chemical analysis, and MYCIN, developed for medical diagnosis, demonstrated AI’s potential in specialized fields (Feigenbaum \u0026amp; Buchanan, 1971; Shortliffe, 1976).\u003c/p\u003e\n\u003cp\u003e3.Challenges and Renewed Interest (1970s - 1980s).\u003c/p\u003e\n\u003cp\u003eDespite early optimism, AI research faced significant setbacks in the 1970s due to unmet expectations and funding cuts, leading to what became known as the “AI winter” (McCarthy et al., 1955). However, the 1980s saw a resurgence in AI development, fueled by increased computational power and the continued success of expert systems in real-world applications (Feigenbaum \u0026amp; Buchanan, 1971).\u003c/p\u003e\n\u003cp\u003e4.The Emergence of Deep Learning (1990s - 2010s).\u003c/p\u003e\n\u003cp\u003eBy the 1990s, advances in machine learning algorithms and computing resources enabled more sophisticated AI models. However, it wasn’t until 2014 that Ian Goodfellow and his colleagues introduced Generative Adversarial Networks (GANs), a breakthrough approach to generating realistic data through adversarial training between two neural networks (Goodfellow et al., 2014). GANs played a crucial role in revolutionizing image synthesis, video generation, and other AI-generated content.\u003c/p\u003e\n\u003cp\u003e5.The Rise of Large Language Models (2018 - Present).\u003c/p\u003e\n\u003cp\u003eThe late 2010s marked the rise of large-scale generative AI models, beginning with OpenAI’s GPT-1 in 2018, which showcased the power of unsupervised pre-training for natural language processing (Radford et al., 2018). This was followed by GPT-2 in 2019, which demonstrated a remarkable ability to generate coherent and contextually relevant text (Radford et al., 2019).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; The introduction of GPT-3 in 2020 represented a significant leap, with its 175 billion parameters enabling it to perform complex language tasks without requiring task-specific training (Brown et al., 2020). More recently, text-to-image generative models such as DALL·E have expanded AI’s creative potential by allowing users to generate high-quality images from text descriptions (Ramesh et al., 2021).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.2 Previous Studies on Generative AI:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e1. Ferrag et al. (2023): This study explored the role of Generative AI in cybersecurity, particularly in detecting cyber threats within 6G-enabled IoT networks. The findings indicated that Generative AI models achieved 95% accuracy in identifying cyber threats, demonstrating their potential in automating cybersecurity processes and improving predictive threat detection.\u003c/p\u003e\n\u003cp\u003e2.Gupta et al. (2023): This research investigated the ethical concerns and security risks associated with Generative AI, highlighting issues such as privacy breaches, misinformation, and academic integrity violations. The study recommended the implementation of ethical guidelines and regulatory frameworks to ensure the responsible use of AI technologies in academic and professional settings.\u003c/p\u003e\n\u003cp\u003e3. Ahmed et al. (2024): This systematic review examined the applications of Generative AI in cybersecurity automation, focusing on its advantages and limitations. The study emphasized the need for balancing AI-driven automation with ethical considerations to prevent misuse and ensure transparency in AI-generated outputs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.3 AI-Powered Writing Tools: Grammarly and ChatGPT:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Role of Grammarly and ChatGPT in Writing Development\u003c/p\u003e\n\u003cp\u003eGrammarly and ChatGPT are among the most widely used AI-powered writing tools, offering users real-time assistance in refining their writing.\u003c/p\u003e\n\u003cp\u003e-Grammarly serves as a proofreading and grammar correction tool, helping writers enhance clarity, coherence, and grammatical accuracy.\u003c/p\u003e\n\u003cp\u003e-ChatGPT, on the other hand, provides text expansion, contextual suggestions, and automated feedback, allowing users to develop richer content and improve text fluency.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.4 Previous Studies on Grammarly and ChatGPT:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e1.Calma et al. (2022): This study examined Grammarly’s impact as an instructional tool in university-level writing. The results showed that Grammarly effectively reduced grammar and sentence structure errors; however, students who depended solely on Grammarly without instructor feedback showed limited improvement in critical thinking and argument development.\u003c/p\u003e\n\u003cp\u003e2.Tambunan et al. (2022): This longitudinal study investigated Grammarly’s influence on EFL (English as a Foreign Language) students’ writing skills. The findings revealed that while Grammarly significantly improved grammatical accuracy, excessive reliance hindered students’ ability to self-correct their writing without AI assistance.\u003c/p\u003e\n\u003cp\u003e3. Liu et al. (2024): This study analyzed the impact of ChatGPT on creativity in writing. The research found that while ChatGPT enhanced idea generation and text coherence, prolonged use led to repetitive patterns and reduced originality, affecting students’ ability to produce truly creative and unique content.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSecond Theme:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;2 Creative Writing:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.1 Importance Writing Skills:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e“\u0026nbsp;\u003c/strong\u003eCreative writing fosters imagination and allows students to express their thoughts and feelings in a unique way, enhancing their engagement and confidence.” (New Educ, 2023).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2Creative Writing Skills:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCreative thinking is a field that explores the cognitive processes involved in producing and interpreting language. Many educators, including Abanmi (2018), Abdul-Bari (2014), and Al-Sharif (2020), have identified several key skills associated with creative thinking. The researcher summarizes them as follows:\u003c/p\u003e\n\u003cp\u003e1. Originality and Creativity:\u003c/p\u003e\n\u003cp\u003eThe ability to generate new and unique ideas while expressing them in innovative and unconventional ways (Abanmi, 2018).\u003c/p\u003e\n\u003cp\u003e2. Organization and Coherence:\u003c/p\u003e\n\u003cp\u003eThe skill of structuring ideas and information in a logical and cohesive manner, ensuring clarity and ease of comprehension for the reader (Abdul-Bari, 2014)..\u003c/p\u003e\n\u003cp\u003e3. Language Use:\u003c/p\u003e\n\u003cp\u003e \u0026nbsp;Mastery of linguistic rules and the effective use of vocabulary and sentence structures to communicate ideas with precision and clarity, while incorporating rhetorical and stylistic elements (Al-Sharif, 2020).\u003c/p\u003e\n\u003cp\u003e4. Critical and Analytical Thinking:\u003c/p\u003e\n\u003cp\u003e \u0026nbsp;The ability to objectively analyze and evaluate information and ideas,leading to logical conclusions supported by evidence (Abanmi, 2018).\u003c/p\u003e\n\u003cp\u003e5.Fluency and Flexibility:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e Fluency refers to the capacity to produce a large volume of ideas or written content within a short period, whereas flexibility involves adapting and varying these ideas by changing approaches or writing styles as needed (Abdul-Bari, 2014)..\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 Previous Studies on Creative Writing:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e1.Gomez-Rodriguez \u0026amp; Williams (2023): This study compared human-written creative texts with AI-generated narratives, finding that while AI-generated texts maintained structural coherence, they lacked emotional depth and originality.\u003c/p\u003e\n\u003cp\u003e2. Shanahan \u0026amp; Clarke (2023): This research assessed the role of AI in creative storytelling, concluding that AI-assisted writing improved organization and fluency but struggled to convey deeper themes and emotional complexity.\u003c/p\u003e\n\u003cp\u003e3. Chakrabarty et al. (2023): This study analyzed AI’s ability to generate original narratives, revealing that while AI models excel at fluency and coherence, they lack the personal nuances and creativity inherent in human writing.\u003c/p\u003e"},{"header":"Research Procedures","content":"\u003cp\u003eStudy Population:\u003cbr\u003eThe study population consisted of 29,435 senior high school students in Riyadh, as reported by the Department of Education in the Riyadh Educational Administration for the academic year 1446 AH.\u003c/p\u003e\n\u003cp\u003eStudy Sample:\u003cbr\u003e\u0026nbsp;Participants in the current study were divided into two phases:\u003c/p\u003e\n\u003cp\u003e1.Phase One:\u003cbr\u003eThis phase involved a pilot sample, referring to the participants on whom the researcher initially administered the study instruments to evaluate their psychometric properties. This sample comprised 22 senior high school students from Riyadh, recruited from Prince Saud bin Abdulaziz High School, with ages ranging between 18 and 19 years. The researcher relied on this group to determine the psychometric characteristics of the Creative Writing Skills Test.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePhase Two:\u003c/strong\u003e\u003cbr\u003eThis phase represents the main sample, consisting of 90 senior high school students from Riyadh, recruited from Abdulaziz Al-Kho waiter High School, and divided into 45 students representing the [group].\u003c/p\u003e\n\u003cp\u003eThe experimental group consisted of 45 senior high school students aged between 18 and 19 years, while the control group comprised 45 senior high school students within the same age range.\u003c/p\u003e"},{"header":"Research Instruments","content":"\u003cdiv id=\"Sec30\" class=\"Section2\"\u003e\n \u003ch2\u003eFirst: Creative Writing Skills Test (Developed by the Researcher)\u003c/h2\u003e\n \u003cp\u003eThe current test aims to assess creative writing skills, specifically originality and creativity, organization and coherence, language usage, critical and analytical thinking, as well as fluency and flexibility among senior high school students.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec31\" class=\"Section2\"\u003e\n \u003ch2\u003eSteps in Test Development:\u003c/h2\u003e\n \u003cp\u003eThe test was constructed through several steps until its final version was reached, as follows:\u003c/p\u003e\n \u003cp\u003eA. Reviewing definitions of creative writing skills as presented in theoretical frameworks, as well as previous studies and research that highlighted the importance of these skills.\u003c/p\u003e\n \u003cp\u003eB. Examining various Arabic and foreign tests designed to measure creative writing skills, such as the Torrance Tests of Creative Thinking (TTCT) and the Test of Written English (TWE).\u003c/p\u003e\n \u003cp\u003eC. Operationally defining the concept of creative writing skills and formulating questions for each skill based on a review of the theoretical frameworks and previous studies concerning the indicators of creative writing skills.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eDuring the formulation and construction of the test questions, the researcher adhered to the following guidelines\u003c/strong\u003e:\u003c/p\u003e\n \u003col\u003e\n \u003cli\u003e\n \u003cp\u003eThe questions should be appropriate for the level of the sample students (senior high school students in Riyadh).\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eThey should be clear, simple, concise, and not compound, representing a single idea.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eThey must be directly related to the subject of measurement and the specific skill being assessed.\u003c/p\u003e\n \u003c/li\u003e\n \u003c/ol\u003e\n \u003cdiv id=\"Sec32\" class=\"Section3\"\u003e\n \u003ch2\u003eAnswer Key for the Test Questions:\u003c/h2\u003e\n \u003cp\u003eThe researcher developed an answer key for the Creative Writing Skills Test. This key was meticulously designed to ensure objectivity and consistency in evaluating the participants\u0026apos; responses. It is based on clear criteria that include the quality of the idea, content coherence, language style, textual cohesion, as well as grammatical and spelling accuracy. Each criterion is assigned a specific score according to a rating scale that reflects different levels of performance. The evaluation is conducted manually following these standards to ensure an accurate assessment and measurement of the actual improvement in students\u0026apos; creative writing skills.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec33\" class=\"Section3\"\u003e\n \u003ch2\u003ePsychometric Properties of the Creative Writing Skills Test:\u003c/h2\u003e\n \u003cdiv id=\"Sec34\" class=\"Section4\"\u003e\n \u003ch2\u003eFirst: Validity:\u003c/h2\u003e\n \u003cp\u003eThe researcher evaluated the test\u0026rsquo;s validity based on three types of validity, as follows:\u003c/p\u003e\n \u003cp\u003eA. Expert Validity:\u003c/p\u003e\n \u003cp\u003eThe test, in its initial version comprising four questions, was presented to a group of five expert educators specializing in curriculum and teaching methods. They were requested to provide feedback on the test\u0026apos;s validity regarding the clarity of its instructions, the accuracy of the wording of the test items, the appropriateness of each question for the skill it intended to measure, the test\u0026rsquo;s overall representation of the target construct, and the suitability of the questions for senior high school students in Riyadh. Additionally, they were allowed to suggest other appropriate modifications. The experts\u0026apos; agreement rates on the evaluation criteria ranged between 80.0% and 100%, with an average agreement rate of 90.0%. This high level of consensus supports the validity of the test for measuring the intended constructs. Consequently, the linguistic phrasing of some questions was adjusted based on the experts\u0026rsquo; recommendations, and the experts\u0026rsquo; agreement rate was considered an indicator of the test\u0026apos;s validity, thereby instilling confidence in the results obtained from its administration to the sample.\u003c/p\u003e\n \u003cp\u003eB. Criterion-Related Validity:\u003c/p\u003e\n \u003cp\u003eThe researcher also assessed the test\u0026rsquo;s validity using criterion-related validity by employing the Mann\u0026ndash;Whitney U test. This analysis aimed to determine the significance of the differences between high- and low-performing students in both specific skills and the overall score on the Creative Writing Skills Test among the pilot sample of senior high school students in Riyadh. The following table illustrates these findings:\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;(1): Results of the Mann-Whitney Test Indicating Significant Differences Between the Mean Rank Scores of the High and Low Groups on the Creative Writing Skills Test\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Taba\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGroup\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean Rank\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSum of Ranks\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eU\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eZ Value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eOriginality and Creativity\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e9.5\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/th\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/th\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e-3.083\u003c/p\u003e\n \u003c/th\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e3.5\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eOrganization and Coherence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHigh\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e9.42\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e56.5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e-2.827\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.005\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLow\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.58\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e21.5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLanguage Usage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHigh\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e9.33\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e56\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e-2.766\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.006\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLow\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.67\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e22\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCritical and Analytical Thinking\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHigh\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e9.5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e57\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e-2.934\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.003\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLow\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e21\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFluency and Flexibility\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHigh\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e9.5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e57\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e-2.918\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.004\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLow\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e21\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverall Test Score\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHigh\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e9.5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e57\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e-2.892\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.004\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLow\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e21\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eIt is evident from the previous table that the Z-values are statistically significant at a level below 0.05 for all test domains and the overall score, with values of -3.083, -2.827, -2.766, -2.934, -2.918, and \u0026minus;\u0026thinsp;2.892, respectively. This indicates that statistically significant differences exist between the low and high groups in all the test skills and the overall score in favor of the high group. Such findings demonstrate that the Creative Writing Skills Test possesses discriminative power in distinguishing between low and high performers, thereby instilling confidence in the test\u0026rsquo;s validity.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003ch3\u003eC. Internal Consistency Validity of the Test\u003c/h3\u003e\n\u003cp\u003eAfter the expert review, the researcher administered the test in the field using data from the pilot sample of 22 senior high school students. The internal consistency validity was determined by calculating the square root of the reliability coefficient (Al-Sayed, \u003cspan class=\"CitationRef\"\u003e2006\u003c/span\u003e, p. 402). The degree of internal consistency is presented in the following table:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;(2) \u0026ndash; Internal Consistency Validity Results of the Creative Writing Skills Test (N\u0026thinsp;=\u0026thinsp;22) Senior High School Students\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003cimg 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\"\u003e\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003eIt is evident from Table\u0026nbsp;(2) that the square root values of the reliability coefficients ranged from 0.874 to 0.947, approaching one. This confirms the validity of the Creative Writing Skills Test.\u003c/p\u003e\n\u003cdiv id=\"Sec36\" class=\"Section2\"\u003e\n \u003ch2\u003eSecondly: Calculation of Internal Consistency\u003c/h2\u003e\n \u003cp\u003eThis was carried out by computing the correlation coefficient between the score of each skill and the overall test score using the pilot sample of 22 senior high school students in Riyadh. The following table (Table\u0026nbsp;(2)) presents the correlation coefficients between each skill score and the overall test score.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;(3) \u0026ndash; Correlation Coefficients between Each Skill Score and the Overall Test Score (N\u0026thinsp;=\u0026thinsp;22) Senior High School Students\u003c/strong\u003e\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u003cimg 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\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003e** : Significant at the 0.01 level *: Significant at the 0.05 level\u003c/p\u003e\n \u003cp\u003eThe table (3) shows that the correlation coefficients between creative writing skills and the total test score range between (0.669\u0026ndash;0.846), all of which are statistically acceptable values. This confirms the internal consistency of the test.\u003c/p\u003e\n \u003cp\u003eThird: Reliability\u003c/p\u003e\n \u003cp\u003eThe researcher calculated the reliability of the Creative Writing Skills Test using the Kuder-Richardson method, as it is suitable for tests with dichotomous scoring (i.e., where answers are scored as either 0 or 1) (Abu Hatab, Othman, \u0026amp; Sadiq, 2008, p. 152). The following table () presents the reliability coefficients for each skill in the test and the total score:\u003c/p\u003e\n \u003cp\u003eIt is evident from Table () that the correlation coefficients between creative writing skills and the total score range between (0.669\u0026ndash;0.846), all of which are statistically significant values, indicating the internal consistency of the test.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;(4): Reliability Coefficients for the Domains and Total Score of the Creative Writing Skills Test\u003c/strong\u003e\u003c/p\u003e\n \u003cdiv id=\"Sec37\" class=\"Section3\"\u003e\n \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;22) Third-Year Secondary Students\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tabk\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSkills and Overall Test Score\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eReliability Coefficient\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOriginality and Creativity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.876\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOrganization and Coherence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.764\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLanguage Use/Usage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.891\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCritical and Analytical Thinking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.787\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFluency and Flexibility\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.799\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eOverall Test Score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.898\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eIt is evident from Table\u0026nbsp;(4) that the overall reliability coefficient for creative writing skills is high, reaching (0.898) for the total test items. The reliability of the domain\u0026apos;s ranges between (0.764) as a minimum and (0.891) as a maximum, indicating that the test has a high degree of reliability and can be trusted for field application in the research.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec38\" class=\"Section2\"\u003e\n \u003ch2\u003eDescription of the Final Version of the Test:\u003c/h2\u003e\n \u003cdiv id=\"Sec39\" class=\"Section3\"\u003e\n \u003ch2\u003eThe Post-Test Objective:\u003c/h2\u003e\n \u003cp\u003eThe post-test aims to measure the effectiveness of the dialogic model based on artificial intelligence in enhancing creative writing skills among third-year secondary students. The test includes a set of essay questions designed to assess elements of creative writing, such as idea development, organization, style, and linguistic coherence.\u003c/p\u003e\n \u003cp\u003eAnswers will be manually graded according to strict criteria to ensure objectivity, with a specified time limit for responses. Participants\u0026apos; answers will be kept strictly confidential and used exclusively for scientific research purposes.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003ch3\u003eDetermining the Appropriate Time for the Creative Writing Skills Test:\u003c/h3\u003e\n\u003cp\u003eThe researcher determined the suitable duration for answering the Creative Writing Skills Test by calculating the time taken by each student in the pilot sample. The average time required to complete the test was then computed. The researcher concluded that the appropriate time to answer the test questions is 35 minutes, with an additional 5 minutes for instructions, making the total test duration 40 minutes, approximately equivalent to a class period.\u003c/p\u003e\n\u003ch3\u003eSecond: Questionnaire for Verifying the Effectiveness of the Experimental Treatment (Prepared by the Researcher)\u003c/h3\u003e\n\u003cp\u003eThe researcher developed a questionnaire to verify the effectiveness of the experimental treatment. The purpose of this questionnaire is to gather students\u0026rsquo; opinions regarding the suitability of the program for them, the benefits gained, and to ensure that the implementation process of the dialogic model based on artificial intelligence achieves its intended objectives.\u003c/p\u003e\n\u003ch3\u003eDescription of the Questionnaire:\u003c/h3\u003e\n\u003cp\u003eThe initial version of the questionnaire consists of (20) statements aimed at understanding students\u0026apos; opinions on the training procedures included in the dialogic model based on artificial intelligence for developing creative writing skills, the suitability of various activities and stimuli for students, the extent to which trainees have benefited from the dialogic model based on artificial intelligence, and the degree of improvement in students\u0026apos; skills at the end of the implementation of the dialogic model based on artificial intelligence.\u003c/p\u003e\n\u003cp\u003eThe researcher ensured that the questionnaire items were formulated in a way that made the vocabulary appropriate for the sample population, while also being clear, simple, and concise. Additionally, the items were designed to align with the procedures of the dialogic model based on artificial intelligence.\u003c/p\u003e\n\u003ch3\u003eTime Required for Completing the Questionnaire:\u003c/h3\u003e\n\u003cp\u003eThere is no specific time limit for completing this questionnaire. Responses are recorded by placing a checkmark (✔) under one of the following options: Strongly Agree, Agree, Neutral, Disagree, Strongly Disagree.\u003c/p\u003e\n\u003ch3\u003eValidity of the Questionnaire:\u003c/h3\u003e\n\u003cp\u003eThe researcher verified the validity of the questionnaire using expert judgment validity. The initial version of the questionnaire, consisting of 20 statements, was reviewed by a panel of five experts specializing in curricula and teaching methods. The agreement percentages among the experts on the evaluation criteria ranged from 80\u0026ndash;100%, which are considered high and acceptable. This indicates confidence in the reliability of the results that can be obtained upon implementing the questionnaire.\u003c/p\u003e\n\u003ch3\u003eControlling Extraneous Variables:\u003c/h3\u003e\n\u003cp\u003eThe researcher controlled extraneous variables that could potentially influence the dependent variable. The independent variable in this study is the dialogic model based on artificial intelligence, while the dependent variable is creative writing skills. Below is a list of key extraneous variables identified through theoretical frameworks and previous studies, which may impact the dependent variable: chronological age, intelligence, socio-economic level, gender, and pre-test measurement of creative writing skills.\u003c/p\u003e\n\u003ch3\u003eA. Chronological Age:\u003c/h3\u003e\n\u003cp\u003eThe chronological age of participants in the current study ranges between 18 and 19 years. To control for this variable, participants above 19 years old were excluded. To ensure equivalence in age distribution between the experimental and control groups, the researcher tested for significant differences in participants\u0026apos; ages using the T-Test. The following table presents the results.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;(5): T-Value and Its Statistical Significance for the Differences Between the Experimental and Control Groups in the Chronological Age Variable\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tabl\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGroup\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStandard Deviation\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003et-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSignificance Level\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eExperimental\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.539\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e-1.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.128\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.604\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNot significant\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe previous table demonstrates that the difference between the experimental and control groups in the chronological age variable is not statistically significant. The mean age for the experimental group was (18.25) years with a standard deviation of (0.539), while the mean age for the control group was (18.43) years with a standard deviation of (0.604). The T-value was (1.680), which is not statistically significant, indicating that both groups are equivalent in terms of chronological age.\u003c/p\u003e\n\u003ch3\u003eB. Gender\u003c/h3\u003e\n\u003cp\u003eThe research sample consisted only of male students to eliminate the potential effect of gender on the dependent variable. Additionally, this selection facilitated easier interaction and training in creative writing skills through the dialogic model based on artificial intelligence.\u003c/p\u003e\n\u003ch3\u003eC. Intelligence\u003c/h3\u003e\n\u003cp\u003eThe researcher considered intelligence as a potential extraneous variable that could influence creative writing skills. Therefore, intelligence levels were controlled in both the experimental and control groups by administering the Verbal Intelligence Test (prepared by Jaber \u0026amp; Omar, 2007). The researcher then analyzed the significance of differences between the two groups using the T-Test, and the following table presents the results.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;(6): T-Value and Its Statistical Significance for the Differences Between the Experimental and Control Groups in the Intelligence Variable\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tabm\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGroup\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStandard Deviation\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003et-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSignificance Level\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eExperimental\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71.343\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.165\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.723-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.714\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e72.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.544\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNot significant\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe previous table demonstrates that the difference between the experimental and control groups in the intelligence variable is not statistically significant. The mean intelligence score for the experimental group was (71.343) with a standard deviation of (6.165), while the mean intelligence score for the control group was (72.031) with a standard deviation of (7.544). This indicates that both groups are equivalent in terms of intelligence.\u003c/p\u003e\n\u003ch3\u003eD. Socio-Economic Level:\u003c/h3\u003e\n\u003cp\u003eThe sample was selected from a single school, Abdulaziz Al-Khuwaiter Secondary School in Riyadh. This ensured that both the experimental and control groups came from the same geographical area, making their socio-economic level as similar as possible. The researcher found that there was a reasonable level of homogeneity in the economic and social status of the participants.\u003c/p\u003e\n\u003ch3\u003eE. Pre-Test Measurement of Creative Writing Skills:\u003c/h3\u003e\n\u003cp\u003eTo ensure equivalence between the experimental and control groups, the researcher administered the Creative Writing Skills Test as a pre-test. The T-value was calculated to determine whether there were significant differences between the mean scores of the two groups in creative writing skills before the experimental treatment. The following table presents the results.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;(7): Means, Standard Deviations, T-Value, and Significance Level for the Experimental and Control Groups in the Pre-Test Measurement of Creative Writing Skills\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003ctable id=\"Tabn\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSkills and Total Score\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGroup\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStandard Deviation\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003et-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSignificance Level\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eOriginality and Creativity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eExperimental\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.2667\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.78885\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e-0.653\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.515\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.5333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.07364\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eSkills in Presenting a Claim or Idea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eExperimental\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.2667\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.14688\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.416\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.679\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.0889\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.90481\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eLanguage Use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eExperimental\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.7556\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.97893\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.443\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.659\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.5778\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.82768\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSkills and Total Score\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGroup\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eStandard Deviation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003et-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSignificance Level\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eCritical and Analytical Thinking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eExperimental\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.1333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.13839\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e-0.379\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.705\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.3111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.30437\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eFluency and Flexibility\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eExperimental\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.6222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.55635\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.433\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.666\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.4889\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.35885\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eTotal Test Score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eExperimental\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.0444\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.17304\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.974\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.78792\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eFindings on Pre-Test Equivalence in Creative Writing Skills\u003c/p\u003e\n\u003cp\u003eThe previous table indicates that the T-value was not statistically significant between the experimental and control groups in the overall score of creative writing skills as well as in each individual skill. This confirms that both groups were equivalent in the pre-test measurement of creative writing skills.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResearch Steps and Procedures\u003c/strong\u003e:\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003e\n \u003cp\u003eReviewing previous studies related to the research topic.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eExamining prior research that focused on the dialogic model based on artificial intelligence and its role in developing creative writing skills among third-year secondary students.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eDesigning and constructing the Creative Writing Skills Test, followed by validation by a panel of experts.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eDesigning the dialogic model based on artificial intelligence, and presenting it to a group of experts for evaluation.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eSelecting the research sample and assigning participants to experimental and control groups.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eTeaching the dialogic model based on artificial intelligence to the experimental group, while the control group follows the traditional method.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eAdministering the post-test to both experimental and control groups.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eConducting statistical analysis of the collected data.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eDeriving the research findings.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eInterpreting and discussing the results in light of the research questions and objectives.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eProviding a set of recommendations and suggestions based on the research findings.\u003c/p\u003e\n \u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eStatistical Methods:\u003c/p\u003e\n\u003cp\u003eTo verify the validity of the study\u0026apos;s hypotheses, the researcher employed the following statistical methods:\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003e\n \u003cp\u003ePearson Correlation Coefficient \u0026ndash; Used to determine the correlation between each test question and the total score of the Creative Writing Skills Test.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eMann-Whitney Test \u0026ndash; Used to examine differences between high and low scorers on the Creative Writing Skills Test (validity through extreme group comparison).\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eFrequencies and Percentages \u0026ndash; Used to analyze the responses of the experimental group on the questionnaire verifying the effectiveness of the experimental treatment.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eIndependent Samples T-Test \u0026ndash; Used to test the statistical significance of the differences between the mean post-test scores of the experimental and control groups in creative writing skills.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003ePaired Samples T-Test \u0026ndash; Used to assess the statistical significance of the differences between the pre-test and post-test mean scores of the experimental group in creative writing skills.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eEta-Squared (\u0026eta;\u0026sup2;) \u0026ndash; Used to measure the effect size of the experimental treatment (the dialogic model based on artificial intelligence) on the dependent variable (creative writing skills) among third-year secondary students in Riyadh.\u003c/p\u003e\n \u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003eResearch Findings\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFindings Related to the First Research Question\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003eThe first research question states:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026quot;What is the effectiveness of the experimental treatment (the dialogic model based on artificial intelligence) based on the results of the questionnaire verifying the effectiveness of the experimental treatment?\u0026quot;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo answer this question, the researcher calculated the percentages of responses from the experimental group for each statement in the questionnaire.\u003c/p\u003e\n\u003cp\u003eThe following table presents the percentage distributions of the experimental group\u0026rsquo;s responses to each statement in the questionnaire for verifying the effectiveness of the experimental treatment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;(8): Percentage Distributions of the Experimental Group\u0026rsquo;s Responses to Each Statement in the Questionnaire for Verifying the Effectiveness of the Experimental Treatment\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tabo\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eStatement No.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eStrongly Agree\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eAgree\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eNeutral\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eDisagree\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eStrongly Disagree\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNo 45\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNo 45\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNo 45\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNo 45\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNo 30\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71.11%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.67%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.44%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.22%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e88.89%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.89%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e73.33%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.22%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.44%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64.44%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.44%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.67%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.44%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66.67%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.67%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.67%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75.56%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.33%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.89%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.22%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e93.33%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.22%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.22%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.22%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71.11%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.44%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.44%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75.56%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.78%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.44%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.22%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66.67%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.89%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.44%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e77.78%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.55%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.67%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e73.33%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.55%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.89%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.22%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e68.89%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.22%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.44%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.44%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e91.11%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.89%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e84.44%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.89%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.22%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.44%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71.11%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.78%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.11%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e77.78%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.22%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95.56%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.44%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e91.11%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.44%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.22%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.22%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64.44%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.33%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.44%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.44%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe previous table shows that the percentage distribution of responses from the experimental group to the questionnaire items was as follows: those who responded with \u0026quot;Strongly Agree\u0026quot; ranged between (64.44% \u0026minus;\u0026thinsp;95.56%), those who responded with \u0026quot;Agree\u0026quot; ranged between (2.22% \u0026minus;\u0026thinsp;28.89%), those who responded with \u0026quot;Neutral\u0026quot; ranged between (2.22% \u0026minus;\u0026thinsp;11.11%), those who responded with \u0026quot;Disagree\u0026quot; ranged between (2.22% \u0026minus;\u0026thinsp;4.44%), and those who responded with \u0026quot;Strongly Disagree\u0026quot; ranged between (2.22% \u0026minus;\u0026thinsp;4.44%). This indicates that the implementation of the dialogic model successfully achieved its objectives, was suitable for students, and benefited them. It also highlights the variety of stimuli included in the model and the appropriateness of the activities and tasks it incorporated for third-year secondary students.\u003c/p\u003e\n\u003cp\u003eRegarding the findings related to the \u003cstrong\u003esecond research question\u003c/strong\u003e, which states: \u003cstrong\u003e\u0026quot;What is the effectiveness of the dialogic model based on artificial intelligence in developing creative writing skills among third-year secondary students in Riyadh?\u0026quot;\u003c/strong\u003e the researcher formulated the following two hypotheses:\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e-\u0026quot;There are no statistically significant differences at the significance level (\u0026alpha;\u0026thinsp;\u0026le;\u0026thinsp;0.05) between the mean scores of the experimental group students and the control group students in the post-test of creative writing skills.\u0026quot;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e-\u0026quot;There are no statistically significant differences at the significance level (\u0026alpha;\u0026thinsp;\u0026le;\u0026thinsp;0.05) between the mean scores of the pre-test and post-test for the experimental group in the creative writing skills test.\u0026quot;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo test the validity of the first hypothesis related to the previous research question, the researcher calculated the T-value for independent samples to examine the differences between the mean scores of students in the experimental and control groups in the post-test of creative writing skills. The results are presented in the following table:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;(9): Means, Standard Deviations, T-Value, and Significance Level for the Experimental and Control Groups in the Post-Test of Creative Writing Skills and Total Score\u003c/strong\u003e:\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tabp\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNo.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCreative Writing Skills\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGroup\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStandard Deviation\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003et-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSignificance Level\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eOriginality and Creativity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eExperimental\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.244\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.227\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e4.367\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.888\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.681\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eOrganization and Coherence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eExperimental\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.933\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.483\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e3.945\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.711\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.455\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eLanguage Use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eExperimental\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.499\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e4.208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.733\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.404\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eCritical and Analytical Thinking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eExperimental\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.955\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.757\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e3.573\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.272\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eFluency and Flexibility\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eExperimental\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.688\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.916\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e3.355\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.533\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.289\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal Test Score\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eExperimental\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.844\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e4.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.666\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.584\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe previous table shows that the T-values for the creative writing skills (Authenticity and Creativity, Organization and Coherence, Language Usage, Critical and Analytical Thinking, Fluency and Flexibility), as well as the total score of the Creative Writing Skills Test, were 4.367, 3.945, 4.208, 3.573, 3.355, and 4.810, respectively. These values are statistically significant at a level lower than (0.01), indicating the presence of statistically significant differences between the experimental and control groups in post-test creative writing skills in favor of the experimental group.\u003c/p\u003e\n\u003cp\u003eBy examining the mean scores for the five skills and the total creative writing skills score, it was evident that they were higher in favor of the experimental group. As a result, the alternative hypothesis is accepted, and the null hypothesis is rejected.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEffect Size of the Experimental Treatment (Dialogic Model Based on Artificial Intelligence) on the Dependent Variable (Creative Writing Skills)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo further evaluate the impact of the dialogic model, the effect size of the experimental treatment on creative writing skills was calculated using Eta-squared (\u0026eta;\u0026sup2;). The following section presents the findings related to the magnitude of the effect.\u003c/p\u003e\n\u003cp\u003eThe researcher used Eta-squared (\u0026eta;\u0026sup2;) to measure the effect size of the experimental treatment (the dialogic model based on artificial intelligence) on the dependent variable (creative writing skills). The researcher calculated the Eta-squared (\u0026eta;\u0026sup2;) value based on the T-value. According to Murad (\u003cspan class=\"CitationRef\"\u003e2000\u003c/span\u003e: 246), an effect size that explains approximately 0.02 of the total variance indicates a small effect, while an effect that explains 0.06 of the total variance indicates a moderate effect, whereas an effect that explains approximately 0.15 or more indicates a large effect (Murad, \u003cspan class=\"CitationRef\"\u003e2000\u003c/span\u003e, p. \u003cstrong\u003e246\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;(10) Eta-Squared (\u0026eta;\u0026sup2;) Value and the Effect Size of the Dialogic Model Based on Artificial Intelligence in Developing Creative Writing Skills.\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tabq\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNo.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCreative Writing Skills\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003et-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDegrees of Freedom\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026eta;\u0026sup2;\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEffect Size\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOriginality and Creativity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLarge\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOrganization and Coherence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLarge\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLanguage Use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.167\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLarge\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCritical and Analytical Thinking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedium\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFluency and Flexibility\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedium\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal Score of Creative Writing Skills\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLarge\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe table (10) illustrates that the Eta-squared (\u0026eta;\u0026sup2;) value for:\u003c/p\u003e\n\u003cp\u003e- The effect size of the dialogic model based on artificial intelligence on creative writing skills reached (0.178), (0.150), (0.167), and (0.208) for three skills which are authenticity and creativity, organization and coherence, and language usage, along with the total score, indicating a large effect size. For the remaining two skills, which are critical and analytical thinking, and fluency and flexibility, the Eta-squared (\u0026eta;\u0026sup2;) values were (0.127) and (0.113), indicating a moderate effect size.\u003c/p\u003e\n\u003cp\u003eTo test the validity of the second hypothesis related to the previous research question, which states: \u0026quot;There are no statistically significant differences at the significance level (\u0026alpha;\u0026thinsp;\u0026le;\u0026thinsp;0.05) between the mean scores of the pre-test and post-test for the experimental group in the creative writing skills test,\u0026quot; the paired samples T-test was used to examine the differences between the mean scores of the pre-test and post-test for the experimental group in the creative writing skills test. The results are presented in the following table:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;(11): Means, Standard Deviations, T-Value, and Statistical Significance of the Differences Between the Pre-Test and Post-Test Mean Scores for the Experimental Group in Creative Writing Skills Among Third-Year Secondary Students\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tabr\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMeasurement\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStandard Deviation\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean Differences\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStandard Deviation of Differences\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003et-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSignificance Level\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eOriginality and Creativity\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePre-test\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e4.266\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e1.788\u003c/p\u003e\n \u003c/th\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e-2.97778\u003c/p\u003e\n \u003c/th\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e1.40598\u003c/p\u003e\n \u003c/th\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e-14.208\u003c/p\u003e\n \u003c/th\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePost-test\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e7.244\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e1.227\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eOrganization and Coherence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePre-test\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e45\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e4.266\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.146\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-2.66667\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.4771\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-12.111\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePost-test\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e45\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e6.933\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.483\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLanguage Use\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePre-test\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e45\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.755\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.978\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-3.26667\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.85129\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-11.837\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePost-test\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e45\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e7.022\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.499\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCritical and Analytical Thinking\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePre-test\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e45\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e4.133\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.138\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-2.82222\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.35326\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-13.99\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePost-test\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e45\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e6.955\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.757\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFluency and Flexibility\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePre-test\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e45\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.622\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.556\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-3.06667\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.38826\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-14.818\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePost-test\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e45\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e6.688\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.916\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal Score of the Creative Writing Skills Scale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePre-test\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e45\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e20.044\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e7.173\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-14.8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.8055\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-26.089\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePost-test\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e45\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e34.844\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e6.56\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe table (11) shows that the T-values for creative writing skills and the total score of the Creative Writing Skills Test among third-year secondary students were (-14.208), (-12.111), (-11.837), (-13.990), (-14.818), and (-26.089), respectively. These values are statistically significant at a level lower than (0.01), indicating the presence of statistically significant differences between the pre-test and post-test scores for the experimental group in both individual skills and the total score of the Creative Writing Skills Test.\u003c/p\u003e\n\u003cp\u003eBy examining the mean scores in the individual skills and the total creative writing skills score, it is evident that the difference favors the post-test scores, meaning that the current hypothesis was not confirmed. As a result, the null hypothesis is rejected, and the alternative hypothesis is accepted. This confirms the effectiveness of the dialogic model based on artificial intelligence in developing creative writing skills among third-year secondary students.\u003c/p\u003e\n\u003cp\u003eFindings Related to the Third Research Question\u003c/p\u003e\n\u003ch3\u003eThe third research question states:\u003c/h3\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026quot;What are the challenges that third-year secondary students may face while applying the dialogic model based on artificial intelligence in creative writing in English?\u0026quot;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo answer this question, the researcher calculated the frequencies and percentages of the responses from the experimental group participants. The results are presented in the following table:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;(12): Challenges Faced by Third-Year Secondary Students While Applying the Dialogic Model\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(N\u0026thinsp;=\u0026thinsp;45 students)\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tabs\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePercentage (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFrequency\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable Categories\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.70%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDifficulty in understanding AI outputs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" align=\"left\"\u003e\n \u003cp\u003eChallenges that high school seniors may face while applying the transactional model\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.20%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIncompatibility of feedback with my personal writing style\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.10%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTime required to interact with the model\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTechnical issues in using AI tools\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e100%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e45\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe table (12) shows that 26.7% of the sample faced difficulty in understanding AI-generated outputs, 22.2% experienced issues with the mismatch between AI feedback and their personal writing style, 11.1% found the time required to interact with the model challenging, and 40.0% encountered technical difficulties while using AI tools. This indicates that the majority of the participants faced technical issues when using AI-based tools.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical Considerations and Data Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study received ethical approval from the Permanent Committee for Research Ethics at King Saud University (Ref No: KSU-HE-24-1133), granted on December 17, 2024. In addition, approval was obtained from the General Administration of Education in Riyadh, Ministry of Education, Saudi Arabia (Letter No. 4300777452), dated 17/11/1445H. The official approval authorized the implementation of the study among third-year high school students, including the use of both a questionnaire and a writing test.\u003c/p\u003e\n\u003cp\u003eWritten informed consent from the students’ parents or legal guardians was obtained on 18/11/1445H, prior to the administration of any research instruments. All research procedures adhered strictly to ethical guidelines issued by the relevant institutional and national authorities.\u003c/p\u003e\n\u003cp\u003eThe entire research process was conducted in accordance with the ethical standards of the Declaration of Helsinki (1964) and its subsequent amendments.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed Consent\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePrior to participation, informed consent was obtained from all students and their guardians. Participants were assured that their responses would remain confidential and used solely for academic research purposes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analyzed during the current study are not publicly available due to participant confidentiality agreements but are available from the corresponding author upon reasonable request\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResearch Summary\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study aimed to investigate the effectiveness of the dialogic model based on artificial intelligence in enhancing creative writing skills among third-year secondary students in Riyadh. Given the importance of creative writing in developing linguistic and cognitive skills, a quasi-experimental design was applied to two groups: an experimental group, which received training using the dialogic model, and a control group, which followed traditional methods.\u003c/p\u003e\n\u003cp\u003eThe findings revealed that the experimental group outperformed the control group in developing authenticity and creativity, organization and coherence, fluency, critical thinking, and language usage, confirming the model’s effectiveness in improving creative writing skills. Additionally, the study identified technical and procedural challenges encountered by students during implementation, emphasizing the need for continuous technical support and adaptation of AI tools to meet learners’ needs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRecommendations:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e1.Enhancing the application of AI in developing creative writing skills: Educational institutions should adopt AI-based strategies to enhance creative writing skills, emphasizing interactive learning environments.\u003c/p\u003e\n\u003cp\u003eIncorporating AI into curricula: It is essential to develop curricula that integrate AI for improving writing skills while training teachers on innovative teaching strategies.\u003c/p\u003e\n\u003cp\u003e2.Conducting in-depth studies on individual differences in student responses: Further research is recommended to understand how individual differences, such as cognitive styles and linguistic proficiency, impact students' ability to benefit from AI.\u003c/p\u003e\n\u003cp\u003e3.Designing teacher training programs: Training programs should be developed to equip teachers with the knowledge and skills to integrate AI in creative writing instruction, enhancing the learning experience for students.\u003c/p\u003e\n\u003cp\u003eIntegrating linguistic analysis with AI: AI-powered text analysis tools should be combined with traditional assessments to improve the accuracy of measuring students' progress in writing skills.\u003c/p\u003e\n\u003cp\u003e4.Addressing technical challenges associated with AI use: Since many students face technical difficulties when using AI tools, continuous technical support should be provided, along with the development of more user-friendly and interactive interfaces.\u003c/p\u003e\n\u003cp\u003e5.Developing adaptive feedback mechanisms: AI feedback mechanisms should be improved to better align with students' individual writing styles, making the learning process more personalized and effective.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSuggestions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e1.Expanding the scope of research to include larger and more diverse samples: Future studies should be conducted on larger student samples in different educational environments to ensure broader generalizability of the results.\u003c/p\u003e\n\u003cp\u003e2.Studying the impact of different types of artificial intelligence: Comparative studies between various AI models are recommended to determine which is most effective in enhancing students' creative writing skills.\u003c/p\u003e\n\u003cp\u003e3.Developing more accurate assessment tools: It is suggested to design advanced AI-based evaluation tools to assess students' progress in creative writing skills with greater objectivity and precision.\u003c/p\u003e\n\u003cp\u003e4.Analyzing the impact of AI use on students' motivation: Research should focus on the relationship between AI utilization and students’ motivation levels in learning creative writing, contributing to the development of more engaging teaching strategies.\u003c/p\u003e\n\u003cp\u003e5.Examining the impact of AI on creative aspects of writing: Studies should investigate how AI affects students’ critical and creative thinking to ensure they do not become passive recipients but instead develop their ability to generate original ideas.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study highlights the growing role of artificial intelligence in enhancing academic skills, particularly in the field of creative writing. The findings confirmed the effectiveness of the dialogic model in improving students' performance, while also identifying certain technical challenges that require innovative solutions. Based on these results, it is recommended to expand future research, develop AI tools that are more adaptive to students' needs, and integrate these technologies more extensively into educational curricula to maximize their potential in enhancing creative writing skills.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbdul-Bari M (2014) Fundamentals of Creative and Functional Writing. Dar Al-Fikr Al-Arabi\u003c/li\u003e\n\u003cli\u003eAbanmi A (2018) Creative Thinking in Language Production and Interpretation. King Saud University\u003c/li\u003e\n\u003cli\u003eAbu Hatab F, Othman S, Sadeq A (2008) Psychological assessment. 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Prof Discourse Communication 3(4):51\u0026ndash;63. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.24833/2687-0126-2021-3-4-51-63\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eNew Educ (2023), April 10 Creative writing in English language subject through project-based learning in elementary stage. Retrieved from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.new-educ.com/creative-writing-english-language\u003c/span\u003e\u003c/span\u003e\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":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6445869/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6445869/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study aimed to evaluate the effectiveness of the AI-based deliberative model in enhancing creative writing skills in English among third-year high school students in Riyadh. A quasi-experimental approach was adopted, with participants divided into an experimental group that used the deliberative model and a control group that followed traditional methods. Students\u0026rsquo; creative writing performance was assessed through a standardized test administered before and after the intervention, while data on the challenges encountered during the implementation of the model were also collected.\u003c/p\u003e \u003cp\u003eThe results revealed that students who used the AI-based deliberative model demonstrated significant improvements in all aspects of creative writing, including originality and creativity, organization and coherence, language use, critical and analytical thinking, and fluency and flexibility, compared to their peers in the control group. Statistical analyses indicated significant differences favoring the experimental group, confirming the model\u0026rsquo;s effectiveness in fostering creative writing.\u003c/p\u003e \u003cp\u003eRegarding challenges, students reported difficulties in aligning AI-generated feedback with their personal writing style, understanding AI outputs, and technical issues. However, these obstacles did not hinder their overall progress. The study recommends integrating AI-assisted writing tools into creative writing curricula and providing structured training for students to optimize their use of AI in writing development.\u003c/p\u003e","manuscriptTitle":"The Effectiveness of the AI-Based Pragmatic Model in Improving English Creative Writing Skills Among Third-Year High School Students in Riyadh","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-16 10:58:18","doi":"10.21203/rs.3.rs-6445869/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":"dbaa957d-2437-4410-8c92-87111a5b2c79","owner":[],"postedDate":"June 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":50056118,"name":"Social science/Education"},{"id":50056119,"name":"Social science/Science technology and society"}],"tags":[],"updatedAt":"2025-07-16T18:38:13+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-16 10:58:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6445869","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6445869","identity":"rs-6445869","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}