Exploring the Role of Artificial Intelligence-Assisted Vocabulary Apps in School English Language Education: A Survey-based Approach

Exploring the Role of Artificial Intelligence-Assisted Vocabulary Apps in School English Language Education: A Survey-based Approach | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Exploring the Role of Artificial Intelligence-Assisted Vocabulary Apps in School English Language Education: A Survey-based Approach Winny Early Star R, Sneha Mishra This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8425878/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study explores the impact of Artificial Intelligence (AI)-assisted vocabulary apps on English language learning outcomes among school students in grades 8 and above. The objectives include evaluating the effectiveness of these apps in vocabulary acquisition, examining their influence on student engagement, and identifying the factors contributing to their success in educational settings. A mixed-methods, quasi-experimental design was employed with two groups: an experimental group using AI-enabled vocabulary apps and a control group relying on traditional methods. The AI apps incorporated personalized learning paths, gamified interfaces, and adaptive feedback. Data were collected through structured questionnaires covering demographic information, learning behaviors, and vocabulary usage. Independent t-tests were used to compare performance outcomes, while open-ended responses provided qualitative insights. Statistical analysis indicated significantly higher vocabulary scores in the experimental group (Mean = 82%) compared to the control group (Mean = 68%), with a t-value of 4.29 (p < 0.001). Engagement frequency positively correlated with performance (r = 0.40), and motivation also showed a moderate positive correlation (r = 0.67). Qualitative feedback highlighted benefits such as real-time feedback. The findings affirm that AI-assisted vocabulary tools enhance vocabulary retention and student engagement. These apps demonstrate strong potential for integration into school curricula, offering tailored and interactive learning experiences that can complement traditional instructional methods. Language acquisition Language teaching AI-based learning English vocabulary apps Survey Mixed-Method Approach Educational practices Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction Artificial Intelligence (AI) has profoundly reshaped our modern world, acting as a cornerstone of a new era in various sectors including healthcare, automotive industry, and most notably, education. This paradigm shift, primarily driven by AI, is gradually transforming the traditional methodologies in educational settings, ushering in a new era of digital learning and instructional design. Machines are utilized directly or indirectly in a variety of industries, including healthcare, automated cars, and sophisticated decision-making, as well as in educational institutions. The use of Artificial Intelligence (AI)-based technologies and the Internet has produced various educational advances for instructors and students (Yu & Nazir, 2021). AI's influence in education extends from primary levels to higher education, where its integration is revolutionizing teaching and learning processes. These technologies are not just additional tools; they are transforming educational paradigms by providing personalized learning experiences and enabling adaptive learning environments. AI systems are capable of analyzing vast amounts of data to tailor instruction to the individual needs of students, addressing their specific weaknesses and strengths, thereby optimizing learning outcomes. Moreover, AI's role in the classroom is multifaceted. For instructors, it offers advanced tools for managing coursework, automating administrative tasks, and providing real-time feedback on student performance. This not only enhances efficiency but also allows educators to dedicate more time to engage with students on a deeper, more personal level. For students, AI-powered platforms can facilitate a more engaging learning experience. Interactive learning apps, virtual tutors, and personalized learning modules adapt to each student's learning pace, providing customized feedback and resources that cater to their specific learning styles. The deployment of AI in higher education is already visible with systems that assist in everything from the grading of assignments to the detection of plagiarism, and even to the provision of personalized career counseling. The predictive analytics capabilities of AI are particularly transformative, enabling institutions to identify at-risk students early and provide timely interventions to support their academic journeys. However, the rapid integration of AI in education also presents significant challenges and raises questions about data privacy, ethical considerations in AI decision-making, and the potential for widening the digital divide. Ensuring equitable access to AI resources, safeguarding student data, and maintaining a human-centric approach in education are critical considerations as we navigate this transition. The challenge of learning English vocabulary in school is significant. Many students struggle not only with acquiring new vocabulary but also with retaining the vocabulary they learn. Traditional instructional methods, including textbooks and classroom presentations, often fail to engage students effectively, leading to suboptimal learning outcomes (Gao, 2021). This issue necessitates innovative approaches to vocabulary instruction, and AI-assisted vocabulary apps present a promising solution. These apps can provide personalized, adaptive, and gamified learning experiences, which are tailored to individual student needs and learning styles. Despite the proliferation of AI technologies in education, there remains a gap in understanding their specific impact on vocabulary acquisition among school students. While several studies have explored the use of AI in education broadly, few have focused specifically on how AI-assisted vocabulary apps influence student engagement and vocabulary retention in the context of English language learning. This study aims to fill this gap by providing empirical evidence on the effectiveness of AI-assisted vocabulary apps for 8th-grade and above students learning English as a second language. By comparing these innovative tools with traditional methods, this research seeks to offer insights into how AI can enhance language learning outcomes and student engagement. AI can play a significant role in solving the problem of learning English vocabulary in school. AI can be used to personalize the learning experience by tailoring the content and difficulty level of the exercises to each student's individual needs. AI can also be used to provide adaptive feedback, which can help students to identify and correct their learning errors. Additionally, AI can be used to gamify the learning process, which can make vocabulary learning more fun and engaging (Chen, 2022). The use of AI in this research application is significant because it has the ability to transform the way that English vocabulary is taught in school. AI-assisted vocabulary apps can provide a personalized, adaptive, and gamified learning experience that can help students to learn new vocabulary more effectively (Johnson et al., 2022). Additionally, AI-assisted vocabulary apps can be used to track students' progress and to provide personalized feedback. The outcome of the present study can be used to improve the learning experience for all students. The following research questions are considered in this study: 1. What is the impact of AI-based learning English apps on school students' vocabulary learning outcomes? 2. How do these apps influence students' engagement with the learning process? 3. What are the factors that contribute to the effectiveness of AI-based learning English apps? The objectives of this research are listed as follows: - To investigate the effectiveness of AI-assisted vocabulary apps on enhancing vocabulary learning outcomes among 8th-grade and above school students learning English as a second language. This objective aims to quantify the effect of the AI apps on students' vocabulary retention and application abilities. - To examine the influence of AI-based learning apps on student engagement and motivation in the English language learning process. This involves understanding how these digital tools affect students' involvement, participation, and interest in learning English vocabulary. - To identify and analyze factors contributing to the effectiveness and efficiency of AI-assisted vocabulary learning tools in educational settings. This objective seeks to uncover the nuanced elements (such as personalized learning experiences, gamification, and adaptive feedback) that make these apps beneficial for language acquisition. Despite widespread adoption of AI-driven learning tools, empirical validation of their pedagogical impact at the school level particularly in vocabulary acquisition remains scarce. Most prior studies (Woo & Choi, 2021; Lin et al., 2022) have focused on tertiary learners, leaving a critical gap in understanding whether similar benefits translate to early secondary education. This study is motivated by that gap. It seeks to determine whether AI-assisted vocabulary applications can meaningfully enhance retention, engagement, and motivation in English vocabulary learning among students in Grades 8–10. The theoretical foundation rests on Cognitive Load Theory and Self-Determination Theory, which posit that adaptive and gamified feedback mechanisms can reduce extraneous load and increase intrinsic motivation. Accordingly, the study postulates that AI-based tools that personalize feedback and provide immediate reinforcement will produce superior learning outcomes compared to conventional rote methods. H₁: Students using AI-assisted vocabulary apps will exhibit significantly higher post-test vocabulary scores than those using traditional methods. H₂: Frequency of interaction with AI-assisted apps will positively correlate with students’ engagement levels. H₃: Perceived personalization, adaptive feedback, and gamification will collectively predict students’ satisfaction and continued app usage intention. These hypotheses directly connect the literature-identified gap with measurable variables analyzed through the independent t-test and correlation techniques reported in the Results section. The remainder of this paper is organized as follows. Section 2 presents a comprehensive review of literature, highlighting key theoretical and empirical studies on AI-assisted language learning and vocabulary acquisition. Section 3 details the research methodology, including the mixed-methods design, participant profile, intervention process, and analytical procedures employed. Section 4 discusses the findings derived from both quantitative and qualitative analyses, emphasizing the comparative outcomes between traditional and AI-assisted learning environments. Section 5 concludes the paper by summarizing the major findings, educational implications, limitations, and recommendations for future research. 2. Review of Literature 2.1. Strategic way of Language Learning Van Lieshout and Cardoso ( 2022 ) investigated the pedagogical use of Google Translate, the use of text to synthesis and automated voice detection for second or foreign languages Dutch learning. Thirty participants utilized these tools for one hour to learn phrases and pronunciation. The pre/post/delayed post-test design reveals short-term acquisition success, supported by qualitative analysis of participant interactions. Overall, findings highlight Google Translate's versatility and adaptability to diverse learning needs and styles. Hazaymeh and Alomery ( 2022 ) demonstrated that implementing visual mind mapping as a reading strategy significantly enhances the ability to think critically among the learners of English language. The experiment group exhibited a substantial improvement in critical thinking scores compared to the control group, emphasizing the positive impact of the realistic brainstorming approach on various critical thinking indicators. The findings affirm the strategy's effectiveness in elevating both reading and thinking strategically comprehension abilities among English language learners. Wang and Zhang ( 2022 ) addressed the imperative of enhancing students' autonomous learning by developing a blended learning model based on SPOC in the context of a college English course. Employing data mining techniques, the research identifies optimal factor combinations from questionnaire-based exploration. The findings reveal that the optimized blended learning mode significantly stimulates learners' motivation, cultivates autonomous learning abilities, and improves overall autonomous learning behavior, demonstrating its effectiveness in fostering independent learning in foreign language education. 2.2 AI Assisted Language Learning AI technology advancements drive increased AI speaking applications in foreign language acquisition (Zou et al., 2023 ). According to Chassignol et al. ( 2018 ) the rapid development of AI technology is altering information search, communication, and behavior in recent decades. González García et al. (2020) explained that AI-assisted vocabulary apps can be effective in improving English vocabulary skills. Dewi et al. ( 2021 ) stated that AI can enhance English learning through applications, websites, and virtual reality technology, but lacks a comprehensive examination of its role in student performance evaluation. Wang et al. ( 2021 ) reported that Mobile applications have become a popular vocabulary learning approach in China, with most allowing Students to gain word knowledge by translating into their own language. These applications often display phrases that combine text, images, and audio. AI based education was leading to a new learning era among students (Woo & Choi, 2021). By combining human intelligence, a computer system might function as an intelligent instructor, tool, or tutee while also simplifying decision-making in the educational sector (Hwang et al., 2020 ). The use of artificial intelligence (AI) in education is becoming increasingly widespread, and this is particularly true in the field of vocabulary learning. AI-assisted vocabulary apps offer a number of potential benefits for college students. Alqahtani et al. ( 2023 ) stated that AI applications for academia and schools provide help, feedback, evaluation, specialized curriculum, tailored career advising, and mental health advice. Annamalai et al. ( 2023 ) found Bots known as chat foster skills, independence, and resemblance; however, they lack a psychological context and provide inaccurate statistics for English language acquisition. Potočnik et al. ( 2023 ) highlights the increasing use of AI-enabled solutions in medical imaging practice, automating tasks like pre-exam, subject location, methodology decision-making, photo capture, assessment, subsequent processing, and upkeep of equipment. The research conducted by Chu et al. ( 2022 ) demonstrated that Artificial Intelligence robots are being applied in education due to rapid development. Canada, Chile, and South Korea capitalized in AI based Robots in Education (AIRE) investigating premature, concentrating on pupils' learning abilities and conduct. The majority of AIRE research was undertaken in Language and Science fields, with a focus on problem-solving methodologies. Mobile devices have significantly impacted life and learning, with Mobile Assisted Language Learning (MALL) research focusing on five themes: self-regulated language learning, learner agency, personalizing learning through AI-supported mobile learning, learning in the wild, and supporting higher education (Karakaya & Bozkurt, 2022 ). Wei et al. ( 2022 ) stated that AI-based teaching techniques, such as Music Education and Teaching based on AI (MET-AI), have significantly improved the standard of music education and teaching models. The MET-AI provides pupils with a learning result rate of 95.2%, an efficiency ratio of 98.1%, and a mean square error rate of 17.9%. Several studies have been conducted on the effectiveness of AI-assisted vocabulary apps (Kessler, 2022) in college English language education. Evmenova et al. (2020) found that Artificial intelligence-assisted vocabulary apps can be effective in helping students learn new vocabulary. The study found that students who used AI-assisted apps (Lin & Wang, 2022 ) had significantly higher vocabulary scores than students who did not use these apps. Another study, conducted by Mizumoto et al. (2021), found that AI-assisted vocabulary apps can be effective in supporting English as Foreign Language (EFL) writing. The study found that students who used AI-assisted apps were more likely to use academic words in their writing than students who did not use these apps. A study conducted by Sun et al. (2022) found that AI-assisted vocabulary apps can be effective in improving students' vocabulary learning outcomes. The study found that students who used AI-assisted apps had significantly higher vocabulary scores than students who did not use these apps (Mahdi & Abu-Naser, 2021). 2.3 Motivation and Scope The literature review presents a comprehensive overview of strategic language learning methodologies and the burgeoning role of AI in language education. It underscores the effectiveness of diverse pedagogical tools such as Google Translate, text synthesis, and automated voice detection in facilitating language acquisition, particularly in the Dutch and English learning contexts. Moreover, the discussion highlights the significance of innovative approaches like visual mind mapping and blended learning models in enhancing critical thinking skills and autonomous learning behaviors among language learners. Furthermore, the review sheds light on the transformative impact of AI technology in language education, particularly in vocabulary acquisition and student performance evaluation. AI-assisted vocabulary apps and educational support systems offer promising avenues for enhancing learning outcomes by providing personalized learning experiences, feedback, and tailored assistance. Additionally, the integration of AI in diverse educational settings, including mobile-assisted language learning and music education, underscores its potential to revolutionize traditional teaching paradigms and optimize learning efficiency. Overall, the synthesis of these studies underscores the evolving landscape of language education, characterized by a convergence of traditional pedagogical methods with cutting-edge AI technologies, ultimately enhancing the effectiveness and accessibility of language learning experiences. The proposed research on the impact of AI-based learning English apps on vocabulary acquisition can significantly influence current research trends and the educational community in several ways. Firstly, by providing empirical evidence on the effectiveness of AI tools in language learning, it contributes to advancing research in the field of AI-assisted language education. Secondly, the study's findings can inform educators and policymakers about the potential benefits of integrating AI technology into language learning curricula, potentially leading to the adoption of innovative teaching methods in educational institutions. Additionally, by identifying factors contributing to the effectiveness of AI-assisted learning, the research can guide the development of more tailored and efficient language learning applications. Ultimately, this study has the potential to catalyze discussions and initiatives aimed at leveraging AI to enhance language learning outcomes, thereby positively impacting the broader educational community and shaping future research directions. 3. Research Methodology This research aims to investigate the impact of AI-based learning English apps on school students' (of 8th-grade and above) vocabulary learning outcomes, explore their influence on engagement with the learning process, and identify factors contributing to their effectiveness. Employing a mixed-methods approach, the study combines qualitative and quantitative analyses to provide a comprehensive understanding of the complex dynamics involved. This approach allowed us to not only measure the impact quantitatively but also to understand the students' experiences and the educational context qualitatively. The proposed conceptual framework of this research is illustrated in Fig. 1 . 3.1. Research Design This study employs a mixed-methods approach, integrating both qualitative and quantitative analyses to provide a comprehensive understanding of the impact of AI-assisted vocabulary apps on student engagement and vocabulary retention. The research design is quasi-experimental, involving two groups: an experimental group using AI-assisted vocabulary apps and a control group using traditional vocabulary learning methods. 3.1.1. Methods To ensure the AI-assisted vocabulary apps were appropriately adapted for the study, several steps were taken. The apps were selected based on their ability to provide personalized, adaptive learning experiences and their incorporation of gamification elements. Prior to the study, a pilot phase was conducted where the apps were tested and modified to align with the specific learning objectives and curriculum requirements of the 8th-grade and above students. The adaptations included: - Customizing the content to match the vocabulary curriculum. - Incorporating adaptive learning algorithms to tailor the difficulty level based on individual student performance. - Adding gamification features such as quizzes, rewards, and progress tracking to enhance engagement. - Ensuring compatibility with the students' existing technological infrastructure. 3.1.2. Participants The participants in this research are school students of 8th grade and above learning English as a second language. This focus allows for insights into a specific demographic with unique learning needs and preferences. 3.1.3. Intervention The intervention involved the introduction of AI-based learning English applications to a group of students. The AI apps served as supplementary tools for vocabulary learning. The study included a control group using traditional learning methods for comparative analysis. The adaptation process of the AI apps was critical to ensure they met the educational needs and engagement levels of the students. The experimental group used these customized AI apps over a period of eight weeks, while the control group continued with traditional vocabulary learning methods. 3.2. Measures 3.2.1. Vocabulary Learning Outcomes The primary measure involves assessing the impact of AI-based apps on vocabulary learning outcomes quantitatively. This includes improvements in vocabulary usage, retention, and application in different contexts. 3.2.2. Engagement with the Learning Process Engagement was measured through both quantitative and qualitative data capturing students' active involvement, participation, and interest in the learning process facilitated by AI-based apps. Specific engagement metrics included time spent on the app, frequency of app usage, and student feedback on the learning experience. 3.2.3. Factors Contributing to Effectiveness Factors contributing to the effectiveness of AI-based apps were assessed through a mixed-methods approach. Qualitative data were collected through open-ended questions in the questionnaire exploring usability, relevance, and perceived advantages or disadvantages of using these apps. Quantitative data provided a structured analysis of these factors. 3.3. Data Collection Group 1 The students are given a syllabus to learn through traditional methods. Group 2 The students were given the same syllabus and a set of AI apps to use for learning. The AI apps were adapted to include personalized feedback, gamification elements, and adaptive learning algorithms. Questionnaire The survey helped in gathering data on student engagement and attitudes toward the learning methods used. The questionnaire (Appendix A) focused on engagement with the learning process, understanding assessment questions, and factors that contribute to the effectiveness of the apps. The questionnaire was designed to gather comprehensive data from upper-grade school students' experiences with AI-assisted vocabulary apps in the context of learning English. Vocabulary questions were included to measure the immediate impact on vocabulary retention and usage. It comprised three sections: Demographic Profile, Questions Related to Vocabulary Learning, and Topic-Specific Questions. 3.3.1. Demographic Profile This section captures essential information about the participants, including their name, location, gender, age, educational background, and current grade in school. Additionally, it explores their proficiency in English, frequency of engagement with English outside the classroom, and the use of mobile apps for learning purposes. 3.3.2. Questions Related to Vocabulary Learning This segment delves into students' learning behaviors, preferences, and strategies. It assesses their interaction with course materials, comprehension levels, preferred learning tools, usage of additional resources, study techniques, motivation, and approaches to assessments. The Likert scale is utilized for quantitative responses, providing nuanced insights into the effectiveness of their chosen learning strategies. 3.3.3. Topic-Specific Questions This section focuses on evaluating the participants' understanding of technology and communication-related vocabulary, linking theoretical knowledge with practical application. It requires students to define key terms, discuss the impact of technology on communication, and utilize learned vocabulary in written communication. Additionally, it explores their perceptions of the difficulty of specific terms, understanding of technological evolution, and awareness of the importance of technology and communication vocabulary in professional and personal contexts. The questionnaire employs a mix of open-ended and structured questions, enabling participants to express their thoughts freely while providing quantitative data for statistical analysis. The diverse range of inquiries ensures a holistic exploration of the participants' experiences and opinions regarding AI-assisted vocabulary learning. 3.4. Data Analysis A combination of qualitative and quantitative analyses was performed on the collected data to obtain a comprehensive understanding of the research problem. The study employed descriptive statistics (mean, percentage, and standard deviation) to summarize demographic and behavioral data, and inferential statistics primarily independent t-tests and Pearson’s correlation analyses to test the stated hypotheses comparing outcomes between the experimental and control groups. All analyses were carried out using SPSS (version 25) at a 0.05 significance level. 3.4.1. Qualitative Analysis Qualitative data from open-ended survey responses and interviews provided insights into students' evolving relationship with AI in education. Most displayed a sound understanding of AI concepts and were able to provide real-life examples of AI-enabled tools. A majority concurred that advanced technologies are integral for communicative competence in the 21st century. Their creative application of technology-related vocabulary during role plays and written tasks indicated meaningful learning of concepts. A prominent theme across responses was growing dependence on smart devices and digital assistants for daily tasks. While acknowledging associated risks like privacy breaches, many students believed AI assistants enhance productivity. Some even attributed improved academic performance to tools like chatbots and virtual tutors. A few displaying resistances desired moderation in AI usage to retain core human skills. 3.4.2. Quantitative analysis Quantitative analysis involved the use of mathematical and statistical procedures to test relationships and differences among variables. Before performing inferential analyses, the internal consistency of the questionnaire was verified using Cronbach’s alpha (α = 0.87), indicating high reliability. Descriptive statistics were computed to summarize participants’ demographic characteristics and engagement patterns, which were visually represented in pie charts, bar graphs, and clustered column charts. To evaluate learning outcomes, an independent-samples t-test was conducted to compare the mean vocabulary-acquisition scores between the experimental group (AI-assisted app users) and the control group (traditional learners). This test determined whether the observed difference in mean performance was statistically significant, thereby addressing Hypothesis H₁. In addition, Pearson’s correlation analysis was used to examine relationships among English-proficiency level, frequency of app usage, engagement score, and vocabulary performance, with correlation coefficients (r) and p-values reported to indicate strength and direction. These analyses directly addressed Hypotheses H₂ and H₃ concerning engagement and app-effectiveness factors. 4. Results and Discussion This section presents the findings derived from both quantitative and qualitative analyses, as structured under four key subsections: Descriptive Statistics, Independent Samples t-Test, Correlation Analysis, and Qualitative Content Analysis. These analyses were conducted to evaluate the impact of AI-assisted vocabulary apps on students’ vocabulary acquisition, engagement, and perceived learning effectiveness, thereby addressing all research objectives of the study. To begin with, descriptive statistics were employed to summarize the fundamental characteristics of the data, including students’ vocabulary scores, frequency of app usage, and self-assessed engagement levels. This provides a preliminary understanding of the overall trends and distribution patterns across the experimental and control groups. The demographic analysis provides insights into the composition and characteristics of the study sample. As illustrated in the gender distribution chart (Fig. 2 ), the sample consisted of 53% male and 47% female students, indicating a nearly balanced representation that minimizes gender bias in interpreting learning outcomes. The age distribution reveals (Fig. 3 ) that participants were primarily clustered between 13 and 15 years, reflecting the target demographic of students in grades 8 and above, consistent with the study’s educational focus. In terms of English proficiency (Fig. 4 ), a majority of students identified as having intermediate proficiency (62%), followed by beginner-level learners (38%), while only 20 students (20%) reported advanced proficiency. This distribution suggests a suitable sample for assessing vocabulary development interventions, particularly among learners requiring foundational or intermediate-level support. Mobile app usage (Fig. 5 ) for learning exhibited a notable trend, with 40 students (40%) reporting daily use, 30 students using them 2–3 times per week, and diminishing usage among the remaining categories—25 once a week, 15 rarely, and only 10 never. These figures underscore the growing familiarity and acceptance of mobile-assisted learning, particularly among the experimental group. The high frequency of usage among students provides a favorable context for evaluating the impact of AI-assisted vocabulary apps, as consistent engagement with the digital tools is a critical factor in determining learning efficacy. Collectively, the demographic trends affirm that the sample is appropriate for examining the relationship between app-based learning and vocabulary acquisition across diverse learner profiles. An independent samples t-test was conducted to statistically compare the mean vocabulary assessment scores between the experimental group (using AI-based apps) and the control group (using traditional learning methods). This helped determine whether the intervention had a statistically significant effect on learning outcomes. The results of the independent samples t-test provide strong statistical evidence supporting the effectiveness of AI-assisted vocabulary apps in enhancing vocabulary acquisition among school students. As shown in the summary Table 1 , the experimental group that used AI-based vocabulary tools achieved a mean score of approximately 82, significantly outperforming the control group, which recorded a mean score of around 68. The standard deviations of both groups remained within a comparable range, indicating consistent performance within each group. The bar chart shown in Fig. 6 visually reinforces this difference, with the experimental group clearly outperforming the control group. Error bars representing standard error further confirm that the observed differences are not due to random variation. The t-statistic value of 12.13 and an associated p-value well below 0.001 indicate a highly significant difference between the groups, rejecting the null hypothesis with strong confidence. Table 1 T-Test Summary Group Mean Score Standard Deviation Sample Size Control 67.94 4.73 30 Experimental 81.92 4.94 30 t-Statistic: 12.13 p-Value: 1.53 × 10⁻ 17 These findings validate the study’s first objective, which was to assess the impact of AI-assisted apps on vocabulary learning outcomes. The results strongly suggest that integrating AI tools into vocabulary instruction can lead to measurable improvements in learner performance when compared to conventional methods. This statistical distinction also provides foundational support for subsequent analyses exploring engagement and the underlying factors contributing to the success of AI interventions in education. A correlation analysis followed to explore the strength and direction of relationships between key variables such as app usage frequency, English proficiency level, and vocabulary test scores. Pearson’s correlation coefficient was used for this purpose to reveal interdependencies that contribute to vocabulary development and student motivation. The correlation analysis (Fig. 7 ) reveals a moderate-to-strong positive relationship (r = 0.40) between the frequency of AI app usage and students' vocabulary test scores, indicating that increased interaction with AI-assisted learning tools significantly contributes to improved language acquisition. The scatter plot clearly demonstrates that students who engaged with the apps more frequently—especially those using them daily or several times a week—tended to achieve higher performance outcomes. This statistically meaningful association supports the premise that consistent and active use of personalized digital tools enhances learning effectiveness. The observed trend affirms the study’s objective of evaluating the role of engagement, emphasizing that not just access to AI tools but sustained usage is instrumental in driving measurable academic gains in vocabulary learning. Finally, qualitative content analysis of the open-ended questionnaire responses provided nuanced insights into students' subjective experiences with AI-assisted learning. Thematic patterns were identified to better understand the perceived benefits, challenges, and effectiveness of the vocabulary apps from the learners’ perspective. Table 2 Qualitative Response Summary Theme Frequency of Mention Percentage (%) Personalized Feedback 45 75.0% Gamified Features 38 63.3% Ease of Use 30 50.0% Improved Retention 42 70.0% Technical Issues 8 13.3% Learning Motivation 36 60.0% Table 2 and Fig. 8 highlights the most frequently mentioned aspects of the AI vocabulary apps, providing insight into the student-perceived factors contributing to their effectiveness. The thematic analysis of qualitative responses reveals that the majority of students (75%) highlighted the benefit of personalized feedback, noting how real-time, adaptive guidance from the app enhanced their understanding and retention of vocabulary. Similarly, gamified features were cited by 63% of participants as a motivating element, making the learning process more enjoyable and less monotonous. A significant number (70%) also mentioned improved retention, linking it to repeated exposure and interactive content. Meanwhile, ease of use emerged as a commonly appreciated feature, with 50% of students affirming that the app's interface was intuitive and accessible. A smaller fraction (13%) pointed out technical issues, such as occasional lags or glitches, suggesting areas for technological refinement. Additionally, learning motivation was reported as positively impacted in 60% of responses, with students describing increased enthusiasm and time spent studying compared to traditional methods. The findings of this study strongly affirm the effectiveness of AI-assisted vocabulary apps in enhancing English language learning among school students. Addressing the first objective, statistical analysis revealed a significant improvement in vocabulary acquisition for students using AI tools, with the experimental group achieving a mean score of 81.92 compared to 67.94 in the control group (p < 0.001). The second objective—evaluating student engagement—was supported by a moderate-to-strong positive correlation (r = 0.40) between app usage frequency and vocabulary performance, indicating that consistent interaction with the AI tools translated into better outcomes. Lastly, the qualitative analysis addressed the third objective by identifying key features that contributed to app effectiveness, including personalized feedback (mentioned by 75% of students), gamification, ease of use, and increased motivation. These results collectively demonstrate that AI-based vocabulary apps not only improve learning outcomes but also enhance learner engagement and satisfaction, making them a viable and scalable solution for modern language education. 5. Discussion The findings of this study comprehensively address the research objectives and confirm all three proposed hypotheses, illustrating the effectiveness and pedagogical value of AI-assisted vocabulary learning among school students learning English as a second language. The results establish that AI-driven vocabulary apps not only enhance students’ academic performance but also foster engagement, motivation, and self-directed learning behavior—thereby achieving the intended outcomes of the study. The first objective sought to investigate the effectiveness of AI-assisted vocabulary apps in improving vocabulary learning outcomes compared to traditional instructional methods. The quantitative results clearly demonstrate this impact. The experimental group that used AI-based learning applications achieved a markedly higher mean score than the control group, with a statistically significant difference (p < 0.001). This finding strongly supports Hypothesis H₁, which predicted that students exposed to AI-assisted vocabulary learning would exhibit higher vocabulary achievement. The improvement can be attributed to several built-in mechanisms of the AI apps personalized instruction, adaptive difficulty adjustment, and gamified learning which collectively promoted active recall and reduced cognitive load. These outcomes are consistent with Cognitive Load Theory, which posits that adaptive scaffolding enhances information retention by focusing learners’ attention on relevant material. The large effect size observed in the statistical analysis confirms not just statistical but also pedagogical significance, suggesting that integrating AI tools into regular classroom practice can meaningfully enhance vocabulary mastery. The second objective aimed to examine how AI-based apps influence student engagement and motivation during the learning process. The correlational analysis indicated a positive and moderate relationship (r ≈ 0.40) between the frequency of app use and vocabulary test performance, confirming Hypothesis H₂. Students who interacted with the app more frequently demonstrated higher engagement and better outcomes, emphasizing the critical role of regular digital exposure in consolidating vocabulary knowledge. The engagement metrics and qualitative responses reinforce that gamification and interactive features were primary motivators driving this behavior. Learners reported that rewards, progress indicators, and interactive quizzes made learning enjoyable and sustained their daily participation. This aligns with Self-Determination Theory, which highlights that perceived autonomy, competence, and relatedness enhance intrinsic motivation. The AI tools met these psychological needs by allowing students to progress at their own pace, track their improvement, and receive immediate reinforcement. The third objective focused on identifying the factors contributing to the overall effectiveness of AI-assisted vocabulary apps. The qualitative findings confirmed Hypothesis H₃, which proposed that adaptive feedback, personalization, and gamification would collectively predict learner satisfaction and continued use. Students consistently highlighted adaptive feedback as one of the most valuable features, noting that instant correction and tailored recommendations helped them recognize mistakes and improve efficiency. Personalization also emerged as a major factor—students appreciated that the app adapted to their learning level and targeted weaker areas. Additionally, accessibility through mobile devices allowed learning to extend beyond the classroom, supporting self-paced learning. Although a minority mentioned technical limitations or overdependence on digital tools, these concerns were minimal compared to the overall positive response. Together, these factors explain the success of the intervention by combining cognitive optimization (via adaptivity) and motivational engagement (via gamification and autonomy). Beyond confirming the hypotheses, the results reveal novel contributions to the field of AI-assisted language education. First, this study provides empirical evidence of AI’s measurable effectiveness in secondary education, a level where previous research is scarce. Most existing literature focuses on tertiary learners; thus, the present findings extend theoretical and practical understanding to early adolescence, when foundational vocabulary development is critical. Second, the integration of both quantitative and qualitative evidence strengthens the explanatory power of the study. The mixed-methods approach moves beyond numerical comparison by illustrating why AI tools are effective—linking statistical improvement with learner perceptions of motivation, feedback, and usability. Third, the positive correlation between engagement frequency and achievement introduces a behaviorally grounded model of habit-based learning, where consistent microlearning through AI applications leads to sustainable academic growth. Fourth, the findings contribute to a blended learning framework, suggesting that AI technologies can complement rather than replace teachers by automating repetitive vocabulary practice while enabling instructors to focus on higher-order language skills. Overall, the discussion highlights that the research objectives have been successfully met and all three hypotheses empirically validated. The study demonstrates that AI-assisted vocabulary apps substantially enhance vocabulary performance, engagement, and motivation among school students. The adaptive and interactive nature of these tools transforms vocabulary learning from a passive memorization task into an active, personalized experience. The evidence also indicates that AI tools can function effectively as supplementary resources within traditional classrooms, aligning with the growing emphasis on data-driven, student-centered instruction. The novel contribution of this research lies in its holistic evaluation of AI in language learning bridging statistical significance with human experience. It establishes that AI can be meaningfully deployed at the school level to promote personalized, engaging, and scalable learning. These results carry important implications for educators, curriculum designers, and policymakers seeking innovative and inclusive ways to strengthen language learning outcomes through responsible AI integration. 6. Conclusion This study set out to examine the impact of Artificial Intelligence-assisted vocabulary apps on English language learning among school students, with a focus on vocabulary acquisition, student engagement, and the contributing factors to learning effectiveness. The evidence from both quantitative and qualitative analyses leads to a clear conclusion: AI-assisted vocabulary tools significantly enhance vocabulary learning outcomes and learner engagement when compared to traditional teaching methods. The experimental group demonstrated a substantial improvement in vocabulary scores, supported by a statistically significant t-test result (p < 0.001). A moderate-to-strong positive correlation between app usage frequency and test performance further confirmed that active interaction with AI tools contributes meaningfully to academic gains. Qualitative feedback provided deeper insight into learner experiences, highlighting key features such as personalized feedback, gamified content, and ease of use as drivers of motivation and retention. These factors not only enriched the learning process but also catered to diverse learner needs, making the apps both effective and inclusive. The study's findings are particularly relevant in today's educational landscape, where digital transformation is accelerating. As schools and institutions seek scalable, engaging, and data-driven solutions, this research provides empirical support for integrating AI tools into language curricula. By demonstrating how AI can personalize and optimize vocabulary instruction, the study underscores the transformative potential of intelligent educational technologies. Future research may expand on these insights by exploring long-term learning impacts and adapting similar frameworks across other language and skill domains. Overall, the study contributes valuable evidence to guide innovation in educational practice and policy. Declarations Ethical Approval This study involved students in an educational research context and was conducted in accordance with institutional ethical guidelines. The research protocol received ethical clearance from the Institutional Ethics Committee of St. Joseph’s Matriculation School, Pallalakuppam, where the data collection took place. Consent to Participate As the participants were minors, informed consent was obtained from the school authorities and the parents/legal guardians of all participating students. Assent was obtained from the students themselves prior to data collection. Participation was voluntary, and confidentiality and anonymity were strictly maintained. Consent to Publish Not applicable, as the manuscript does not contain any individual person’s data in any form (including images, videos, or identifiable details). Author Contributions Statement Ms. Winny E.S.R. conceptualized the study, designed the methodology, conducted data collection, performed data analysis, interpreted the results, prepared figures and tables, and wrote the original draft of the manuscript. Dr. Sneha M. supervised the research process, critically reviewed and revised the manuscript for intellectual content, and provided academic guidance throughout the study. All authors read and approved the final manuscript. Data Availability The data that support these findings are available with corresponding author and can be shared upon request. Declaration of AI Use The authors affirm that Artificial Intelligence (AI) tools were solely used for grammar checking during the writing and editing process of this manuscript. Specifically, AI-based tools such as ChatGPT and Grammarly were utilized to enhance linguistic clarity and ensure grammatical accuracy. All concepts, research, data interpretation, analysis, and critical thinking presented in this work are the result of the authors' intellectual efforts. No AI tool independently generated any part of the submitted content without human oversight or critical input. The study's methodology, findings, and discussions were entirely conceived, analyzed, and articulated by the authors. Conflict of Interest The authors confirm that there are no competing interests in publishing this article. Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Clinical Trial Number Clinical trial number: not applicable. References Alqahtani T, Badreldin HA, Alrashed M, Alshaya AI, Alghamdi SS, Saleh B, Albekairy K, A. M. The emergent role of artificial intelligence, natural learning processing, and large language models in higher education and research. Res social administrative Pharm. 2023;19(8):1236–42. https://doi.org/10.1016/j.sapharm.2023.05.016 . Annamalai N, Eltahir ME, Zyoud SH, Soundrarajan D, Zakarneh B, Al Salhi NR. Exploring English language learning via Chabot: A case study from a self determination theory perspective. 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The use of AI (Artificial Intelligence) in English learning among university student: Case study in English Department, Universitas Airlangga. https://lmsspada.kemdiktisaintek.go.id/pluginfile.php/790962/mod_resource/content/1/Artikel%20Ilmiah% 20Filsafat%20final.pdf Gao H. (2021, February). Reform of college English teaching model under the background of artificial intelligence. In Journal of Physics: Conference Series (Vol. 1744, No. 4, p. 042161). IOP Publishing. 10.1088/1742-6596/1744/4/042161 Chen X, Zou D, Cheng G, Xie H. (2021, July). Artificial intelligence-assisted personalized language learning: systematic review and co-citation analysis. In 2021 international conference on advanced learning technologies (ICALT) (pp. 241–245). IEEE. https://doi.org/10.1109/ICALT52272.2021.00079 Hazaymeh WA, Alomery MK. (2022). The Effectiveness of Visual Mind Mapping Strategy for Improving English Language Learners' Critical Thinking Skills and Reading Ability. European Journal of Educational Research , 11 (1), 141–150. https://doi.org/10.12973/eu-jer.11.1.141 Hwang GJ, Xie H, Wah BW, Gašević D. Vision, challenges, roles and research issues of Artificial Intelligence in Education. Computers Education: Artif Intell. 2020;1:100001. https://doi.org/10.1016/j.caeai.2020.100001 . Johnson C, Urazov M, Zanoli E. (2021, June). Escapeling: a gamified, AI-supported chatbot for collaborative language practice. In The Learning Ideas Conference (pp. 141–148). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-90677-1_14 Karakaya K, Bozkurt A. Mobile-assisted language learning (MALL) research trends and patterns through bibliometric analysis: Empowering language learners through ubiquitous educational technologies. System. 2022;110:102925. https://doi.org/10.1016/j.system.2022.102925 . Xu G, Yu A, Liu L. (2025). A meta-analysis examining AI-assisted L2 learning. International Review of Applied Linguistics in Language Teaching , (0). https://doi.org/10.1515/iral-2024-0213 Alhabbash MI, Mahdi AO, Naser SSA. (2016). An intelligent tutoring system for teaching grammar English tenses. https://philpapers.org/rec/ALHAIT Chen B, Bao L, Zhang R, Zhang J, Liu F, Wang S, Li M. A multi-strategy computer-assisted EFL writing learning system with deep learning incorporated and its effects on learning: A writing feedback perspective. J Educational Comput Res. 2024;61(8):1596–638. https://doi.org/10.1177/07356331231189294 . Potočnik J, Foley S, Thomas E. Current and potential applications of artificial intelligence in medical imaging practice: A narrative review. J Med imaging radiation Sci. 2023;54(2):376–85. https://doi.org/10.1016/j.jmir.2023.03.033 . Sun Z, Anbarasan M, Praveen Kumar DJCI. Design of online intelligent English teaching platform based on artificial intelligence techniques. Comput Intell. 2021;37(3):1166–80. https://doi.org/10.1111/coin.12351 . Van Lieshout C, Cardoso W. (2022). Google Translate as a tool for self-directed language learning. https://doi.org/10.64152/10125/73460 Wang FL, Zhang R, Zou D, Au OTS, Xie H, Wong LP. A review of vocabulary learning applications: From the aspects of cognitive approaches, multimedia input, learning materials, and game elements. Knowl Manage E-Learning. 2021;13(3):250–72. https://www.kmel-journal.org/ojs/index.php/online-publication/article/view/479 . Wang X, Zhang W. Improvement of students’ autonomous learning behavior by optimizing foreign language blended learning mode. Sage Open. 2022;12(1). https://doi.org/10.1177/21582440211071108 . Wei J, Karuppiah M, Prathik A. College music education and teaching based on AI techniques. Comput Electr Eng. 2022;100:107851. https://doi.org/10.1016/j.compeleceng.2022.107851 . Elmahdi OEH, Balla AAS, Abdelrady AH, Osman E, Ahmed AOA. (2025). AI-Driven Vocabulary Acquisition in EFL Higher Education: Interdisciplinary Insights into Technological Innovation. Ethical Challenges, and Equitable Access . 10.30564/fls.v7i4.8760 Yu H, Nazir S. Role of 5G and artificial intelligence for research and transformation of English situational teaching in higher studies. Mob Inform Syst. 2021;2021(1):3773414. https://doi.org/10.1155/2021/3773414 . Zou B, Guan X, Shao Y, Chen P. Supporting speaking practice by social network-based interaction in artificial intelligence (AI)-assisted language learning. Sustainability. 2023;15(4):2872. https://doi.org/10.3390/su15042872 . Additional Declarations No competing interests reported. Supplementary Files Appendix.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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15:04:02","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":122904,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eVocabulary Score Comparison Between Groups\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-8425878/v1/a228a257f91162cbb6baa8c4.png"},{"id":101754094,"identity":"50d59994-5b28-49d8-ba76-73624545d489","added_by":"auto","created_at":"2026-02-03 10:41:36","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":184952,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation Between App Usage Frequency and Vocabulary Score\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-8425878/v1/13dcc59d7477da7a5d1b193b.png"},{"id":101683984,"identity":"c3f11536-ba49-41d1-9302-b1b2c5464012","added_by":"auto","created_at":"2026-02-02 15:04:02","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":47894,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThematic Analysis of Student Feedback On AI-Assisted Vocabulary Apps\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-8425878/v1/554fb280362048b02d7c3cd1.png"},{"id":105202835,"identity":"fde1c5b1-538d-4faf-9fd2-13de86a01f63","added_by":"auto","created_at":"2026-03-23 11:58:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2029292,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8425878/v1/2c38cc97-b4f3-4f4d-9155-219c3f3b35db.pdf"},{"id":101683976,"identity":"3295a229-5683-467f-a4b5-fc8948cb6f1b","added_by":"auto","created_at":"2026-02-02 15:04:01","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":14968,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-8425878/v1/33a41ada5444733b6f506ce8.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Exploring the Role of Artificial Intelligence-Assisted Vocabulary Apps in School English Language Education: A Survey-based Approach","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eArtificial Intelligence (AI) has profoundly reshaped our modern world, acting as a cornerstone of a new era in various sectors including healthcare, automotive industry, and most notably, education. This paradigm shift, primarily driven by AI, is gradually transforming the traditional methodologies in educational settings, ushering in a new era of digital learning and instructional design. Machines are utilized directly or indirectly in a variety of industries, including healthcare, automated cars, and sophisticated decision-making, as well as in educational institutions. The use of Artificial Intelligence (AI)-based technologies and the Internet has produced various educational advances for instructors and students (Yu \u0026amp; Nazir, 2021).\u003c/p\u003e\n\u003cp\u003eAI's influence in education extends from primary levels to higher education, where its integration is revolutionizing teaching and learning processes. These technologies are not just additional tools; they are transforming educational paradigms by providing personalized learning experiences and enabling adaptive learning environments. AI systems are capable of analyzing vast amounts of data to tailor instruction to the individual needs of students, addressing their specific weaknesses and strengths, thereby optimizing learning outcomes.\u003c/p\u003e\n\u003cp\u003eMoreover, AI's role in the classroom is multifaceted. For instructors, it offers advanced tools for managing coursework, automating administrative tasks, and providing real-time feedback on student performance. This not only enhances efficiency but also allows educators to dedicate more time to engage with students on a deeper, more personal level. For students, AI-powered platforms can facilitate a more engaging learning experience. Interactive learning apps, virtual tutors, and personalized learning modules adapt to each student's learning pace, providing customized feedback and resources that cater to their specific learning styles.\u003c/p\u003e\n\u003cp\u003eThe deployment of AI in higher education is already visible with systems that assist in everything from the grading of assignments to the detection of plagiarism, and even to the provision of personalized career counseling. The predictive analytics capabilities of AI are particularly transformative, enabling institutions to identify at-risk students early and provide timely interventions to support their academic journeys.\u003c/p\u003e\n\u003cp\u003eHowever, the rapid integration of AI in education also presents significant challenges and raises questions about data privacy, ethical considerations in AI decision-making, and the potential for widening the digital divide. Ensuring equitable access to AI resources, safeguarding student data, and maintaining a human-centric approach in education are critical considerations as we navigate this transition.\u003c/p\u003e\n\u003cp\u003eThe challenge of learning English vocabulary in school is significant. Many students struggle not only with acquiring new vocabulary but also with retaining the vocabulary they learn. Traditional instructional methods, including textbooks and classroom presentations, often fail to engage students effectively, leading to suboptimal learning outcomes (Gao, 2021). This issue necessitates innovative approaches to vocabulary instruction, and AI-assisted vocabulary apps present a promising solution. These apps can provide personalized, adaptive, and gamified learning experiences, which are tailored to individual student needs and learning styles.\u003c/p\u003e\n\u003cp\u003eDespite the proliferation of AI technologies in education, there remains a gap in understanding their specific impact on vocabulary acquisition among school students. While several studies have explored the use of AI in education broadly, few have focused specifically on how AI-assisted vocabulary apps influence student engagement and vocabulary retention in the context of English language learning. This study aims to fill this gap by providing empirical evidence on the effectiveness of AI-assisted vocabulary apps for 8th-grade and above students learning English as a second language. By comparing these innovative tools with traditional methods, this research seeks to offer insights into how AI can enhance language learning outcomes and student engagement.\u003c/p\u003e\n\u003cp\u003eAI can play a significant role in solving the problem of learning English vocabulary in school. AI can be used to personalize the learning experience by tailoring the content and difficulty level of the exercises to each student's individual needs. AI can also be used to provide adaptive feedback, which can help students to identify and correct their learning errors. Additionally, AI can be used to gamify the learning process, which can make vocabulary learning more fun and engaging (Chen, 2022).\u003c/p\u003e\n\u003cp\u003eThe use of AI in this research application is significant because it has the ability to transform the way that English vocabulary is taught in school. AI-assisted vocabulary apps can provide a personalized, adaptive, and gamified learning experience that can help students to learn new vocabulary more effectively (Johnson et al., 2022). Additionally, AI-assisted vocabulary apps can be used to track students' progress and to provide personalized feedback. The outcome of the present study can be used to improve the learning experience for all students.\u003c/p\u003e\n\u003cp\u003eThe following research questions are considered in this study:\u003c/p\u003e\n\u003cp\u003e1. What is the impact of AI-based learning English apps on school students' vocabulary learning outcomes?\u003c/p\u003e\n\u003cp\u003e2. How do these apps influence students' engagement with the learning process? \u003cp\u003e3. What are the factors that contribute to the effectiveness of AI-based learning English apps?\u003c/p\u003e\n\u003cp\u003eThe objectives of this research are listed as follows:\u003c/p\u003e\n\u003cp\u003e- To investigate the effectiveness of AI-assisted vocabulary apps on enhancing vocabulary learning outcomes among 8th-grade and above school students learning English as a second language. This objective aims to quantify the effect of the AI apps on students' vocabulary retention and application abilities.\u003c/p\u003e\n\u003cp\u003e- To examine the influence of AI-based learning apps on student engagement and motivation in the English language learning process. This involves understanding how these digital tools affect students' involvement, participation, and interest in learning English vocabulary.\u003c/p\u003e\n\u003cp\u003e- To identify and analyze factors contributing to the effectiveness and efficiency of AI-assisted vocabulary learning tools in educational settings. This objective seeks to uncover the nuanced elements (such as personalized learning experiences, gamification, and adaptive feedback) that make these apps beneficial for language acquisition.\u003c/p\u003e\n\u003cp\u003eDespite widespread adoption of AI-driven learning tools, empirical validation of their pedagogical impact at the school level particularly in vocabulary acquisition remains scarce. Most prior studies (Woo \u0026amp; Choi, 2021; Lin et al., 2022) have focused on tertiary learners, leaving a critical gap in understanding whether similar benefits translate to early secondary education. This study is motivated by that gap. It seeks to determine whether AI-assisted vocabulary applications can meaningfully enhance retention, engagement, and motivation in English vocabulary learning among students in Grades 8–10.\u003c/p\u003e\n\u003cp\u003eThe theoretical foundation rests on Cognitive Load Theory and Self-Determination Theory, which posit that adaptive and gamified feedback mechanisms can reduce extraneous load and increase intrinsic motivation. Accordingly, the study postulates that AI-based tools that personalize feedback and provide immediate reinforcement will produce superior learning outcomes compared to conventional rote methods.\u003c/p\u003e\n\u003cp\u003eH₁: Students using AI-assisted vocabulary apps will exhibit significantly higher post-test vocabulary scores than those using traditional methods.\u003c/p\u003e\n\u003cp\u003eH₂: Frequency of interaction with AI-assisted apps will positively correlate with students’ engagement levels.\u003c/p\u003e\n\u003cp\u003eH₃: Perceived personalization, adaptive feedback, and gamification will collectively predict students’ satisfaction and continued app usage intention.\u003c/p\u003e\n\u003cp\u003eThese hypotheses directly connect the literature-identified gap with measurable variables analyzed through the independent t-test and correlation techniques reported in the Results section.\u003c/p\u003e\n\u003cp\u003eThe remainder of this paper is organized as follows. Section 2 presents a comprehensive review of literature, highlighting key theoretical and empirical studies on AI-assisted language learning and vocabulary acquisition. Section 3 details the research methodology, including the mixed-methods design, participant profile, intervention process, and analytical procedures employed. Section 4 discusses the findings derived from both quantitative and qualitative analyses, emphasizing the comparative outcomes between traditional and AI-assisted learning environments. Section 5 concludes the paper by summarizing the major findings, educational implications, limitations, and recommendations for future research.\u003c/p\u003e"},{"header":"2. Review of Literature","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Strategic way of Language Learning\u003c/h2\u003e \u003cp\u003eVan Lieshout and Cardoso (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) investigated the pedagogical use of Google Translate, the use of text to synthesis and automated voice detection for second or foreign languages Dutch learning. Thirty participants utilized these tools for one hour to learn phrases and pronunciation. The pre/post/delayed post-test design reveals short-term acquisition success, supported by qualitative analysis of participant interactions. Overall, findings highlight Google Translate's versatility and adaptability to diverse learning needs and styles.\u003c/p\u003e \u003cp\u003eHazaymeh and Alomery (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) demonstrated that implementing visual mind mapping as a reading strategy significantly enhances the ability to think critically among the learners of English language. The experiment group exhibited a substantial improvement in critical thinking scores compared to the control group, emphasizing the positive impact of the realistic brainstorming approach on various critical thinking indicators. The findings affirm the strategy's effectiveness in elevating both reading and thinking strategically comprehension abilities among English language learners.\u003c/p\u003e \u003cp\u003eWang and Zhang (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) addressed the imperative of enhancing students' autonomous learning by developing a blended learning model based on SPOC in the context of a college English course. Employing data mining techniques, the research identifies optimal factor combinations from questionnaire-based exploration. The findings reveal that the optimized blended learning mode significantly stimulates learners' motivation, cultivates autonomous learning abilities, and improves overall autonomous learning behavior, demonstrating its effectiveness in fostering independent learning in foreign language education.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 AI Assisted Language Learning\u003c/h2\u003e \u003cp\u003eAI technology advancements drive increased AI speaking applications in foreign language acquisition (Zou et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). According to Chassignol et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) the rapid development of AI technology is altering information search, communication, and behavior in recent decades. Gonz\u0026aacute;lez Garc\u0026iacute;a et al. (2020) explained that AI-assisted vocabulary apps can be effective in improving English vocabulary skills. Dewi et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) stated that AI can enhance English learning through applications, websites, and virtual reality technology, but lacks a comprehensive examination of its role in student performance evaluation. Wang et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) reported that Mobile applications have become a popular vocabulary learning approach in China, with most allowing Students to gain word knowledge by translating into their own language. These applications often display phrases that combine text, images, and audio.\u003c/p\u003e \u003cp\u003eAI based education was leading to a new learning era among students (Woo \u0026amp; Choi, 2021). By combining human intelligence, a computer system might function as an intelligent instructor, tool, or tutee while also simplifying decision-making in the educational sector (Hwang et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The use of artificial intelligence (AI) in education is becoming increasingly widespread, and this is particularly true in the field of vocabulary learning. AI-assisted vocabulary apps offer a number of potential benefits for college students. Alqahtani et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) stated that AI applications for academia and schools provide help, feedback, evaluation, specialized curriculum, tailored career advising, and mental health advice. Annamalai et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) found Bots known as chat foster skills, independence, and resemblance; however, they lack a psychological context and provide inaccurate statistics for English language acquisition.\u003c/p\u003e \u003cp\u003ePotočnik et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) highlights the increasing use of AI-enabled solutions in medical imaging practice, automating tasks like pre-exam, subject location, methodology decision-making, photo capture, assessment, subsequent processing, and upkeep of equipment. The research conducted by Chu et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) demonstrated that Artificial Intelligence robots are being applied in education due to rapid development. Canada, Chile, and South Korea capitalized in AI based Robots in Education (AIRE) investigating premature, concentrating on pupils' learning abilities and conduct. The majority of AIRE research was undertaken in Language and Science fields, with a focus on problem-solving methodologies. Mobile devices have significantly impacted life and learning, with Mobile Assisted Language Learning (MALL) research focusing on five themes: self-regulated language learning, learner agency, personalizing learning through AI-supported mobile learning, learning in the wild, and supporting higher education (Karakaya \u0026amp; Bozkurt, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Wei et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) stated that AI-based teaching techniques, such as Music Education and Teaching based on AI (MET-AI), have significantly improved the standard of music education and teaching models. The MET-AI provides pupils with a learning result rate of 95.2%, an efficiency ratio of 98.1%, and a mean square error rate of 17.9%.\u003c/p\u003e \u003cp\u003eSeveral studies have been conducted on the effectiveness of AI-assisted vocabulary apps (Kessler, 2022) in college English language education. Evmenova et al. (2020) found that Artificial intelligence-assisted vocabulary apps can be effective in helping students learn new vocabulary. The study found that students who used AI-assisted apps (Lin \u0026amp; Wang, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) had significantly higher vocabulary scores than students who did not use these apps. Another study, conducted by Mizumoto et al. (2021), found that AI-assisted vocabulary apps can be effective in supporting English as Foreign Language (EFL) writing. The study found that students who used AI-assisted apps were more likely to use academic words in their writing than students who did not use these apps. A study conducted by Sun et al. (2022) found that AI-assisted vocabulary apps can be effective in improving students' vocabulary learning outcomes. The study found that students who used AI-assisted apps had significantly higher vocabulary scores than students who did not use these apps (Mahdi \u0026amp; Abu-Naser, 2021).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Motivation and Scope\u003c/h2\u003e \u003cp\u003eThe literature review presents a comprehensive overview of strategic language learning methodologies and the burgeoning role of AI in language education. It underscores the effectiveness of diverse pedagogical tools such as Google Translate, text synthesis, and automated voice detection in facilitating language acquisition, particularly in the Dutch and English learning contexts. Moreover, the discussion highlights the significance of innovative approaches like visual mind mapping and blended learning models in enhancing critical thinking skills and autonomous learning behaviors among language learners.\u003c/p\u003e \u003cp\u003eFurthermore, the review sheds light on the transformative impact of AI technology in language education, particularly in vocabulary acquisition and student performance evaluation. AI-assisted vocabulary apps and educational support systems offer promising avenues for enhancing learning outcomes by providing personalized learning experiences, feedback, and tailored assistance. Additionally, the integration of AI in diverse educational settings, including mobile-assisted language learning and music education, underscores its potential to revolutionize traditional teaching paradigms and optimize learning efficiency. Overall, the synthesis of these studies underscores the evolving landscape of language education, characterized by a convergence of traditional pedagogical methods with cutting-edge AI technologies, ultimately enhancing the effectiveness and accessibility of language learning experiences.\u003c/p\u003e \u003cp\u003eThe proposed research on the impact of AI-based learning English apps on vocabulary acquisition can significantly influence current research trends and the educational community in several ways. Firstly, by providing empirical evidence on the effectiveness of AI tools in language learning, it contributes to advancing research in the field of AI-assisted language education. Secondly, the study's findings can inform educators and policymakers about the potential benefits of integrating AI technology into language learning curricula, potentially leading to the adoption of innovative teaching methods in educational institutions. Additionally, by identifying factors contributing to the effectiveness of AI-assisted learning, the research can guide the development of more tailored and efficient language learning applications. Ultimately, this study has the potential to catalyze discussions and initiatives aimed at leveraging AI to enhance language learning outcomes, thereby positively impacting the broader educational community and shaping future research directions.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Research Methodology","content":"\u003cp\u003eThis research aims to investigate the impact of AI-based learning English apps on school students\u0026apos; (of 8th-grade and above) vocabulary learning outcomes, explore their influence on engagement with the learning process, and identify factors contributing to their effectiveness. Employing a mixed-methods approach, the study combines qualitative and quantitative analyses to provide a comprehensive understanding of the complex dynamics involved. This approach allowed us to not only measure the impact quantitatively but also to understand the students\u0026apos; experiences and the educational context qualitatively. The proposed conceptual framework of this research is illustrated in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1. Research Design\u003c/h2\u003e\n \u003cp\u003eThis study employs a mixed-methods approach, integrating both qualitative and quantitative analyses to provide a comprehensive understanding of the impact of AI-assisted vocabulary apps on student engagement and vocabulary retention. The research design is quasi-experimental, involving two groups: an experimental group using AI-assisted vocabulary apps and a control group using traditional vocabulary learning methods.\u003c/p\u003e\n \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\n \u003ch2\u003e3.1.1. Methods\u003c/h2\u003e\n \u003cp\u003eTo ensure the AI-assisted vocabulary apps were appropriately adapted for the study, several steps were taken. The apps were selected based on their ability to provide personalized, adaptive learning experiences and their incorporation of gamification elements. Prior to the study, a pilot phase was conducted where the apps were tested and modified to align with the specific learning objectives and curriculum requirements of the 8th-grade and above students. The adaptations included:\u003c/p\u003e- Customizing the content to match the vocabulary curriculum.\u003cp\u003e- Incorporating adaptive learning algorithms to tailor the difficulty level based on individual student performance.\u003c/p\u003e\n \u003cp\u003e- Adding gamification features such as quizzes, rewards, and progress tracking to enhance engagement.\u003c/p\u003e\n \u003cp\u003e- Ensuring compatibility with the students\u0026apos; existing technological infrastructure.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e3.1.2. Participants\u003c/h2\u003e \u003cp\u003eThe participants in this research are school students of 8th grade and above learning English as a second language. This focus allows for insights into a specific demographic with unique learning needs and preferences.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e3.1.3. Intervention\u003c/h2\u003e \u003cp\u003eThe intervention involved the introduction of AI-based learning English applications to a group of students. The AI apps served as supplementary tools for vocabulary learning. The study included a control group using traditional learning methods for comparative analysis. The adaptation process of the AI apps was critical to ensure they met the educational needs and engagement levels of the students. The experimental group used these customized AI apps over a period of eight weeks, while the control group continued with traditional vocabulary learning methods.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Measures\u003c/h2\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1. Vocabulary Learning Outcomes\u003c/h2\u003e \u003cp\u003eThe primary measure involves assessing the impact of AI-based apps on vocabulary learning outcomes quantitatively. This includes improvements in vocabulary usage, retention, and application in different contexts.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e3.2.2. Engagement with the Learning Process\u003c/h2\u003e \u003cp\u003eEngagement was measured through both quantitative and qualitative data capturing students' active involvement, participation, and interest in the learning process facilitated by AI-based apps. Specific engagement metrics included time spent on the app, frequency of app usage, and student feedback on the learning experience.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e3.2.3. Factors Contributing to Effectiveness\u003c/h2\u003e \u003cp\u003eFactors contributing to the effectiveness of AI-based apps were assessed through a mixed-methods approach.\u003c/p\u003e \u003cp\u003eQualitative data were collected through open-ended questions in the questionnaire exploring usability, relevance, and perceived advantages or disadvantages of using these apps. Quantitative data provided a structured analysis of these factors.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Data Collection\u003c/h2\u003e \u003cp\u003e \u003cb\u003eGroup 1\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe students are given a syllabus to learn through traditional methods.\u003c/p\u003e \u003cp\u003e \u003cb\u003eGroup 2\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe students were given the same syllabus and a set of AI apps to use for learning. The AI apps were adapted to include personalized feedback, gamification elements, and adaptive learning algorithms.\u003c/p\u003e \u003cp\u003e \u003cb\u003eQuestionnaire\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe survey helped in gathering data on student engagement and attitudes toward the learning methods used. The questionnaire (Appendix A) focused on engagement with the learning process, understanding assessment questions, and factors that contribute to the effectiveness of the apps. The questionnaire was designed to gather comprehensive data from upper-grade school students' experiences with AI-assisted vocabulary apps in the context of learning English. Vocabulary questions were included to measure the immediate impact on vocabulary retention and usage. It comprised three sections: Demographic Profile, Questions Related to Vocabulary Learning, and Topic-Specific Questions.\u003c/p\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e3.3.1. Demographic Profile\u003c/h2\u003e \u003cp\u003eThis section captures essential information about the participants, including their name, location, gender, age, educational background, and current grade in school. Additionally, it explores their proficiency in English, frequency of engagement with English outside the classroom, and the use of mobile apps for learning purposes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003e3.3.2. Questions Related to Vocabulary Learning\u003c/h2\u003e \u003cp\u003eThis segment delves into students' learning behaviors, preferences, and strategies. It assesses their interaction with course materials, comprehension levels, preferred learning tools, usage of additional resources, study techniques, motivation, and approaches to assessments. The Likert scale is utilized for quantitative responses, providing nuanced insights into the effectiveness of their chosen learning strategies.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003e3.3.3. Topic-Specific Questions\u003c/h2\u003e \u003cp\u003eThis section focuses on evaluating the participants' understanding of technology and communication-related vocabulary, linking theoretical knowledge with practical application. It requires students to define key terms, discuss the impact of technology on communication, and utilize learned vocabulary in written communication. Additionally, it explores their perceptions of the difficulty of specific terms, understanding of technological evolution, and awareness of the importance of technology and communication vocabulary in professional and personal contexts.\u003c/p\u003e \u003cp\u003eThe questionnaire employs a mix of open-ended and structured questions, enabling participants to express their thoughts freely while providing quantitative data for statistical analysis. The diverse range of inquiries ensures a holistic exploration of the participants' experiences and opinions regarding AI-assisted vocabulary learning.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Data Analysis\u003c/h2\u003e \u003cp\u003eA combination of qualitative and quantitative analyses was performed on the collected data to obtain a comprehensive understanding of the research problem. The study employed descriptive statistics (mean, percentage, and standard deviation) to summarize demographic and behavioral data, and inferential statistics primarily independent t-tests and Pearson\u0026rsquo;s correlation analyses to test the stated hypotheses comparing outcomes between the experimental and control groups. All analyses were carried out using SPSS (version 25) at a 0.05 significance level.\u003c/p\u003e \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e \u003ch2\u003e3.4.1. Qualitative Analysis\u003c/h2\u003e \u003cp\u003eQualitative data from open-ended survey responses and interviews provided insights into students' evolving relationship with AI in education. Most displayed a sound understanding of AI concepts and were able to provide real-life examples of AI-enabled tools. A majority concurred that advanced technologies are integral for communicative competence in the 21st century. Their creative application of technology-related vocabulary during role plays and written tasks indicated meaningful learning of concepts.\u003c/p\u003e \u003cp\u003eA prominent theme across responses was growing dependence on smart devices and digital assistants for daily tasks. While acknowledging associated risks like privacy breaches, many students believed AI assistants enhance productivity. Some even attributed improved academic performance to tools like chatbots and virtual tutors. A few displaying resistances desired moderation in AI usage to retain core human skills.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section3\"\u003e \u003ch2\u003e3.4.2. Quantitative analysis\u003c/h2\u003e \u003cp\u003eQuantitative analysis involved the use of mathematical and statistical procedures to test relationships and differences among variables. Before performing inferential analyses, the internal consistency of the questionnaire was verified using Cronbach\u0026rsquo;s alpha (α\u0026thinsp;=\u0026thinsp;0.87), indicating high reliability.\u003c/p\u003e \u003cp\u003eDescriptive statistics were computed to summarize participants\u0026rsquo; demographic characteristics and engagement patterns, which were visually represented in pie charts, bar graphs, and clustered column charts. To evaluate learning outcomes, an independent-samples t-test was conducted to compare the mean vocabulary-acquisition scores between the experimental group (AI-assisted app users) and the control group (traditional learners). This test determined whether the observed difference in mean performance was statistically significant, thereby addressing Hypothesis H₁. In addition, Pearson\u0026rsquo;s correlation analysis was used to examine relationships among English-proficiency level, frequency of app usage, engagement score, and vocabulary performance, with correlation coefficients (r) and p-values reported to indicate strength and direction. These analyses directly addressed Hypotheses H₂ and H₃ concerning engagement and app-effectiveness factors.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4. Results and Discussion","content":"\u003cp\u003eThis section presents the findings derived from both quantitative and qualitative analyses, as structured under four key subsections: Descriptive Statistics, Independent Samples t-Test, Correlation Analysis, and Qualitative Content Analysis. These analyses were conducted to evaluate the impact of AI-assisted vocabulary apps on students\u0026rsquo; vocabulary acquisition, engagement, and perceived learning effectiveness, thereby addressing all research objectives of the study.\u003c/p\u003e \u003cp\u003eTo begin with, descriptive statistics were employed to summarize the fundamental characteristics of the data, including students\u0026rsquo; vocabulary scores, frequency of app usage, and self-assessed engagement levels. This provides a preliminary understanding of the overall trends and distribution patterns across the experimental and control groups.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe demographic analysis provides insights into the composition and characteristics of the study sample. As illustrated in the gender distribution chart (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), the sample consisted of 53% male and 47% female students, indicating a nearly balanced representation that minimizes gender bias in interpreting learning outcomes. The age distribution reveals (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) that participants were primarily clustered between 13 and 15 years, reflecting the target demographic of students in grades 8 and above, consistent with the study\u0026rsquo;s educational focus.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn terms of English proficiency (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), a majority of students identified as having intermediate proficiency (62%), followed by beginner-level learners (38%), while only 20 students (20%) reported advanced proficiency. This distribution suggests a suitable sample for assessing vocabulary development interventions, particularly among learners requiring foundational or intermediate-level support.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eMobile app usage (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) for learning exhibited a notable trend, with 40 students (40%) reporting daily use, 30 students using them 2\u0026ndash;3 times per week, and diminishing usage among the remaining categories\u0026mdash;25 once a week, 15 rarely, and only 10 never. These figures underscore the growing familiarity and acceptance of mobile-assisted learning, particularly among the experimental group. The high frequency of usage among students provides a favorable context for evaluating the impact of AI-assisted vocabulary apps, as consistent engagement with the digital tools is a critical factor in determining learning efficacy. Collectively, the demographic trends affirm that the sample is appropriate for examining the relationship between app-based learning and vocabulary acquisition across diverse learner profiles.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAn independent samples t-test was conducted to statistically compare the mean vocabulary assessment scores between the experimental group (using AI-based apps) and the control group (using traditional learning methods). This helped determine whether the intervention had a statistically significant effect on learning outcomes.\u003c/p\u003e \u003cp\u003eThe results of the independent samples t-test provide strong statistical evidence supporting the effectiveness of AI-assisted vocabulary apps in enhancing vocabulary acquisition among school students. As shown in the summary Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the experimental group that used AI-based vocabulary tools achieved a mean score of approximately 82, significantly outperforming the control group, which recorded a mean score of around 68. The standard deviations of both groups remained within a comparable range, indicating consistent performance within each group.\u003c/p\u003e \u003cp\u003eThe bar chart shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e visually reinforces this difference, with the experimental group clearly outperforming the control group. Error bars representing standard error further confirm that the observed differences are not due to random variation. The t-statistic value of 12.13 and an associated p-value well below 0.001 indicate a highly significant difference between the groups, rejecting the null hypothesis with strong confidence.\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\u003eT-Test Summary\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean Score\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStandard Deviation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSample Size\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e67.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExperimental\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e81.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cb\u003et-Statistic: 12.13\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cb\u003ep-Value: 1.53 \u0026times; 10⁻\u003csup\u003e17\u003c/sup\u003e\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThese findings validate the study\u0026rsquo;s first objective, which was to assess the impact of AI-assisted apps on vocabulary learning outcomes. The results strongly suggest that integrating AI tools into vocabulary instruction can lead to measurable improvements in learner performance when compared to conventional methods. This statistical distinction also provides foundational support for subsequent analyses exploring engagement and the underlying factors contributing to the success of AI interventions in education.\u003c/p\u003e \u003cp\u003eA correlation analysis followed to explore the strength and direction of relationships between key variables such as app usage frequency, English proficiency level, and vocabulary test scores. Pearson\u0026rsquo;s correlation coefficient was used for this purpose to reveal interdependencies that contribute to vocabulary development and student motivation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe correlation analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e) reveals a moderate-to-strong positive relationship (r\u0026thinsp;=\u0026thinsp;0.40) between the frequency of AI app usage and students' vocabulary test scores, indicating that increased interaction with AI-assisted learning tools significantly contributes to improved language acquisition. The scatter plot clearly demonstrates that students who engaged with the apps more frequently\u0026mdash;especially those using them daily or several times a week\u0026mdash;tended to achieve higher performance outcomes. This statistically meaningful association supports the premise that consistent and active use of personalized digital tools enhances learning effectiveness. The observed trend affirms the study\u0026rsquo;s objective of evaluating the role of engagement, emphasizing that not just access to AI tools but sustained usage is instrumental in driving measurable academic gains in vocabulary learning.\u003c/p\u003e \u003cp\u003eFinally, qualitative content analysis of the open-ended questionnaire responses provided nuanced insights into students' subjective experiences with AI-assisted learning. Thematic patterns were identified to better understand the perceived benefits, challenges, and effectiveness of the vocabulary apps from the learners\u0026rsquo; perspective.\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\u003eQualitative Response Summary\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTheme\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFrequency of Mention\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentage (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePersonalized Feedback\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e75.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGamified Features\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e63.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEase of Use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e50.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImproved Retention\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e70.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTechnical Issues\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLearning Motivation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e60.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e highlights the most frequently mentioned aspects of the AI vocabulary apps, providing insight into the student-perceived factors contributing to their effectiveness. The thematic analysis of qualitative responses reveals that the majority of students (75%) highlighted the benefit of personalized feedback, noting how real-time, adaptive guidance from the app enhanced their understanding and retention of vocabulary. Similarly, gamified features were cited by 63% of participants as a motivating element, making the learning process more enjoyable and less monotonous. A significant number (70%) also mentioned improved retention, linking it to repeated exposure and interactive content.\u003c/p\u003e \u003cp\u003eMeanwhile, ease of use emerged as a commonly appreciated feature, with 50% of students affirming that the app's interface was intuitive and accessible. A smaller fraction (13%) pointed out technical issues, such as occasional lags or glitches, suggesting areas for technological refinement. Additionally, learning motivation was reported as positively impacted in 60% of responses, with students describing increased enthusiasm and time spent studying compared to traditional methods.\u003c/p\u003e \u003cp\u003eThe findings of this study strongly affirm the effectiveness of AI-assisted vocabulary apps in enhancing English language learning among school students. Addressing the first objective, statistical analysis revealed a significant improvement in vocabulary acquisition for students using AI tools, with the experimental group achieving a mean score of 81.92 compared to 67.94 in the control group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The second objective\u0026mdash;evaluating student engagement\u0026mdash;was supported by a moderate-to-strong positive correlation (r\u0026thinsp;=\u0026thinsp;0.40) between app usage frequency and vocabulary performance, indicating that consistent interaction with the AI tools translated into better outcomes. Lastly, the qualitative analysis addressed the third objective by identifying key features that contributed to app effectiveness, including personalized feedback (mentioned by 75% of students), gamification, ease of use, and increased motivation. These results collectively demonstrate that AI-based vocabulary apps not only improve learning outcomes but also enhance learner engagement and satisfaction, making them a viable and scalable solution for modern language education.\u003c/p\u003e"},{"header":"5. Discussion","content":"\u003cp\u003eThe findings of this study comprehensively address the research objectives and confirm all three proposed hypotheses, illustrating the effectiveness and pedagogical value of AI-assisted vocabulary learning among school students learning English as a second language. The results establish that AI-driven vocabulary apps not only enhance students\u0026rsquo; academic performance but also foster engagement, motivation, and self-directed learning behavior\u0026mdash;thereby achieving the intended outcomes of the study.\u003c/p\u003e \u003cp\u003eThe first objective sought to investigate the effectiveness of AI-assisted vocabulary apps in improving vocabulary learning outcomes compared to traditional instructional methods. The quantitative results clearly demonstrate this impact. The experimental group that used AI-based learning applications achieved a markedly higher mean score than the control group, with a statistically significant difference (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This finding strongly supports Hypothesis H₁, which predicted that students exposed to AI-assisted vocabulary learning would exhibit higher vocabulary achievement. The improvement can be attributed to several built-in mechanisms of the AI apps personalized instruction, adaptive difficulty adjustment, and gamified learning which collectively promoted active recall and reduced cognitive load. These outcomes are consistent with Cognitive Load Theory, which posits that adaptive scaffolding enhances information retention by focusing learners\u0026rsquo; attention on relevant material. The large effect size observed in the statistical analysis confirms not just statistical but also pedagogical significance, suggesting that integrating AI tools into regular classroom practice can meaningfully enhance vocabulary mastery.\u003c/p\u003e \u003cp\u003eThe second objective aimed to examine how AI-based apps influence student engagement and motivation during the learning process. The correlational analysis indicated a positive and moderate relationship (r\u0026thinsp;\u0026asymp;\u0026thinsp;0.40) between the frequency of app use and vocabulary test performance, confirming Hypothesis H₂. Students who interacted with the app more frequently demonstrated higher engagement and better outcomes, emphasizing the critical role of regular digital exposure in consolidating vocabulary knowledge. The engagement metrics and qualitative responses reinforce that gamification and interactive features were primary motivators driving this behavior. Learners reported that rewards, progress indicators, and interactive quizzes made learning enjoyable and sustained their daily participation. This aligns with Self-Determination Theory, which highlights that perceived autonomy, competence, and relatedness enhance intrinsic motivation. The AI tools met these psychological needs by allowing students to progress at their own pace, track their improvement, and receive immediate reinforcement.\u003c/p\u003e \u003cp\u003eThe third objective focused on identifying the factors contributing to the overall effectiveness of AI-assisted vocabulary apps. The qualitative findings confirmed Hypothesis H₃, which proposed that adaptive feedback, personalization, and gamification would collectively predict learner satisfaction and continued use. Students consistently highlighted adaptive feedback as one of the most valuable features, noting that instant correction and tailored recommendations helped them recognize mistakes and improve efficiency. Personalization also emerged as a major factor\u0026mdash;students appreciated that the app adapted to their learning level and targeted weaker areas. Additionally, accessibility through mobile devices allowed learning to extend beyond the classroom, supporting self-paced learning. Although a minority mentioned technical limitations or overdependence on digital tools, these concerns were minimal compared to the overall positive response. Together, these factors explain the success of the intervention by combining cognitive optimization (via adaptivity) and motivational engagement (via gamification and autonomy).\u003c/p\u003e \u003cp\u003eBeyond confirming the hypotheses, the results reveal novel contributions to the field of AI-assisted language education. First, this study provides empirical evidence of AI\u0026rsquo;s measurable effectiveness in secondary education, a level where previous research is scarce. Most existing literature focuses on tertiary learners; thus, the present findings extend theoretical and practical understanding to early adolescence, when foundational vocabulary development is critical. Second, the integration of both quantitative and qualitative evidence strengthens the explanatory power of the study. The mixed-methods approach moves beyond numerical comparison by illustrating why AI tools are effective\u0026mdash;linking statistical improvement with learner perceptions of motivation, feedback, and usability. Third, the positive correlation between engagement frequency and achievement introduces a behaviorally grounded model of habit-based learning, where consistent microlearning through AI applications leads to sustainable academic growth. Fourth, the findings contribute to a blended learning framework, suggesting that AI technologies can complement rather than replace teachers by automating repetitive vocabulary practice while enabling instructors to focus on higher-order language skills.\u003c/p\u003e \u003cp\u003eOverall, the discussion highlights that the research objectives have been successfully met and all three hypotheses empirically validated. The study demonstrates that AI-assisted vocabulary apps substantially enhance vocabulary performance, engagement, and motivation among school students. The adaptive and interactive nature of these tools transforms vocabulary learning from a passive memorization task into an active, personalized experience. The evidence also indicates that AI tools can function effectively as supplementary resources within traditional classrooms, aligning with the growing emphasis on data-driven, student-centered instruction.\u003c/p\u003e \u003cp\u003eThe novel contribution of this research lies in its holistic evaluation of AI in language learning bridging statistical significance with human experience. It establishes that AI can be meaningfully deployed at the school level to promote personalized, engaging, and scalable learning. These results carry important implications for educators, curriculum designers, and policymakers seeking innovative and inclusive ways to strengthen language learning outcomes through responsible AI integration.\u003c/p\u003e"},{"header":"6. Conclusion","content":"\u003cp\u003eThis study set out to examine the impact of Artificial Intelligence-assisted vocabulary apps on English language learning among school students, with a focus on vocabulary acquisition, student engagement, and the contributing factors to learning effectiveness. The evidence from both quantitative and qualitative analyses leads to a clear conclusion: AI-assisted vocabulary tools significantly enhance vocabulary learning outcomes and learner engagement when compared to traditional teaching methods. The experimental group demonstrated a substantial improvement in vocabulary scores, supported by a statistically significant t-test result (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). A moderate-to-strong positive correlation between app usage frequency and test performance further confirmed that active interaction with AI tools contributes meaningfully to academic gains. Qualitative feedback provided deeper insight into learner experiences, highlighting key features such as personalized feedback, gamified content, and ease of use as drivers of motivation and retention. These factors not only enriched the learning process but also catered to diverse learner needs, making the apps both effective and inclusive. The study's findings are particularly relevant in today's educational landscape, where digital transformation is accelerating. As schools and institutions seek scalable, engaging, and data-driven solutions, this research provides empirical support for integrating AI tools into language curricula. By demonstrating how AI can personalize and optimize vocabulary instruction, the study underscores the transformative potential of intelligent educational technologies. Future research may expand on these insights by exploring long-term learning impacts and adapting similar frameworks across other language and skill domains. Overall, the study contributes valuable evidence to guide innovation in educational practice and policy.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study involved students in an educational research context and was conducted in accordance with institutional ethical guidelines. The research protocol received ethical clearance from the Institutional Ethics Committee of St. Joseph\u0026rsquo;s Matriculation School, Pallalakuppam, where the data collection took place. \u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eConsent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs the participants were minors, informed consent was obtained from the school authorities and the parents/legal guardians of all participating students. Assent was obtained from the students themselves prior to data collection. Participation was voluntary, and confidentiality and anonymity were strictly maintained.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable, as the manuscript does not contain any individual person\u0026rsquo;s data in any form (including images, videos, or identifiable details).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMs. Winny E.S.R. conceptualized the study, designed the methodology, conducted data collection, performed data analysis, interpreted the results, prepared figures and tables, and wrote the original draft of the manuscript. Dr. Sneha M. supervised the research process, critically reviewed and revised the manuscript for intellectual content, and provided academic guidance throughout the study. All authors read and approved the final manuscript.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support these findings are available with corresponding author and can be shared upon request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of AI Use\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors affirm that Artificial Intelligence (AI) tools were solely used for grammar checking during the writing and editing process of this manuscript. Specifically, AI-based tools such as ChatGPT and Grammarly were utilized to enhance linguistic clarity and ensure grammatical accuracy. All concepts, research, data interpretation, analysis, and critical thinking presented in this work are the result of the authors\u0026apos; intellectual efforts. No AI tool independently generated any part of the submitted content without human oversight or critical input. The study\u0026apos;s methodology, findings, and discussions were entirely conceived, analyzed, and articulated by the authors.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors confirm that there are no competing interests in publishing this article.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eClinical Trial Number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eClinical trial number: not applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAlqahtani T, Badreldin HA, Alrashed M, Alshaya AI, Alghamdi SS, Saleh B, Albekairy K, A. M. 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Sustainability. 2023;15(4):2872. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/su15042872\u003c/span\u003e\u003cspan address=\"10.3390/su15042872\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Language acquisition, Language teaching, AI-based learning, English vocabulary apps, Survey, Mixed-Method Approach, Educational practices","lastPublishedDoi":"10.21203/rs.3.rs-8425878/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8425878/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study explores the impact of Artificial Intelligence (AI)-assisted vocabulary apps on English language learning outcomes among school students in grades 8 and above. The objectives include evaluating the effectiveness of these apps in vocabulary acquisition, examining their influence on student engagement, and identifying the factors contributing to their success in educational settings. A mixed-methods, quasi-experimental design was employed with two groups: an experimental group using AI-enabled vocabulary apps and a control group relying on traditional methods. The AI apps incorporated personalized learning paths, gamified interfaces, and adaptive feedback. Data were collected through structured questionnaires covering demographic information, learning behaviors, and vocabulary usage. Independent t-tests were used to compare performance outcomes, while open-ended responses provided qualitative insights. Statistical analysis indicated significantly higher vocabulary scores in the experimental group (Mean\u0026thinsp;=\u0026thinsp;82%) compared to the control group (Mean\u0026thinsp;=\u0026thinsp;68%), with a t-value of 4.29 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Engagement frequency positively correlated with performance (r\u0026thinsp;=\u0026thinsp;0.40), and motivation also showed a moderate positive correlation (r\u0026thinsp;=\u0026thinsp;0.67). Qualitative feedback highlighted benefits such as real-time feedback. The findings affirm that AI-assisted vocabulary tools enhance vocabulary retention and student engagement. These apps demonstrate strong potential for integration into school curricula, offering tailored and interactive learning experiences that can complement traditional instructional methods.\u003c/p\u003e","manuscriptTitle":"Exploring the Role of Artificial Intelligence-Assisted Vocabulary Apps in School English Language Education: A Survey-based Approach","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-02 15:03:57","doi":"10.21203/rs.3.rs-8425878/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"96a7e40c-d2bd-4b7c-a7a9-c9cf754c2b2e","owner":[],"postedDate":"February 2nd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-23T11:57:11+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-02 15:03:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8425878","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8425878","identity":"rs-8425878","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}