Exploring the integration of artificial intelligence in enhancing English speaking skills: an experimental study at Secondary school level in Punjab, Pakistan

Exploring the integration of artificial intelligence in enhancing English speaking skills: an experimental study at Secondary school level in Punjab, Pakistan | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Exploring the integration of artificial intelligence in enhancing English speaking skills: an experimental study at Secondary school level in Punjab, Pakistan Sobia Nageen, Dr. Hisham Ul Hasan Khawaja, Dr. Muhammad Sarwar, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8331993/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The integration of Artificial Intelligence (AI) into education is expanding rapidly, particularly in the area of language learning. This study examined the effectiveness of Chat GPT assisted learning module in enhancing speaking skills (in terms of range, accuracy, fluency, interaction and coherence) at secondary level. True-experimental design was employed, included fifty female students of 9th grade from a public school who were randomly assigned to experimental and control group. Speaking proficiency was evaluated with the support of ChatGPT, using the Common European Framework of Reference scale. Independent t- test was employed to compare the effectiveness of both methods. The findings show that the experimental group outperformed the control group in both monologue and dialogue tasks, with notable improvements in broad lexical range, grammatical accuracy, fluent speech, interactive engagement, and coherence. These results underscore the potential of AI-driven tools like ChatGPT to improve speaking skills and help bridge the gap between existing speaking proficiency levels and international language standards. Introduction Speaking is an essential skill in language learning. It is a collaborative process where individuals produce, receive, and understand information to construct meaning (Pitura, 2022). Those who are proficient in spoken English can enjoy numerus opportunities in across social ,academic and professional sectors (Akther, 2022; Manire et al., 2023).Despite its importance, Education First English Proficiency report (2024) declared that Pakistan ranked at 67 number with low proficiency out of 116 countries. Nageen et al. (2025) also found that 88% of secondary school students performed at basic English-speaking levels (A1–A2) across range, accuracy, fluency, interaction, and coherence, while only 12% reached pre-intermediate level (B1) and none achieved advanced proficiency (B2 or above). This flaw restricts their confidence and affects their future academic growth and professional opportunities. In Pakistani public schools, Traditional teaching practices primarily emphasize on rote learning and written exercises, with little focus on spoken English activities(Haryati, 2019). This sharp gap from international standards calls for innovative, technology-driven solutions such as Artificial Intelligence integration. The emergence of artificial Intelligence (AI) has introduced Chat GPT as a potential tool to improve speaking abilities by providing personalized learning, Interactive sessions and real time feedback. Many studies had been conducted for the last two years on Artificial intelligence in improving English language. Sayed et al. (2024)stated in his study that Chat GPT can enhance EFL learners' psychological wellbeing, speaking skills, autonomy and academic resilience.it has the potential to transform traditional methods. Yildiz (2024) examined the impact of Chat GPT on EFL learners’ self-efficacy in speaking, and indicated that Chat GPT has improved their grammar, fluency, pronunciation and stress level during talk. Slamet (2024) explores Chat GPT’s capacity as digital language learning tutor, underscoring its impact in boosting EFL learners’ motivation, opportunity to authentic materials and self-directing learning. The primary focus of above studies was to improve psychological wellbeing, motivation, confidence building removing stress with EFL learners. The integration of Chat GPT for enhancing qualitative aspects of spoken language (range, accuracy, fluency, interaction and coherence) as outlined by CEFR remained unexplored. To address these challenges, innovative educational approaches are needed. Integrating Artificial Intelligence, particularly Chat GPT, offers a modern solution to enhance English speaking skills. Chat GPT provides personalized, interactive practice with immediate feedback, engaging conversations, and customized learning experiences. This study aims to evaluate the effectiveness of ChatGPT in enhancing secondary school students' English-speaking proficiency. This study employs the CEFR framework as an assessment model, focusing on range, accuracy, fluency, interaction, and coherence to evaluate students' English-speaking skills. ChatGPT serves as both an assessment and learning tool, offering real-time feedback, interactive practice, and personalized learning. The ADDIE model provides a structured framework for designing, implementing, and evaluating the ChatGPT-assisted learning module developed in this study. By incorporating these frameworks, this study not only enhances students' speaking proficiency but also contributes to bridging gaps in existing research on AI-driven language learning. This study is important because it addresses the persistent gap in English-speaking proficiency among secondary school students by leveraging AI-driven learning and assessment. Traditional teaching methods in public schools emphasize rote learning and written exercises, leaving students with limited opportunities for spoken interaction. By integrating ChatGPT as both a learning and assessment tool, this study provides a structured and interactive approach to language practice, fostering real-time feedback, autonomy, and engagement. Its findings will contribute to improving instructional strategies, aligning language education with global proficiency standards, and equipping students with essential communication skills for academic and professional success. Objective To examine the effectiveness of the ChatGPT-assisted learning module in enhancing English-speaking skills. Hypothesis Ho1: There is no significant effect of the ChatGPT-assisted learning module in enhancing students’ English-speaking skills. Theoretical Framework This study is based on Vygotsky’s Sociocultural Theory, which explains that students learn better through interaction and support. According to this theory, learners can improve when they receive guidance from a more knowledgeable source. In this study, ChatGPT acts as a supportive learning partner, providing feedback and practice that help students strengthen their English-speaking skills. Literature Review Speaking is the act of constructing and conveying meaning through verbal and non-verbal symbols (Houn & Em, 2022). As a productive skill, it involves transforming thoughts into meaningful communication, whether in the form of monologues or dialogues (Karpovich et al., 2021). Despite English being the official language, only a small percentage of people can speak it fluently, and most secondary-level students struggle to communicate effectively even after studying it for 7 to 10 years (Abbas et al., 2018). Traditional teaching methods in Pakistan, like rote memorization and the grammar-translation method, hinder the development of English-speaking skills by prioritizing written over oral proficiency(Haryati, 2019). To bridge the gap in speaking proficiency, a shift toward more interactive and communicative teaching methods is essential. One potential solution lies in the integration of technology, which has revolutionized many aspects of education. Integrating technology into language instruction enhances learners' motivation and engagement, providing opportunities for interactive learning with instant feedback (De Souza et al., 2021).Advanced technologies like chatbots further expand opportunities for English language learners(Kohnke et al., 2023). Building on this technological advancement, Artificial Intelligence (AI) has emerged as a transformative tool in education. Artificial Intelligence (AI) refers to the simulation of human intelligence in machines designed to think and learn like humans. As AI has been playing a significant role in many sectors, the education sector has also received its attention in recent years.(Ahmad et al., 2021). The emergence of Artificial Intelligence (AI) in language learning represents a transformative shift in educational methodologies. Traditional language acquisition has been limited by one-size-fits-all approaches, restricted access to native speakers, and a lack of personalized learning strategies. In contrast, AI offers a fit-for-all solution by providing tailored, efficient, and engaging learning experiences. Its ability to process and analyze vast amounts of data makes it a powerful tool in reshaping language education.(Chen, 2024)The key examples of AI language learning include personalized language materials and resources, AI chat bots, and AI language learning software (Shafiee Rad, 2024).AI has transformed language learning by enhancing skills such as pronunciation, vocabulary acquisition, writing, and reading comprehension. For example, ELSA Speak improves pronunciation accuracy through real-time feedback on intonation and fluency (Anggraini, 2022; Sholekhah & Fakhrurriana, 2023).Li et al. (2020) investigated the effect of an AI-driven speech recognition system on improving pronunciation. People who received feedback from the AI system outperformed those who did not receive such feedback during their practice. Peng et al. (2023) narrated that in vocabulary learning, Storyfier integrates target words into narratives, proving more effective for retention than traditional methods.Phan et al. (2024) sated that digital storytelling also boosts students' interest and language proficiency. Duolingo’s adaptive learning supports reading and listening practice, showing positive outcomes for beginner and intermediate learners (Jiang et al., 2024; Loewen et al., 2020). These studies highlight AI’s role in fostering engaging, effective language learning experiences across various proficiency areas. Among the many AI tools available, Chat GPT, developed by Open AI, is an advanced AI language model that can engage in natural language conversations. It can understand and generate human-like responses, making it a valuable tool for language learning. (Khalil & Er, 2023). Chat GPT's capabilities include answering questions, providing explanations, and participating in interactive dialogues. Chat GPT, an advanced language model, offers numerous advantages for language learning that go beyond traditional teaching methods (Baskara, 2023; Kohnke et al., 2023). Several studies have explored the impact of ChatGPT on language learning. Prasetya and Syarif (2023) found that Chat GPT significantly enhances language accuracy, grammar understanding, and vocabulary proficiency in Indonesian higher education. Their study highlighted the tool’s ability to provide tailored feedback, improving self-awareness and goal-setting in students. Songsiengchai et al. (2023) reported that Chat GPT boosted Thai students' language skills, with a paired t-test confirming statistically significant improvements (p < 0.05). Students found the AI-based learning engaging and personalized, which increased motivation and self-confidence. Afkarin and Asmara (2024) observed that Chat GPT positively influenced student motivation and GPA, though they emphasized the need for further research on its long-term impact and integration in education. Explored the role of Chat GPT in English acquisition among ESL learners. The study concluded that while Chat GPT offers significant benefits like customized feedback and mock assignments, it also has drawbacks and risks if misused. Proper application of AI in language instruction can enhance learning efficiency and effectiveness, but it should be used responsibly to avoid undermining personal interactions and educational integrity. Hayashi and Sato (2024) studied the effectiveness of Chat GPT in enhancing English proficiency and reducing second language anxiety. The research found that using Chat GPT as an L2 interlocutor improved listening and speaking abilities, reduced L2 speaking anxiety, and fostered proactive learning attitudes, though it noted the need for more challenging assessments and a more robust research design. Yildiz (2024) found that integrating ChatGPT into EFL education significantly enhances students' speaking self-efficacy The existing literature highlights a gap in examining ChatGPT’s impact on speaking skills, particularly in terms of range, accuracy, fluency, interaction, and coherence at the secondary school level in Pakistan. This study is unique in applying the Common European Framework of Reference (CEFR), which includes six proficiency levels and five key indicators. Additionally, it integrates ChatGPT as both a learning tool and an assessor of speaking performance. By addressing this gap, the study aims to contribute to existing knowledge and bridge the gap between students’ current proficiency and international language standards. Methodology Research Design A true experimental pre-test, post-test control group design was adopted to measure the effectiveness of ChatGPT in enhancing English-speaking skills. This design was chosen because it ensures reliability and validity through random assignment, minimizing bias and allowing causal inferences. Participants, sampling and sample size The selected sample comprised ninth-grade female students who were registered in Science, Arts, and Computer Science groups at a public school in Sahiwal. These diverse groups were selected because English proficiency is essential across all academic disciplines for higher education, making it important to assess a broad range of learners. This school was selected due to its well-equipped IT lab, which was a crucial resource for conducting AI-driven assessment. The secondary level was selected because it serves as a foundational stage in the education system that forms the base for higher education. Ignoring English proficiency gaps at this stage can hinder success in higher education opportunities and limit career opportunities. A stratified systematic sampling technique was utilized to address the heterogeneity of the population. The total population consisted of three hundred and ten students. They were divided into three academic strata: Science (150 students), Arts (70 students), and Computer Science (90 students). The students were selected systematically from an ordered list based on roll numbers. Every sixth student was selected from the science and computer group, and every seventh student from the arts section. This k-th interval selection ensured randomness and equal selection probability. While academic streams generally show varying English capabilities in Pakistan, our systematic method ensured fair comparison to evaluate whether these typical differences existed or not. The academic distribution included Science (48%), Arts (22%), and Computer Science (30%) streams. A total of fifty participants (16% of the total population) were selected, aligning with educational research recommendations. According to Gay, Lorraine, and Mills (2012),a sample size of 10–20% of the total population is typically sufficient for educational studies, ensuring the reliability and generalizability of findings. Demographic data showed that almost all the students had 8 to 9 years of experience learning English in the public education system. Most students belonged to lower-middle to middle-class families. The socio-economic background is similar. They were all residents of an urban area. Their mother tongue was Punjabi and Urdu. Ninety-six percent of students were 14 years old while 4% were 15 years old. All participants have no additional support at home for improving speaking skills. Although participants belonged to different academic streams, they shared a uniform educational background: all were in Grade 9, enrolled in the same school, taught by the same English faculty, and followed the same curriculum and were assessed by the same examination system. This ensured consistency in teaching and learning conditions, allowing for a fair comparison of speaking proficiency levels across the groups Instruments Three key instruments were employed in this study. The detail of instruments is under: CEFR Scale The researcher adopted the Common European Framework of Reference for Languages (CEFR) to assess students' English-speaking proficiency. The CEFR is an internationally recognized framework that defines six levels of language proficiency, ranging from A1 (beginner) to C2 (proficient). These levels are categorized into three broad user groups: Basic users (A1–A2), Independent users (B1–B2), and Proficient users (C1–C2). Each level is characterized by specific descriptors that assess key aspects of language use, including range, accuracy, fluency, interaction, and coherence. This study utilized an organized marking scheme aligned with CEFR’s classification of proficiency levels such as A1, A2, B1, B2, C1, and C2. Since CEFR’s qualitative descriptors alone were not sufficient for detailed analysis, a numeric scoring scheme was established as it allowed researchers to calculate means, standard deviations, and one-way ANOVA for comparison of groups. Each speaking component, such as range, accuracy, fluency, interaction, and coherence, was evaluated using CEFR descriptors and assigned corresponding numeric values: A1 = 1 mark, A2 = 2 marks, B1 = 3 marks, B2 = 4 marks, C1 = 5 marks, and C2 = 6 marks. Each construct was assigned six marks, resulting in thirty marks each for monologue and dialogue tasks. The total marks for the spoken proficiency test were sixty. Students were classified into six levels: A1(beginning), A2(elementary), B1(pre-Intermediate), B2(upper Intermediate), C1(advanced), and C2(proficient). Based on their percentage ranges, they were further grouped into three categories: A1 and A2 as basic users, B1 and B2 as independent users, and C1 and C2 as proficient users. This systematic marking scheme ensures fair evaluation. The spoken English proficiency Test (SEPT) The Spoken English Proficiency Test (SEPT) was developed by the researcher using the Churchill paradigm steps for tool development, ensuring a systematic development process. To ensure feasibility and validity, a pilot study was conducted over one week with five students. Based on the experts’ feedback, the test format was revised to align with the IELTS structure and CEFR standards. The revision extended the duration from 4–5 minutes to 10–13 minutes and added a monologue section as effective speaking requires both independent speech and interactive communication. The revised test was then reviewed by two Associate Professors from the English department who confirmed its validity, linguistic appropriateness, and CEFR alignment. The test structure and scoring were refined accordingly. Internal consistency analysis using Cronbach’s alpha yielded a high reliability value of 0.872 across 13 items, supporting the test’s credibility. The final version of the Spoken English Proficiency Test (SEPT) consisted of three parts. The test duration was 10 to 13 minutes and consisted of three parts: Part one was a warm-up session that lasted two to three minutes, which aimed to help students feel comfortable during the conversation. Part two involved monologue tasks lasting four to five minutes on the topic: “Your Favorite Hobby.” Part three comprised a five-to-six-minute dialogue task on the topic of “A Recently Watched Film or Drama.” Chat GPT-Assisted Learning Module: A structured 12-week learning module was developed to integrate Chat GPT within students’ speaking practice, focusing on range, accuracy, fluency, interaction, and coherence. Aligned with the 9th-grade English syllabus, the module addressed proficiency gaps identified in the needs assessment while ensuring academic relevance. Each weekly session followed a consistent structure to maximize student engagement and learning. The structure of each session included: Presentation on the topics given in 9th grade Punjab text book board (10 - Minute) Interactive Practice with Chat GPT ( 35-Minute) Role Plays, Group Discussions, and Pair Work Activities ( 35-Minute) The module was reviewed by language experts, who confirmed its alignment with the CEFR descriptors and validated its pedagogical effectiveness. Procedure of the study Pre-Test Administration Before the intervention, Informed consent was obtained from the school’s administration, and the test was conducted in a controlled environment with stable internet access. Speaking Test activities (monologue and dialogue) were evaluated via Chat GPT to assess students' initial spoken proficiency. Using CEFR rubrics and a structured marking scheme, Chat GPT assessed range, accuracy, fluency, and coherence, generating real-time transcripts and providing immediate feedback and scores. However, interaction was evaluated manually by the researcher, as Chat GPT can’t perceive non-verbal cues. All transcripts, scores, and feedback were securely recorded for analysis. Group Assignment After the pre-test, participants were randomly divided into two groups. The Experimental Group consisted of students who received Chat GPT-assisted instruction, while the Control Group followed traditional speaking exercises without AI-based assistance. Intervention The intervention spanned 12 weeks, during which the Experimental Group engaged in Chat GPT-assisted training. This training focused on AI-generated feedback, structured speaking tasks, and interactive discussions. The Control Group, on the other hand, followed a conventional approach to speaking practice without AI integration. Post-Test Administration After the intervention, the post-test was conducted to measure students' progress in speaking proficiency. The post-test was administered in a controlled environment with stable internet access to ensure consistency with the pre-test conditions. Speaking test activities (monologue and dialogue) were evaluated via Chat GPT, which analyzed range, accuracy, fluency, and coherence using CEFR rubrics and a structured marking scheme. Chat GPT generated real-time transcripts, provided immediate feedback, and assigned scores. However, interaction was manually assessed by the researcher, as Chat GPT can’t interpret non-verbal cues. The recorded transcripts, scores, and feedback were systematically stored for comparative analysis with pre-test results, ensuring the reliability and validity of the assessment. Data Analysis The collected data was systematically analyzed to evaluate students' English-speaking proficiency using a structured approach aligned with the CEFR framework. First, all monologue and dialogue transcripts were arranged sequentially from Student 1 to 50. Next, a manual result list was prepared, documenting monologue, dialogue, and overall speaking test scores along with key subcomponents to facilitate smooth data entry into SPSS. The third step involved coding and entering the data into SPSS for statistical analysis. Inferential statistical tests, such as independent sample t-tests were employed to compare the performance of the experimental and control groups. Pre-test and post-test results were analyzed to determine the effectiveness of ChatGPT-assisted learning in enhancing students' speaking skills. The findings were interpreted in relation to the CEFR criteria, ensuring an objective assessment of progress in range, accuracy, fluency, interaction, and coherence. Results and Discussion The study aimed to examine the effectiveness of the ChatGPT-assisted learning module in enhancing secondary school students’ English-speaking skills across five key constructs: range, accuracy, fluency, interaction, and coherence. The findings indicated that students who engaged with the ChatGPT-based learning module showed significant improvements in these areas compared to those using traditional methods. This can be attributed to the interactive, personalized learning environment created by ChatGPT, which supported vocabulary expansion, improved grammatical accuracy, increased fluency, fostered better interaction, and enhanced coherence in students' spoken English. Table 1 Comparison of Overall English-Speaking Proficiency Levels in Pre-Test and Post-Test for Experimental and Control Groups Item Group Proficiency Levels Pre-test Post-test f f % M f f % M Overall Experimental A1: beginning 6 24% 16.12 0 0% 36.56 Speaking Group A2: elementary 17 68% 0 0% B1: Pre-intermediate 2 8% 3 12% B2: upper-intermediate 0 0% 21 84% C1: advanced 0 0% 1 4% C2: proficient 0 0% 0 0 Control A1: beginning 6 24% 16.56 4 16% 17.60 Group A2: elementary 15 60% 15 60% B1: Pre-intermediate 4 16% 6 24% B2: upper-intermediate 0 0% 0 0% C1: advanced 0 0% 0 0% C2: proficient 0 0% 0 0% As Table 1 illustrates the English-speaking proficiency levels of secondary school students categorized according to the CEFR framework. The experimental group showed a remarkable improvement, progressing from predominantly 92% basic users (A1-A2) at the pre-test to mainly 96% independent users (B1-B2) and a few proficient users (C1) in the post-test, demonstrating the effectiveness of the ChatGPT-assisted learning module in elevating English-speaking proficiency. The control group showed minimal progress, with most students remaining at basic user levels (A1–A2) from pre-test (84%) to post-test (76%). Only a slight increase was observed in independent users (B1) from 16% to 24%, and no students reached higher proficiency levels, underscoring the limited impact of traditional teaching methods. This outcome aligns with Xiao and Zhi (2023), who found that AI tools such as ChatGPT enhance EFL learners’ oral skills by providing immediate feedback and adaptive support, thereby accelerating language acquisition. Such improvement suggests that technology-mediated interaction offers learners more opportunities for authentic practice than conventional methods. In contrast, the control group remained at the basic user level (A1–A2), indicating minimal development in speaking ability. This finding is consistent with Rahman (2020), who observed that traditional instruction often focuses heavily on grammar and written exercises, leaving limited scope for oral communication practice. The disparity between the two groups underscores the potential of AI-assisted learning to address persistent gaps in speaking skill development within traditional classroom contexts. Table 2 Comparison of Overall Speaking Skills Scores Between Experimental and Control Groups in the Post-test Items Groups N M SD df t p English-Speaking Experimental 25 36.56 4.61 48 13.0 .000 control 25 17.60 5.64 p < 0.001 (highly significant) As shown in Table 2 , the overall English-speaking performance, was significantly better in the experimental group using ChatGPT-assisted learning (M = 36.56) compared to the control group, which followed the traditional method (M = 17.60). This statistically significant improvement (p = .000) highlights the effectiveness of ChatGPT-assisted learning in enhancing students' speaking skills in all key components, leading to the rejection of the null hypothesis (Ho2). These findings align with the previous literature, which supports the idea that language proficiency is enhanced through AI chatbots compared to traditional methods (Bekou et al., 2024) Table 3 Comparison of Monologue and its Key Components Scores between Experimental and Control Groups in the Post-Test Items Groups N M SD Df t P Monologue Experimental group 25 18.00 2.43 48 11.61 .000 control group 25 8.56 3.25 Range Experimental group 25 3.60 .500 48 9.79 .000 control group 25 1.84 .746 Accuracy Experimental group 25 3.60 .500 48 11.26 .000 control group 25 1.68 .690 Fluency Experimental group 25 3.60 .500 48 11.26 .000 control group 25 1.68 .690 Interaction Experimental group 25 3.60 .500 48 11.39 .000 control group 25 1.64 .700 Coherence Experimental group 25 3.60 .500 48 12.09 .000 Control group 25 1.64 .637 p < 0.001 (highly significant) As shown in Table 3 , the overall monologue performance, the experimental group (M = 18.00) significantly outperformed the control group (M = 8.56), with a p-value of .000, demonstrating the effectiveness of ChatGPT-based learning in enhancing monologue delivery. The experimental group showed improvements in all five constructs: range, with greater lexical diversity; accuracy, with fewer grammatical errors; fluency, with smoother speech and fewer pauses; interaction, with improved engagement and non-verbal cues; and coherence, with better organization and logical flow of ideas. These results support previous studies indicating that ChatGPT can effectively improve students' speaking abilities, including vocabulary, grammar, fluency, interaction, and coherence (Mohammad Ali, 2023; Nazeer et al., 2024; Nguyen Thi Thu, 2023; Wei-Xun & Jia-Ying, 2024). Linguistic range in the monologue, the experimental group (M = 3.60) significantly outperformed the control group (M = 1.84), with a p-value of. 000, reflecting better lexical variety. These are similar with the findings of Ngo (2024), who stated that using ChatGPT for vocabulary acquisition offers key advantages. It creates personalized and interactive learning experiences that cater to individual needs, enabling learners to expand their vocabulary and improve their language performance more effectively. For grammatical accuracy in monologue, the findings indicated that students who engaged with ChatGPT demonstrated improved grammatical accuracy, with fewer errors in sentence structure, verb tense. the experimental group (M = 3.60) significantly outperformed the control group (M = 1.68), with a p-value of. 000, reflecting more accurate speech. These findings are consistent with the research of Balcı (2024) and Boudouaia et al. (2024). For monologue fluency, the study showed that ChatGPT-based interaction enhanced students’ fluency, allowing them to speak more smoothly and with fewer hesitations. The experimental group (M = 3.60) significantly outperformed the control group (M = 1.68), with a p-value of.000, reflecting smoother and fluent speech without hesitation. These results are consistent with the studies by Nazeer et al. (2024) and Wei-Xun & Jia-Ying (2024) For monologue interaction, the findings showed that the experimental group (M = 3.60) significantly outperformed the control group (M = 1.64), with a p-value of .000, indicating enhanced engagement in speech. This improvement not only reflected in verbal interaction but also in the use of non-verbal cues, such as body language and facial expressions, which contributed to more dynamic and effective communication. These are similar to the findings of (Hayashi & Sato, 2024). Lastly, regarding coherence in monologue, the findings indicated that the experimental group (M = 3.60) demonstrated better coherence than the control group (M = 1.64), with a p-value of .000, indicating stronger logical flow in speech. These findings are consistent with those of Boudouaia et al. (2024) who found that the experimental group outperformed the control group in task completion, coherence, and cohesion. Table 4 Comparison of Dialogue and its Key Components Mean Scores in the Post-Test between Experimental and Control Groups Variable Groups N M SD Df t P Overall Dialogue Experimental group 25 18.60 2.466 48 12.325 .000 control group 25 9.04 2.992 Range Experimental group 25 3.68 .556 48 11.027 .000 control group 25 1.80 .645 Accuracy Experimental group 25 3.68 .556 48 11.720 .000 control group 25 1.80 .577 Fluency Experimental group 25 3.68 .556 48 11.720 .000 control group 25 1.80 .577 Interaction Experimental group 25 3.88 .525 48 12.492 .000 control group 25 1.84 .624 Coherence Experimental group 25 3.68 .556 48 11.027 .000 Control group 25 1.80 .645 p < 0.001 (highly significant) As shown in Table 4 , the overall dialogue performance, the experimental group (M = 18.60) significantly outperformed the control group (M = 9.04), with a p-value of .000, demonstrating the effectiveness of ChatGPT-based learning in improving overall dialogue skills. This finding aligns with previous studies showing AI tools effectively enhance vocabulary retention and recall more effectively than traditional methods, reduce language errors and improve grammar proficiency, promote greater comfort and fluency in speech through real-time dialogue, organize coherence in both written and spoken language.(Mohammad Ali, 2023; Nazeer et al., 2024; Nguyen Thi Thu, 2023; Wei-Xun & Jia-Ying, 2024). In terms of linguistic range in dialogue, the current study revealed that ChatGPT-assisted learning sessions expanded students’ lexical and grammatical variety. The experimental group (M = 3.68) outperformed the control group (M = 1.80), with a statistically significant difference (p = .000), showing better lexical variety. This finding is similar to Karataş et al. (2024) who stated the potential of Chat GPT in increasing vocabulary. Nazeer et al. (2024) and Song and Song (2023) explored that Chat GPT is very useful tool for improving vocabulary. Regarding grammatical accuracy in dialogue, the findings showed that the students who engaged with ChatGPT demonstrated improved grammatical control. The experimental group scored higher (M = 3.60) than the control group (M = 1.80), with the difference being statistically significant (p = .000). This supports existing evidence from Nazeer et al. (2024) emphasizing AI's role in improving learners’ grammar. In terms of fluency in dialogue, the experimental group achieved a mean score of (M = 3.60), significantly outperforming the control group (M = 1.80), with a p-value of .000. This suggests that ChatGPT-based dialogue practice led to more spontaneous and fluid speech, resonating with prior findings that AI tools promote fluency.(Nazeer et al., 2024) As for interaction in dialogue, the study showed notable improvement in students' conversational engagement through ChatGPT sessions. The experimental group (M = 3.88) scored significantly higher than the control group (M = 1.84), with a p-value of. 000.indicating better engagement. This aligned with Nazeer et al. (2024) and Hayashi and Sato (2024) studies who stated Chat GPT has positive effects in improving conversational skills. Lastly, in terms of coherence in dialogue, the results indicated a clear improvement in the logical flow and organization of students’ speech. The experimental group scored (M = 3.68), while the control group scored (M = 1.80), with the difference being statistically significant (p = .000). This finding underscores the effectiveness of ChatGPT in enhancing coherence, aligning with Song and Song (2023) who found that integrating ChatGPT into writing courses significantly improved students’ ability to produce coherent texts, alongside gains in vocabulary, grammar, and organization. These findings align with Vygotsky’s Sociocultural Theory: the use of ChatGPT provided the type of social and instructional support learners need to grow. Its real-time interaction and feedback mimicked the role of knowledgeable others, effectively helping students develop strong oral communication skills. Recent evidence, including Guan et al. (2024), confirms that AI tools can effectively mediate language learning. The ChatGPT-assisted module improved students’ linguistic range, grammar, fluency, interaction, and coherence, while also boosting confidence, motivation, and engagement within the learners’ Zone of Proximal Development. The findings have important implications for educators, curriculum developers, and policymakers, suggesting the need for AI-driven interventions in language teaching and a shift towards more interactive and technology-supported educational practices. While this study provides valuable insights, it was conducted with a limited sample of 50 female students from a public school in an urban area, which may limit the generalizability of the findings. Future research should involve a larger and more diverse sample to validate and expand upon these results. Additionally, further studies should explore the long-term effectiveness of AI-based interventions and examine the potential for integrating other interactive learning technologies to further enhance language skills development. Conclusion The study examined the effectiveness of the ChatGPT-assisted learning module in enhancing English-speaking skills among secondary school students. The experimental group showed significant improvements across all speaking competencies, suggesting the rejection of the null hypothesis (H₀₁) and confirming the effectiveness of the ChatGPT-assisted learning module. Descriptive analysis further showed that the majority of students progressed from basic (A1-A2) to the independent user level (B1-B2). Noticeable gains were observed in both monologue and dialogue tasks, particularly across the five core CEFR-aligned subcomponents: range, accuracy, fluency, interaction, and coherence. These findings demonstrate that the ChatGPT-assisted learning module was more effective than traditional methods in improving students’ spoken English skills. Abbreviations AI Artificial Intelligence ESPT English speaking proficiency test CEFR Common European Framework of Reference Declarations Ethics Statement: This study was approved by the Institutional Review Board (IRB) of Superior University, Lahore. All procedures involving human participants were conducted in accordance with institutional ethical standards. Written informed consent was obtained from all students, and formal permission was granted by the school administration prior to data collection. Competing Interests The author declares no competing interests. Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Author Contribution SN conceptualized and designed the study, collected and analyzed the data, interpreted the results, and wrote the manuscript. The author read and approved the final manuscript. Acknowledgement The author sincerely thanks Dr. Muhammad Sarwar for his invaluable supervision and guidance throughout this research. Special appreciation is extended to Dr. Hisham ul Hasan Khawaja for reviewing each section of the article and providing critical insights, particularly in designing the test activities; to Dr. Farhana for her assistance in developing the ChatGPT-assisted learning module; to Dr. Mehmood ul Hasan for support in the data analysis process; and to Ms. Shazia for her help with the literature review. Gratitude is also due to the students and the head of the institute for facilitating data collection. Data Availability The datasets generated and analyzed during the current study are not publicly available due to institutional restrictions but are available from the corresponding author upon reasonable request. References Abbas, N., Ashiq, U., & Abrar ul haq, M. (2018). Gap between acquired and required English learning objectives for the primary school students: Empirical evidence from Sargodha (Pakistan). Cogent Social Sciences , 4 (1), 1457421. Afkarin, M. Y., & Asmara, C. H. (2024). Investigating the Implementation of ChatGPT in English Language Education. Journey: Journal of English Language and Pedagogy , 7 (1), 57-66. Ahmad, S. F., Rahmat, M. K., Mubarik, M. S., Alam, M. M., & Hyder, S. I. (2021). Artificial intelligence and its role in education. Sustainability , 13 (22), 12902. Akther, F. (2022). English for personal and career development and its importance for better employment opportunities. Journal of Languages, Linguistics and Literary Studies , 2 (3), 95-100. Anggraini, A. (2022). Improving students' pronunciation skill using ELSA Speak application. Journey , 5 (1), 135-141. Balcı, Ö. (2024). The role of ChatGPT in English as a foreign language (EFL) learning and teaching: A systematic review. International Journal of Current Educational Studies , 3 (1). Baskara, R. (2023). Exploring the implications of ChatGPT for language learning in higher education. Indonesian Journal of English Language Teaching and Applied Linguistics , 7 (2), 343-358. Bekou, A., Mhamed, M. B., & Assissou, K. (2024). Exploring opportunities and challenges of using ChatGPT in English language teaching (ELT) in Morocco. Focus on ELT Journal , 6 (1), 87-106. Boudouaia, A., Mouas, S., & Kouider, B. (2024). A study on ChatGPT-4 as an innovative approach to enhancing English as a foreign language writing learning. Journal of Educational Computing Research , 62 (6), 1509-1537. Chen, Y. (2024). Enhancing Language Acquisition: The Role of AI in Facilitating Effective Language Learning. In (pp. 593-600). https://doi.org/10.2991/978-2-38476-253-8_71 De Souza, R., Parveen, R., Chupradit, S., Velasco, L. G., Arcinas, M., Tabuena, A. C., Pentang, J., & Ventayen, R. J. M. (2021). Language teachers’ pedagogical orientations in integrating technology in the online classroom: Its effect on students motivation and engagement. Turkish Journal of Computer and Mathematics Education , 12 . First, E. (2024). EF English Proficiency Index A Ranking of 116 Countries and Regions by English Skills United States. Retrieved from www.ef.edu/epi Gay, L. R., Mills, G. E., & Airasian, P. W. (2012). Educational research: Competencies for analysis and applications . Pearson. Guan, L., Zhang, E. Y., & Gu, M. M. (2024). Examining generative AI–mediated informal digital learning of English practices with social cognitive theory: a mixed-methods study. ReCALL , 1-17. Haryati, S. (2019). An analysis of student’s problem in learning English at smkn 1 simpang empat University of Muhammadiyah Malang]. Hayashi, K., & Sato, T. (2024). The Effectiveness of ChatGPT in Enhancing English Language Proficiency and Reducing Second Language Anxiety (L2). Houn, T., & Em, S. (2022). COMMON FACTORS AFFECTING GRADE-12 STUDENTS’SPEAKING FLUENCY: A SURVEY OF CAMBODIAN HIGH SCHOOL STUDENTS. Jurnal As-Salam , 6 (1), 11-24. Jiang, X., Peters, R., Plonsky, L., & Pajak, B. (2024). The Effectiveness of Duolingo English Courses in Developing Reading and Listening Proficiency. Karataş, F., Abedi, F. Y., Ozek Gunyel, F., Karadeniz, D., & Kuzgun, Y. (2024). Incorporating AI in foreign language education: An investigation into ChatGPT’s effect on foreign language learners. Education and Information Technologies , 1-24. Karpovich, I., Sheredekina, O., Krepkaia, T., & Voronova, L. (2021). The use of monologue speaking tasks to improve first-year students’ English-speaking skills. Education Sciences , 11 (6), 298. Khalil, M., & Er, E. (2023). Will ChatGPT G et You Caught? Rethinking of Plagiarism Detection. International Conference on Human-Computer Interaction, Kohnke, L., Moorhouse, B. L., & Zou, D. (2023). ChatGPT for language teaching and learning. RELC journal , 54 (2), 537-550. Li, J., Xie, H., Li, D., & Lu, X. (2020). Effects of an AI-based speech recognition system on improving EFL learners’ pronunciation. Computers & Education , 156 , 103952. Loewen, S., Isbell, D. R., & Sporn, Z. (2020). The effectiveness of app‐based language instruction for developing receptive linguistic knowledge and oral communicative ability. Foreign Language Annals , 53 (2), 209-233. Manire, E., Kilag, O. K., Cordova Jr, N., Tan, S. J., Poligrates, J., & Omaña, E. (2023). Artificial Intelligence and English Language Learning: A Systematic Review. Excellencia: International Multi-disciplinary Journal of Education (2994-9521) , 1 (5), 485-497. Mohammad Ali, A. (2023). An Intervention Study on the Use of Artificial Intelligence in the ESL Classroom: English teacher perspectives on the Effectiveness of ChatGPT for Personalized Language LearningEn. In. Nageen, S., Sarwar, M., Alam, M., Jabeen, M., & Tayyab, J. (2025). Assessing English-speaking proficiency among secondary school students in Pakistan: a quantitative cross-sectional study. Language Testing in Asia , 15 (1), 49. https://doi.org/10.1186/s40468-025-00384-7 Nazeer, I., Yasmin, S., & Khan, N. M. (2024). English language acquisition through chatgpt: a study on personalized conversational practice and feedback. Journal of Applied Linguistics and TESOL (JALT) , 7 (4), 1076-1091. Ngo, T. (2024). The use of ChatGPT for vocabulary acquisition: A literature review. Available at SSRN 5059052 . Nguyen Thi Thu, H. (2023). EFL teachers’ perspectives toward the use of ChatGPT in writing classes: A case study at Van Lang University. Nguyen, TTH (2023). EFL Teachers’ Perspectives toward the Use of ChatGPT in Writing Classes: A Case Study at Van Lang University. International Journal of Language Instruction , 2 (3), 1-47. Peng, Z., Wang, X., Han, Q., Zhu, J., Ma, X., & Qu, H. (2023). Storyfier: Exploring vocabulary learning support with text generation models. Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology, Phan, T. A. N., Le, H. H., & Phan, G. A. V. (2024). From Words to Wonders: EFL Students' Perceptions of Digital Storytelling for Language Learning. International Journal of Language Instruction , 3 (4), 59-92. Pitura, J. (2022). Developing L2 speaking skills in English-medium EFL higher education. Innovation in Language Learning and Teaching , 16 (2), 118-143. Prasetya, R. E., & Syarif, A. (2023). ChatGPT as a Tool for Language Development: Investigating Its Impact on Proficiency and Self-Evaluation Accuracy in Indonesian Higher Education. Voices of English Language Education Society , 7 (3), 402-415. Sayed, B. T., Bani Younes, Z. B., Alkhayyat, A., Adhamova, I., & Teferi, H. (2024). To be with artificial intelligence in oral test or not to be: a probe into the traces of success in speaking skill, psychological well-being, autonomy, and academic buoyancy. Language Testing in Asia , 14 (1), 49. Shafiee Rad, H. (2024). Revolutionizing L2 speaking proficiency, willingness to communicate, and perceptions through artificial intelligence: a case of Speeko application. Innovation in Language Learning and Teaching , 1-16. Sholekhah, M. F., & Fakhrurriana, R. (2023). The Use of ELSA Speak as a Mobile-Assisted Language Learning (MALL) towards EFL Students Pronunciation. JELITA: Journal of Education, Language Innovation, and Applied Linguistics , 2 (2), 93-100. Slamet, J. (2024). Potential of ChatGPT as a digital language learning assistant: EFL teachers’ and students’ perceptions. Discover Artificial Intelligence , 4 (1), 46. Song, C., & Song, Y. (2023). Enhancing academic writing skills and motivation: assessing the efficacy of ChatGPT in AI-assisted language learning for EFL students. Frontiers in Psychology , 14 , 1260843. Songsiengchai, S., Sereerat, B.-o., & Watananimitgul, W. (2023). Leveraging Artificial Intelligence (AI): Chat GPT for Effective English Language Learning among Thai Students. English Language Teaching , 16 (11), 1-68. Wei-Xun, L., & Jia-Ying, Z. (2024). Impact of AI-Driven Language Learning Apps on Vocabulary Acquisition among English Learners. Research Studies in English Language Teaching and Learning , 2 (1), 1-11. Yildiz, C. (2024). ChatGPT Integration in EFL Education: A Path to Enhanced Speaking Self-Efficacy. Novitas-ROYAL (Research on Youth and Language) , 18 (2), 167-182. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8331993","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":559205061,"identity":"292ca7fb-0b4a-4919-b844-d9d87fff3a09","order_by":0,"name":"Sobia Nageen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAklEQVRIiWNgGAWjYFACHoYDUBbjgw8VQIqZuYFoLcyGM86AKEbCWmCATZq3DWwbfi3y7b0HD/zcYWfXz957THLmvNpo/naglh8V23BqMThzLuFg75nk5Jk955ItPm47njvjMGMDY8+Z27i1SOQYHOBtY042uJFjeHPmtmO5DUAtzIxtuLXIz8gxOPi3rT7Z/v4bA2neOcdy5xPSwnAjx+Awb9thOwMJHiNp3oaa3A2EtID8cli27XiCxJkcY8MZxw7kbgRqOYjPL8AQO/zxbVu1PX/7GcMHH2rqcuedP3zwwY8KPA6DgsQGCH0YTB4gqB4I7KF0HTGKR8EoGAWjYIQBAAqfY2SQGUHAAAAAAElFTkSuQmCC","orcid":"","institution":"Superior University Lahore","correspondingAuthor":true,"prefix":"","firstName":"Sobia","middleName":"","lastName":"Nageen","suffix":""},{"id":559205062,"identity":"429b0a16-1c36-4820-a378-0e876818096f","order_by":1,"name":"Dr. Hisham Ul Hasan Khawaja","email":"","orcid":"","institution":"Superior University Lahore","correspondingAuthor":false,"prefix":"Dr.","firstName":"Hisham","middleName":"Ul Hasan","lastName":"Khawaja","suffix":""},{"id":559205068,"identity":"343a287b-105f-49fc-84f3-5e9e5b82b8a0","order_by":2,"name":"Dr. Muhammad Sarwar","email":"","orcid":"","institution":"Superior University Lahore","correspondingAuthor":false,"prefix":"Dr.","firstName":"Muhammad","middleName":"","lastName":"Sarwar","suffix":""},{"id":559205069,"identity":"32c67de6-5a5e-4c6c-aeec-653e3f8501d4","order_by":3,"name":"Dr. Farhana Akmal","email":"","orcid":"","institution":"Superior University Lahore","correspondingAuthor":false,"prefix":"Dr.","firstName":"Farhana","middleName":"","lastName":"Akmal","suffix":""},{"id":559205072,"identity":"ceace946-faa7-48ed-a3e3-3694de075d37","order_by":4,"name":". 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It is a collaborative process where individuals produce, receive, and understand information to construct meaning (Pitura, 2022). Those who are proficient in spoken English can enjoy numerus opportunities in across social ,academic and professional sectors (Akther, 2022; Manire et al., 2023).Despite its importance, Education First English Proficiency report (2024) declared that Pakistan ranked at 67 number with low proficiency out of 116 countries.\u003c/p\u003e \u003cp\u003eNageen et al. (2025) also found that 88% of secondary school students performed at basic English-speaking levels (A1\u0026ndash;A2) across range, accuracy, fluency, interaction, and coherence, while only 12% reached pre-intermediate level (B1) and none achieved advanced proficiency (B2 or above). This flaw restricts their confidence and affects their future academic growth and professional opportunities. In Pakistani public schools, Traditional teaching practices primarily emphasize on rote learning and written exercises, with little focus on spoken English activities(Haryati, 2019). This sharp gap from international standards calls for innovative, technology-driven solutions such as Artificial Intelligence integration.\u003c/p\u003e \u003cp\u003eThe emergence of artificial Intelligence (AI) has introduced Chat GPT as a potential tool to improve speaking abilities by providing personalized learning, Interactive sessions and real time feedback. Many studies had been conducted for the last two years on Artificial intelligence in improving English language. Sayed et al. (2024)stated in his study that Chat GPT can enhance EFL learners' psychological wellbeing, speaking skills, autonomy and academic resilience.it has the potential to transform traditional methods. Yildiz (2024) examined the impact of Chat GPT on EFL learners\u0026rsquo; self-efficacy in speaking, and indicated that Chat GPT has improved their grammar, fluency, pronunciation and stress level during talk. Slamet (2024) explores Chat GPT\u0026rsquo;s capacity as digital language learning tutor, underscoring its impact in boosting EFL learners\u0026rsquo; motivation, opportunity to authentic materials and self-directing learning. The primary focus of above studies was to improve psychological wellbeing, motivation, confidence building removing stress with EFL learners. The integration of Chat GPT for enhancing qualitative aspects of spoken language (range, accuracy, fluency, interaction and coherence) as outlined by CEFR remained unexplored.\u003c/p\u003e \u003cp\u003eTo address these challenges, innovative educational approaches are needed. Integrating Artificial Intelligence, particularly Chat GPT, offers a modern solution to enhance English speaking skills. Chat GPT provides personalized, interactive practice with immediate feedback, engaging conversations, and customized learning experiences. This study aims to evaluate the effectiveness of ChatGPT in enhancing secondary school students' English-speaking proficiency. This study employs the CEFR framework as an assessment model, focusing on range, accuracy, fluency, interaction, and coherence to evaluate students' English-speaking skills. ChatGPT serves as both an assessment and learning tool, offering real-time feedback, interactive practice, and personalized learning. The ADDIE model provides a structured framework for designing, implementing, and evaluating the ChatGPT-assisted learning module developed in this study. By incorporating these frameworks, this study not only enhances students' speaking proficiency but also contributes to bridging gaps in existing research on AI-driven language learning.\u003c/p\u003e \u003cp\u003eThis study is important because it addresses the persistent gap in English-speaking proficiency among secondary school students by leveraging AI-driven learning and assessment. Traditional teaching methods in public schools emphasize rote learning and written exercises, leaving students with limited opportunities for spoken interaction. By integrating ChatGPT as both a learning and assessment tool, this study provides a structured and interactive approach to language practice, fostering real-time feedback, autonomy, and engagement. Its findings will contribute to improving instructional strategies, aligning language education with global proficiency standards, and equipping students with essential communication skills for academic and professional success.\u003c/p\u003e\n\u003ch3\u003eObjective\u003c/h3\u003e\n\u003cp\u003eTo examine the effectiveness of the ChatGPT-assisted learning module in enhancing English-speaking skills.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eHypothesis\u003c/h2\u003e \u003cp\u003eHo1: There is no significant effect of the ChatGPT-assisted learning module in enhancing students\u0026rsquo; English-speaking skills.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eTheoretical Framework\u003c/h3\u003e\n\u003cp\u003eThis study is based on Vygotsky\u0026rsquo;s Sociocultural Theory, which explains that students learn better through interaction and support. According to this theory, learners can improve when they receive guidance from a more knowledgeable source. In this study, ChatGPT acts as a supportive learning partner, providing feedback and practice that help students strengthen their English-speaking skills.\u003c/p\u003e"},{"header":"Literature Review","content":"\u003cp\u003eSpeaking is the act of constructing and conveying meaning through verbal and non-verbal symbols (Houn \u0026amp; Em, 2022). As a productive skill, it involves transforming thoughts into meaningful communication, whether in the form of monologues or dialogues (Karpovich et al., 2021). Despite English being the official language, only a small percentage of people can speak it fluently, and most secondary-level students struggle to communicate effectively even after studying it for 7 to 10 years (Abbas et al., 2018). Traditional teaching methods in Pakistan, like rote memorization and the grammar-translation method, hinder the development of English-speaking skills by prioritizing written over oral proficiency(Haryati, 2019). To bridge the gap in speaking proficiency, a shift toward more interactive and communicative teaching methods is essential.\u003c/p\u003e \u003cp\u003eOne potential solution lies in the integration of technology, which has revolutionized many aspects of education. Integrating technology into language instruction enhances learners' motivation and engagement, providing opportunities for interactive learning with instant feedback (De Souza et al., 2021).Advanced technologies like chatbots further expand opportunities for English language learners(Kohnke et al., 2023).\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eBuilding on this technological advancement, Artificial Intelligence (AI) has emerged as a transformative tool in education. Artificial Intelligence (AI) refers to the simulation of human intelligence in machines designed to think and learn like humans. As AI has been playing a significant role in many sectors, the education sector has also received its attention in recent years.(Ahmad et al., 2021). The emergence of Artificial Intelligence (AI) in language learning represents a transformative shift in educational methodologies. Traditional language acquisition has been limited by one-size-fits-all approaches, restricted access to native speakers, and a lack of personalized learning strategies. In contrast, AI offers a fit-for-all solution by providing tailored, efficient, and engaging learning experiences. Its ability to process and analyze vast amounts of data makes it a powerful tool in reshaping language education.(Chen, 2024)The key examples of AI language learning include personalized language materials and resources, AI chat bots, and AI language learning software (Shafiee Rad, 2024).AI has transformed language learning by enhancing skills such as pronunciation, vocabulary acquisition, writing, and reading comprehension. For example, ELSA Speak improves pronunciation accuracy through real-time feedback on intonation and fluency (Anggraini, 2022; Sholekhah \u0026amp; Fakhrurriana, 2023).Li et al. (2020) investigated the effect of an AI-driven speech recognition system on improving pronunciation. People who received feedback from the AI system outperformed those who did not receive such feedback during their practice. Peng et al. (2023) narrated that in vocabulary learning, Storyfier integrates target words into narratives, proving more effective for retention than traditional methods.Phan et al. (2024) sated that digital storytelling also boosts students' interest and language proficiency. Duolingo\u0026rsquo;s adaptive learning supports reading and listening practice, showing positive outcomes for beginner and intermediate learners (Jiang et al., 2024; Loewen et al., 2020). These studies highlight AI\u0026rsquo;s role in fostering engaging, effective language learning experiences across various proficiency areas.\u003c/p\u003e\u003cp\u003eAmong the many AI tools available, Chat GPT, developed by Open AI, is an advanced AI language model that can engage in natural language conversations. It can understand and generate human-like responses, making it a valuable tool for language learning. (Khalil \u0026amp; Er, 2023). Chat GPT's capabilities include answering questions, providing explanations, and participating in interactive dialogues. Chat GPT, an advanced language model, offers numerous advantages for language learning that go beyond traditional teaching methods (Baskara, 2023; Kohnke et al., 2023). Several studies have explored the impact of ChatGPT on language learning.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ePrasetya and Syarif (2023) found that Chat GPT significantly enhances language accuracy, grammar understanding, and vocabulary proficiency in Indonesian higher education. Their study highlighted the tool\u0026rsquo;s ability to provide tailored feedback, improving self-awareness and goal-setting in students.\u003c/p\u003e \u003cp\u003eSongsiengchai et al. (2023) reported that Chat GPT boosted Thai students' language skills, with a paired t-test confirming statistically significant improvements (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Students found the AI-based learning engaging and personalized, which increased motivation and self-confidence.\u003c/p\u003e \u003cp\u003eAfkarin and Asmara (2024) observed that Chat GPT positively influenced student motivation and GPA, though they emphasized the need for further research on its long-term impact and integration in education. Explored the role of Chat GPT in English acquisition among ESL learners. The study concluded that while Chat GPT offers significant benefits like customized feedback and mock assignments, it also has drawbacks and risks if misused. Proper application of AI in language instruction can enhance learning efficiency and effectiveness, but it should be used responsibly to avoid undermining personal interactions and educational integrity.\u003c/p\u003e \u003cp\u003eHayashi and Sato (2024) studied the effectiveness of Chat GPT in enhancing English proficiency and reducing second language anxiety. The research found that using Chat GPT as an L2 interlocutor improved listening and speaking abilities, reduced L2 speaking anxiety, and fostered proactive learning attitudes, though it noted the need for more challenging assessments and a more robust research design. Yildiz (2024) found that integrating ChatGPT into EFL education significantly enhances students' speaking self-efficacy\u003c/p\u003e \u003cp\u003eThe existing literature highlights a gap in examining ChatGPT\u0026rsquo;s impact on speaking skills, particularly in terms of range, accuracy, fluency, interaction, and coherence at the secondary school level in Pakistan. This study is unique in applying the Common European Framework of Reference (CEFR), which includes six proficiency levels and five key indicators. Additionally, it integrates ChatGPT as both a learning tool and an assessor of speaking performance. By addressing this gap, the study aims to contribute to existing knowledge and bridge the gap between students\u0026rsquo; current proficiency and international language standards.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eResearch Design\u003c/h2\u003e \u003cp\u003eA true experimental pre-test, post-test control group design was adopted to measure the effectiveness of ChatGPT in enhancing English-speaking skills. This design was chosen because it ensures reliability and validity through random assignment, minimizing bias and allowing causal inferences.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eParticipants, sampling and sample size\u003c/h2\u003e \u003cp\u003eThe selected sample comprised ninth-grade female students who were registered in Science, Arts, and Computer Science groups at a public school in Sahiwal. These diverse groups were selected because English proficiency is essential across all academic disciplines for higher education, making it important to assess a broad range of learners. This school was selected due to its well-equipped IT lab, which was a crucial resource for conducting AI-driven assessment. The secondary level was selected because it serves as a foundational stage in the education system that forms the base for higher education. Ignoring English proficiency gaps at this stage can hinder success in higher education opportunities and limit career opportunities.\u003c/p\u003e \u003cp\u003eA stratified systematic sampling technique was utilized to address the heterogeneity of the population. The total population consisted of three hundred and ten students. They were divided into three academic strata: Science (150 students), Arts (70 students), and Computer Science (90 students). The students were selected systematically from an ordered list based on roll numbers. Every sixth student was selected from the science and computer group, and every seventh student from the arts section. This k-th interval selection ensured randomness and equal selection probability. While academic streams generally show varying English capabilities in Pakistan, our systematic method ensured fair comparison to evaluate whether these typical differences existed or not. The academic distribution included Science (48%), Arts (22%), and Computer Science (30%) streams.\u003c/p\u003e \u003cp\u003eA total of fifty participants (16% of the total population) were selected, aligning with educational research recommendations. According to Gay, Lorraine, and Mills (2012),a sample size of 10\u0026ndash;20% of the total population is typically sufficient for educational studies, ensuring the reliability and generalizability of findings.\u003c/p\u003e \u003cp\u003eDemographic data showed that almost all the students had 8 to 9 years of experience learning English in the public education system. Most students belonged to lower-middle to middle-class families. The socio-economic background is similar. They were all residents of an urban area. Their mother tongue was Punjabi and Urdu. Ninety-six percent of students were 14 years old while 4% were 15 years old. All participants have no additional support at home for improving speaking skills.\u003c/p\u003e \u003cp\u003eAlthough participants belonged to different academic streams, they shared a uniform educational background: all were in Grade 9, enrolled in the same school, taught by the same English faculty, and followed the same curriculum and were assessed by the same examination system. This ensured consistency in teaching and learning conditions, allowing for a fair comparison of speaking proficiency levels across the groups\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eInstruments\u003c/h3\u003e\n\u003cp\u003eThree key instruments were employed in this study. The detail of instruments is under:\u003c/p\u003e\n\u003ch3\u003eCEFR Scale\u003c/h3\u003e\n\u003cp\u003eThe researcher adopted the Common European Framework of Reference for Languages (CEFR) to assess students' English-speaking proficiency. The CEFR is an internationally recognized framework that defines six levels of language proficiency, ranging from A1 (beginner) to C2 (proficient). These levels are categorized into three broad user groups: Basic users (A1\u0026ndash;A2), Independent users (B1\u0026ndash;B2), and Proficient users (C1\u0026ndash;C2). Each level is characterized by specific descriptors that assess key aspects of language use, including range, accuracy, fluency, interaction, and coherence.\u003c/p\u003e \u003cp\u003eThis study utilized an organized marking scheme aligned with CEFR\u0026rsquo;s classification of proficiency levels such as A1, A2, B1, B2, C1, and C2. Since CEFR\u0026rsquo;s qualitative descriptors alone were not sufficient for detailed analysis, a numeric scoring scheme was established as it allowed researchers to calculate means, standard deviations, and one-way ANOVA for comparison of groups. Each speaking component, such as range, accuracy, fluency, interaction, and coherence, was evaluated using CEFR descriptors and assigned corresponding numeric values: A1\u0026thinsp;=\u0026thinsp;1 mark, A2\u0026thinsp;=\u0026thinsp;2 marks, B1\u0026thinsp;=\u0026thinsp;3 marks, B2\u0026thinsp;=\u0026thinsp;4 marks, C1\u0026thinsp;=\u0026thinsp;5 marks, and C2\u0026thinsp;=\u0026thinsp;6 marks. Each construct was assigned six marks, resulting in thirty marks each for monologue and dialogue tasks. The total marks for the spoken proficiency test were sixty. Students were classified into six levels: A1(beginning), A2(elementary), B1(pre-Intermediate), B2(upper Intermediate), C1(advanced), and C2(proficient). Based on their percentage ranges, they were further grouped into three categories: A1 and A2 as basic users, B1 and B2 as independent users, and C1 and C2 as proficient users. This systematic marking scheme ensures fair evaluation.\u003c/p\u003e \u003cp\u003e \u003cb\u003eThe spoken English proficiency Test\u003c/b\u003e (SEPT)\u003c/p\u003e \u003cp\u003eThe Spoken English Proficiency Test (SEPT) was developed by the researcher using the Churchill paradigm steps for tool development, ensuring a systematic development process.\u003c/p\u003e \u003cp\u003eTo ensure feasibility and validity, a pilot study was conducted over one week with five students. Based on the experts\u0026rsquo; feedback, the test format was revised to align with the IELTS structure and CEFR standards. The revision extended the duration from 4\u0026ndash;5 minutes to 10\u0026ndash;13 minutes and added a monologue section as effective speaking requires both independent speech and interactive communication. The revised test was then reviewed by two Associate Professors from the English department who confirmed its validity, linguistic appropriateness, and CEFR alignment. The test structure and scoring were refined accordingly. Internal consistency analysis using Cronbach\u0026rsquo;s alpha yielded a high reliability value of 0.872 across 13 items, supporting the test\u0026rsquo;s credibility.\u003c/p\u003e \u003cp\u003eThe final version of the Spoken English Proficiency Test (SEPT) consisted of three parts. The test duration was 10 to 13 minutes and consisted of three parts: Part one was a warm-up session that lasted two to three minutes, which aimed to help students feel comfortable during the conversation. Part two involved monologue tasks lasting four to five minutes on the topic: \u0026ldquo;Your Favorite Hobby.\u0026rdquo; Part three comprised a five-to-six-minute dialogue task on the topic of \u0026ldquo;A Recently Watched Film or Drama.\u0026rdquo;\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eChat GPT-Assisted Learning Module:\u003c/h2\u003e \u003cp\u003eA structured 12-week learning module was developed to integrate Chat GPT within students\u0026rsquo; speaking practice, focusing on range, accuracy, fluency, interaction, and coherence. Aligned with the 9th-grade English syllabus, the module addressed proficiency gaps identified in the needs assessment while ensuring academic relevance. Each weekly session followed a consistent structure to maximize student engagement and learning. The structure of each session included:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003ePresentation on the topics given in 9th grade Punjab text book board (10\u003cb\u003e-\u003c/b\u003eMinute)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eInteractive Practice with Chat GPT \u003cb\u003e(\u003c/b\u003e35-Minute)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eRole Plays, Group Discussions, and Pair Work Activities \u003cb\u003e(\u003c/b\u003e35-Minute)\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThe module was reviewed by language experts, who confirmed its alignment with the CEFR descriptors and validated its pedagogical effectiveness.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eProcedure of the study\u003c/h2\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003ePre-Test Administration\u003c/h2\u003e \u003cp\u003eBefore the intervention, Informed consent was obtained from the school\u0026rsquo;s administration, and the test was conducted in a controlled environment with stable internet access. Speaking Test activities (monologue and dialogue) were evaluated via Chat GPT to assess students' initial spoken proficiency. Using CEFR rubrics and a structured marking scheme, Chat GPT assessed range, accuracy, fluency, and coherence, generating real-time transcripts and providing immediate feedback and scores. However, interaction was evaluated manually by the researcher, as Chat GPT can\u0026rsquo;t perceive non-verbal cues. All transcripts, scores, and feedback were securely recorded for analysis.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eGroup Assignment\u003c/h2\u003e \u003cp\u003eAfter the pre-test, participants were randomly divided into two groups. The Experimental Group consisted of students who received Chat GPT-assisted instruction, while the Control Group followed traditional speaking exercises without AI-based assistance.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eIntervention\u003c/h2\u003e \u003cp\u003eThe intervention spanned 12 weeks, during which the Experimental Group engaged in Chat GPT-assisted training. This training focused on AI-generated feedback, structured speaking tasks, and interactive discussions. The Control Group, on the other hand, followed a conventional approach to speaking practice without AI integration.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003ePost-Test Administration\u003c/h2\u003e \u003cp\u003eAfter the intervention, the post-test was conducted to measure students' progress in speaking proficiency. The post-test was administered in a controlled environment with stable internet access to ensure consistency with the pre-test conditions. Speaking test activities (monologue and dialogue) were evaluated via Chat GPT, which analyzed range, accuracy, fluency, and coherence using CEFR rubrics and a structured marking scheme. Chat GPT generated real-time transcripts, provided immediate feedback, and assigned scores. However, interaction was manually assessed by the researcher, as Chat GPT can\u0026rsquo;t interpret non-verbal cues. The recorded transcripts, scores, and feedback were systematically stored for comparative analysis with pre-test results, ensuring the reliability and validity of the assessment.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eData Analysis\u003c/h2\u003e \u003cp\u003eThe collected data was systematically analyzed to evaluate students' English-speaking proficiency using a structured approach aligned with the CEFR framework. First, all monologue and dialogue transcripts were arranged sequentially from Student 1 to 50. Next, a manual result list was prepared, documenting monologue, dialogue, and overall speaking test scores along with key subcomponents to facilitate smooth data entry into SPSS. The third step involved coding and entering the data into SPSS for statistical analysis. Inferential statistical tests, such as independent sample t-tests were employed to compare the performance of the experimental and control groups. Pre-test and post-test results were analyzed to determine the effectiveness of ChatGPT-assisted learning in enhancing students' speaking skills. The findings were interpreted in relation to the CEFR criteria, ensuring an objective assessment of progress in range, accuracy, fluency, interaction, and coherence.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results and Discussion","content":"\u003cp\u003eThe study aimed to examine the effectiveness of the ChatGPT-assisted learning module in enhancing secondary school students\u0026rsquo; English-speaking skills across five key constructs: range, accuracy, fluency, interaction, and coherence. The findings indicated that students who engaged with the ChatGPT-based learning module showed significant improvements in these areas compared to those using traditional methods. This can be attributed to the interactive, personalized learning environment created by ChatGPT, which supported vocabulary expansion, improved grammatical accuracy, increased fluency, fostered better interaction, and enhanced coherence in students' spoken English.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eComparison of Overall English-Speaking Proficiency Levels in Pre-Test and Post-Test for Experimental and Control Groups\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eItem\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProficiency Levels\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePre-test\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c10\" namest=\"c6\"\u003e \u003cp\u003ePost-test\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003ef %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003ef %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExperimental\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA1: beginning\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e24%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e16.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e36.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpeaking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA2: elementary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e68%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eB1: Pre-intermediate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e12%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eB2: upper-intermediate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e84%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC1: advanced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC2: proficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA1: beginning\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e24%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e16.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e16%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e17.60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA2: elementary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e60%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e60%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eB1: Pre-intermediate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e16%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e24%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eB2: upper-intermediate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC1: advanced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC2: proficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAs Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e illustrates the English-speaking proficiency levels of secondary school students categorized according to the CEFR framework. The experimental group showed a remarkable improvement, progressing from predominantly 92% basic users (A1-A2) at the pre-test to mainly 96% independent users (B1-B2) and a few proficient users (C1) in the post-test, demonstrating the effectiveness of the ChatGPT-assisted learning module in elevating English-speaking proficiency. The control group showed minimal progress, with most students remaining at basic user levels (A1\u0026ndash;A2) from pre-test (84%) to post-test (76%). Only a slight increase was observed in independent users (B1) from 16% to 24%, and no students reached higher proficiency levels, underscoring the limited impact of traditional teaching methods.\u003c/p\u003e \u003cp\u003eThis outcome aligns with Xiao and Zhi (2023), who found that AI tools such as ChatGPT enhance EFL learners\u0026rsquo; oral skills by providing immediate feedback and adaptive support, thereby accelerating language acquisition. Such improvement suggests that technology-mediated interaction offers learners more opportunities for authentic practice than conventional methods.\u003c/p\u003e \u003cp\u003eIn contrast, the control group remained at the basic user level (A1\u0026ndash;A2), indicating minimal development in speaking ability. This finding is consistent with Rahman (2020), who observed that traditional instruction often focuses heavily on grammar and written exercises, leaving limited scope for oral communication practice. The disparity between the two groups underscores the potential of AI-assisted learning to address persistent gaps in speaking skill development within traditional classroom contexts.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eComparison of Overall Speaking Skills Scores Between Experimental and Control Groups in the Post-test\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eItems\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGroups\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eM\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eSD\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003edf\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnglish-Speaking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExperimental\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003econtrol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001 (highly significant)\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eAs shown in Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the overall English-speaking performance, was significantly better in the experimental group using ChatGPT-assisted learning (M\u0026thinsp;=\u0026thinsp;36.56) compared to the control group, which followed the traditional method (M\u0026thinsp;=\u0026thinsp;17.60). This statistically significant improvement (p\u0026thinsp;=\u0026thinsp;.000) highlights the effectiveness of ChatGPT-assisted learning in enhancing students' speaking skills in all key components, leading to the rejection of the null hypothesis (Ho2). These findings align with the previous literature, which supports the idea that language proficiency is enhanced through AI chatbots compared to traditional methods (Bekou et al., 2024)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eComparison of Monologue and its Key Components Scores between Experimental and Control Groups in the Post-Test\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eItems\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGroups\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eM\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eSD\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eDf\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonologue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExperimental group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003econtrol group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExperimental group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003econtrol group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.746\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAccuracy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExperimental group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003econtrol group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.690\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFluency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExperimental group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003econtrol group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.690\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInteraction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExperimental group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003econtrol group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoherence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExperimental group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.637\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001 (highly significant)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAs shown in Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the overall monologue performance, the experimental group (M\u0026thinsp;=\u0026thinsp;18.00) significantly outperformed the control group (M\u0026thinsp;=\u0026thinsp;8.56), with a p-value of .000, demonstrating the effectiveness of ChatGPT-based learning in enhancing monologue delivery. The experimental group showed improvements in all five constructs: range, with greater lexical diversity; accuracy, with fewer grammatical errors; fluency, with smoother speech and fewer pauses; interaction, with improved engagement and non-verbal cues; and coherence, with better organization and logical flow of ideas. These results support previous studies indicating that ChatGPT can effectively improve students' speaking abilities, including vocabulary, grammar, fluency, interaction, and coherence (Mohammad Ali, 2023; Nazeer et al., 2024; Nguyen Thi Thu, 2023; Wei-Xun \u0026amp; Jia-Ying, 2024).\u003c/p\u003e \u003cp\u003eLinguistic range in the monologue, the experimental group (M\u0026thinsp;=\u0026thinsp;3.60) significantly outperformed the control group (M\u0026thinsp;=\u0026thinsp;1.84), with a p-value of. 000, reflecting better lexical variety. These are similar with the findings of Ngo (2024), who stated that using ChatGPT for vocabulary acquisition offers key advantages. It creates personalized and interactive learning experiences that cater to individual needs, enabling learners to expand their vocabulary and improve their language performance more effectively.\u003c/p\u003e \u003cp\u003eFor grammatical accuracy in monologue, the findings indicated that students who engaged with ChatGPT demonstrated improved grammatical accuracy, with fewer errors in sentence structure, verb tense. the experimental group (M\u0026thinsp;=\u0026thinsp;3.60) significantly outperformed the control group (M\u0026thinsp;=\u0026thinsp;1.68), with a p-value of. 000, reflecting more accurate speech. These findings are consistent with the research of Balcı (2024) and Boudouaia et al. (2024).\u003c/p\u003e \u003cp\u003eFor monologue fluency, the study showed that ChatGPT-based interaction enhanced students\u0026rsquo; fluency, allowing them to speak more smoothly and with fewer hesitations. The experimental group (M\u0026thinsp;=\u0026thinsp;3.60) significantly outperformed the control group (M\u0026thinsp;=\u0026thinsp;1.68), with a p-value of.000, reflecting smoother and fluent speech without hesitation. These results are consistent with the studies by Nazeer et al. (2024) and Wei-Xun \u0026amp; Jia-Ying (2024)\u003c/p\u003e \u003cp\u003eFor monologue interaction, the findings showed that the experimental group (M\u0026thinsp;=\u0026thinsp;3.60) significantly outperformed the control group (M\u0026thinsp;=\u0026thinsp;1.64), with a p-value of .000, indicating enhanced engagement in speech. This improvement not only reflected in verbal interaction but also in the use of non-verbal cues, such as body language and facial expressions, which contributed to more dynamic and effective communication. These are similar to the findings of (Hayashi \u0026amp; Sato, 2024).\u003c/p\u003e \u003cp\u003eLastly, regarding coherence in monologue, the findings indicated that the experimental group (M\u0026thinsp;=\u0026thinsp;3.60) demonstrated better coherence than the control group (M\u0026thinsp;=\u0026thinsp;1.64), with a p-value of .000, indicating stronger logical flow in speech. These findings are consistent with those of Boudouaia et al. (2024) who found that the experimental group outperformed the control group in task completion, coherence, and cohesion.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eComparison of Dialogue and its Key Components Mean Scores in the Post-Test between Experimental and Control Groups\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGroups\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eM\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eSD\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eDf\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverall Dialogue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExperimental group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.466\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12.325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003econtrol group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExperimental group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.556\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003econtrol group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.645\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAccuracy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExperimental group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.556\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11.720\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003econtrol group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.577\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFluency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExperimental group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.556\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11.720\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003econtrol group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.577\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInteraction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExperimental group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.525\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12.492\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003econtrol group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.624\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoherence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExperimental group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.556\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.645\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001 (highly significant)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAs shown in Table \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, the overall dialogue performance, the experimental group (M\u0026thinsp;=\u0026thinsp;18.60) significantly outperformed the control group (M\u0026thinsp;=\u0026thinsp;9.04), with a p-value of .000, demonstrating the effectiveness of ChatGPT-based learning in improving overall dialogue skills. This finding aligns with previous studies showing AI tools effectively enhance vocabulary retention and recall more effectively than traditional methods, reduce language errors and improve grammar proficiency, promote greater comfort and fluency in speech through real-time dialogue, organize coherence in both written and spoken language.(Mohammad Ali, 2023; Nazeer et al., 2024; Nguyen Thi Thu, 2023; Wei-Xun \u0026amp; Jia-Ying, 2024).\u003c/p\u003e \u003cp\u003eIn terms of linguistic range in dialogue, the current study revealed that ChatGPT-assisted learning sessions expanded students\u0026rsquo; lexical and grammatical variety. The experimental group (M\u0026thinsp;=\u0026thinsp;3.68) outperformed the control group (M\u0026thinsp;=\u0026thinsp;1.80), with a statistically significant difference (p\u0026thinsp;=\u0026thinsp;.000), showing better lexical variety. This finding is similar to Karataş et al. (2024) who stated the potential of Chat GPT in increasing vocabulary. Nazeer et al. (2024) and Song and Song (2023) explored that Chat GPT is very useful tool for improving vocabulary.\u003c/p\u003e \u003cp\u003eRegarding grammatical accuracy in dialogue, the findings showed that the students who engaged with ChatGPT demonstrated improved grammatical control. The experimental group scored higher (M\u0026thinsp;=\u0026thinsp;3.60) than the control group (M\u0026thinsp;=\u0026thinsp;1.80), with the difference being statistically significant (p\u0026thinsp;=\u0026thinsp;.000). This supports existing evidence from Nazeer et al. (2024) emphasizing AI's role in improving learners\u0026rsquo; grammar.\u003c/p\u003e \u003cp\u003eIn terms of fluency in dialogue, the experimental group achieved a mean score of (M\u0026thinsp;=\u0026thinsp;3.60), significantly outperforming the control group (M\u0026thinsp;=\u0026thinsp;1.80), with a p-value of .000. This suggests that ChatGPT-based dialogue practice led to more spontaneous and fluid speech, resonating with prior findings that AI tools promote fluency.(Nazeer et al., 2024)\u003c/p\u003e \u003cp\u003eAs for interaction in dialogue, the study showed notable improvement in students' conversational engagement through ChatGPT sessions. The experimental group (M\u0026thinsp;=\u0026thinsp;3.88) scored significantly higher than the control group (M\u0026thinsp;=\u0026thinsp;1.84), with a p-value of. 000.indicating better engagement. This aligned with Nazeer et al. (2024) and Hayashi and Sato (2024) studies who stated Chat GPT has positive effects in improving conversational skills.\u003c/p\u003e \u003cp\u003eLastly, in terms of coherence in dialogue, the results indicated a clear improvement in the logical flow and organization of students\u0026rsquo; speech. The experimental group scored (M\u0026thinsp;=\u0026thinsp;3.68), while the control group scored (M\u0026thinsp;=\u0026thinsp;1.80), with the difference being statistically significant (p\u0026thinsp;=\u0026thinsp;.000). This finding underscores the effectiveness of ChatGPT in enhancing coherence, aligning with Song and Song (2023) who found that integrating ChatGPT into writing courses significantly improved students\u0026rsquo; ability to produce coherent texts, alongside gains in vocabulary, grammar, and organization.\u003c/p\u003e \u003cp\u003eThese findings align with Vygotsky\u0026rsquo;s Sociocultural Theory: the use of ChatGPT provided the type of social and instructional support learners need to grow. Its real-time interaction and feedback mimicked the role of knowledgeable others, effectively helping students develop strong oral communication skills. Recent evidence, including Guan et al. (2024), confirms that AI tools can effectively mediate language learning. The ChatGPT-assisted module improved students\u0026rsquo; linguistic range, grammar, fluency, interaction, and coherence, while also boosting confidence, motivation, and engagement within the learners\u0026rsquo; Zone of Proximal Development.\u003c/p\u003e \u003cp\u003eThe findings have important implications for educators, curriculum developers, and policymakers, suggesting the need for AI-driven interventions in language teaching and a shift towards more interactive and technology-supported educational practices.\u003c/p\u003e \u003cp\u003eWhile this study provides valuable insights, it was conducted with a limited sample of 50 female students from a public school in an urban area, which may limit the generalizability of the findings. Future research should involve a larger and more diverse sample to validate and expand upon these results. Additionally, further studies should explore the long-term effectiveness of AI-based interventions and examine the potential for integrating other interactive learning technologies to further enhance language skills development.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe study examined the effectiveness of the ChatGPT-assisted learning module in enhancing English-speaking skills among secondary school students. The experimental group showed significant improvements across all speaking competencies, suggesting the rejection of the null hypothesis (H₀₁) and confirming the effectiveness of the ChatGPT-assisted learning module. Descriptive analysis further showed that the majority of students progressed from basic (A1-A2) to the independent user level (B1-B2). Noticeable gains were observed in both monologue and dialogue tasks, particularly across the five core CEFR-aligned subcomponents: range, accuracy, fluency, interaction, and coherence. These findings demonstrate that the ChatGPT-assisted learning module was more effective than traditional methods in improving students\u0026rsquo; spoken English skills.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAI \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Artificial Intelligence\u003c/p\u003e\n\u003cp\u003eESPT \u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;English speaking proficiency test\u003c/p\u003e\n\u003cp\u003eCEFR \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Common European Framework of Reference\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics Statement: This study was approved by the Institutional Review Board (IRB) of Superior University, Lahore. All procedures involving human participants were conducted in accordance with institutional ethical standards. Written informed consent was obtained from all students, and formal permission was granted by the school administration prior to data collection.\u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting Interests\u003c/h2\u003e \u003cp\u003eThe author declares no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eSN conceptualized and designed the study, collected and analyzed the data, interpreted the results, and wrote the manuscript. The author read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe author sincerely thanks Dr. Muhammad Sarwar for his invaluable supervision and guidance throughout this research. Special appreciation is extended to Dr. Hisham ul Hasan Khawaja for reviewing each section of the article and providing critical insights, particularly in designing the test activities; to Dr. Farhana for her assistance in developing the ChatGPT-assisted learning module; to Dr. Mehmood ul Hasan for support in the data analysis process; and to Ms. Shazia for her help with the literature review. Gratitude is also due to the students and the head of the institute for facilitating data collection.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated and analyzed during the current study are not publicly available due to institutional restrictions but are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAbbas, N., Ashiq, U., \u0026amp; Abrar ul haq, M. (2018). 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ChatGPT Integration in EFL Education: A Path to Enhanced Speaking Self-Efficacy. \u003cem\u003eNovitas-ROYAL (Research on Youth and Language)\u003c/em\u003e,\u003cem\u003e\u0026nbsp;18\u003c/em\u003e(2), 167-182.\u0026nbsp;\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-8331993/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8331993/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe integration of Artificial Intelligence (AI) into education is expanding rapidly, particularly in the area of language learning. This study examined the effectiveness of Chat GPT assisted learning module in enhancing speaking skills (in terms of range, accuracy, fluency, interaction and coherence) at secondary level. True-experimental design was employed, included fifty female students of 9th grade from a public school who were randomly assigned to experimental and control group. Speaking proficiency was evaluated with the support of ChatGPT, using the Common European Framework of Reference scale. Independent t- test was employed to compare the effectiveness of both methods. The findings show that the experimental group outperformed the control group in both monologue and dialogue tasks, with notable improvements in broad lexical range, grammatical accuracy, fluent speech, interactive engagement, and coherence. These results underscore the potential of AI-driven tools like ChatGPT to improve speaking skills and help bridge the gap between existing speaking proficiency levels and international language standards.\u003c/p\u003e","manuscriptTitle":"Exploring the integration of artificial intelligence in enhancing English speaking skills: an experimental study at Secondary school level in Punjab, Pakistan","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-15 08:10:58","doi":"10.21203/rs.3.rs-8331993/v1","editorialEvents":[{"type":"communityComments","content":1}],"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":"6e59f201-a32d-4d1a-b2b7-7eb9218f49fe","owner":[],"postedDate":"December 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-15T15:09:51+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-15 08:10:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8331993","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8331993","identity":"rs-8331993","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}