Gamified and Non-Gamified AI Tools in Enhancing EFL Listening Comprehension: An Analysis of Duolingo and Replika’s Impact on Engagement, Motivation, and Learning Outcomes

Gamified and Non-Gamified AI Tools in Enhancing EFL Listening Comprehension: An Analysis of Duolingo and Replika’s Impact on Engagement, Motivation, and Learning Outcomes | 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 Gamified and Non-Gamified AI Tools in Enhancing EFL Listening Comprehension: An Analysis of Duolingo and Replika’s Impact on Engagement, Motivation, and Learning Outcomes Dr Aliakbar Tajik This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6032009/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 Listening comprehension is a critical component of English language acquisition and a core foundation for effective communication and language development. This study investigates the impact of gamified and non-gamified AI-driven learning systems, Duolingo and Replika, on the improvement of English as a foreign language (EFL) listening comprehension. A concurrent mixed methods design was used to collect data from 53 Iranian high school students (aged 14–15) residing in Tehran Province. Quantitative data were collected through researcher-developed pre- and post-intervention listening comprehension assessments and a perception questionnaire, while qualitative data were derived from classroom observation checklists and semi-structured interviews (n = 15). Students were randomly allocated to two groups: Duolingo (n = 27) and Replika (n = 26), who participated in a 12-week intervention consisting of 24 sessions. Duolingo uses gamified elements such as rewards and points to increase engagement, while Replika uses conversation-based interactions. Quantitative analysis revealed significant improvements in listening comprehension for both groups, with the Duolingo group showing higher engagement metrics. Qualitative findings showed that Duolingo's adaptive algorithms and gamification elements fostered a more engaging and personalized learning experience. The study highlights how these AI-driven systems address previously overlooked aspects of EFL instruction through personalized, data-driven pathways, effectively improving adolescents' listening comprehension skills. The research reviewed here highlights how these systems address previously overlooked aspects of EFL instruction, particularly in improving adolescents' listening comprehension skills. By focusing on personalized, data-driven pathways, the study demonstrates how intelligent learning platforms can fill gaps in traditional teaching and effectively improve listening comprehension - a critical yet often underemphasized skill in language teaching. Artificial Intelligence and Machine Learning Educational Philosophy and Theory Gamified Smart Learning Systems EFL Listening Comprehension Personalized Learning Paths Learning Analytics Self-efficacy Adaptive Algorithms 1. Introduction Listening comprehension is a pivotal component in the acquisition of a second language, accounting for 50–80% of the total engagement in language learning (Yıldırım & Yıldırım, 2016 ; Hosseini et al., 2021 ). This essential skill contributes to the development of phonological awareness, lexical understanding, and pragmatic competence among language learners. As a multifaceted cognitive process, listening comprehension requires the simultaneous activation of several mechanisms, including sound discrimination, word recognition, syntactic parsing, and semantic integration, to construct meaning from auditory input (Buck, 2001 ; Goh & Vandergrift, 2021 ). In real-time communication, learners must also manage external factors such as speaker accent, speech rate, and contextual cues, which serve to further complicate the listening task (Rost, 2016).In academic and formal contexts, learners encounter a variety of discourse types, ranging from informal conversations to academic lectures, which demand distinct listening strategies and underscore the necessity for more nuanced instruction that is tailored to these challenges (Field, 2019). Despite its critical importance, conventional English as a Foreign Language (EFL) instruction frequently fails to prioritize the development of listening skills, particularly within Iranian high schools (Ghaed Sharaf et al., 2018 ). This deficit is evidenced by a paucity of exposure to authentic listening materials, minimal opportunities for practice, and the near-total absence of structured feedback mechanisms. Moreover, conventional language teaching practices in these contexts disproportionately focus on grammar, translation, reading, and test preparation, reducing listening exercises to mere assessment tools rather than opportunities for meaningful skill development (El Baghdadi et al., 2024 ; Namaziandost et al., 2019 ). The artificiality of these methods, exemplified by oversimplified listening materials and teacher-centered classrooms, leaves learners ill-prepared to navigate real-world communication scenarios involving natural speech variations, hesitations, and varied accents. The limitations of traditional methods highlight the urgent need for pedagogical innovations capable of addressing these gaps, and in recent years intelligent digital learning platforms, such as Duolingo, have emerged as promising solutions for EFL learners (Bakhtiar et al., 2024 ). These platforms leverage artificial intelligence (AI) to provide personalized learning pathways, track learner progress, and offer immediate feedback on performance. By analyzing learner-specific weaknesses, such as difficulties in word recognition, prosody, or understanding connected speech, these systems adapt instruction in real time to individual needs (Bakhtiar et al., 2024 ). Furthermore, they offer learners access to authentic listening materials across diverse accents, genres, and speech styles, significantly improving their exposure to real-life communication. Additionally, the integration of socio-emotional dimensions, such as anxiety reduction, motivational support, and confidence-building, addresses emotional barriers that are often overlooked in traditional classroom settings (Febrina & Hamdi, 2024 ). Among the innovations featured in these intelligent platforms, gamification is a powerful tool for enhancing language learning (Bennani et al., 2022 ; Hsu, 2024 ). Gamified elements, including progress tracking, achievement badges, streaks, competitive challenges, and collaborative tasks, create an engaging and interactive environment that encourages consistent practice. When integrated with intelligent learning systems, these gamified features have been shown to enhance learner motivation and engagement, while also addressing specific listening comprehension challenges. For instance, learners encountering difficulties with connected speech patterns can engage in targeted, game-like activities that focus on distinguishing boundaries between words. Similarly, those facing prosodic challenges can practice rhythm and intonation through contextualized exercises. This integration of gamification and personalization serves to address the deficiencies in motivation and pedagogical effectiveness that are characteristic of conventional methodologies, including in the context of EFL (English as a Foreign Language) instruction in Iranian high schools. Duolingo, a prominent intelligent language learning platform, integrates gamification components and adaptive learning mechanisms, rendering it particularly pertinent to the enhancement of listening comprehension skills (Jiang et al., 2024 ). The platform incorporates features such as the "Streak" system, crown levels, and leaderboards, which collectively foster an engaging environment conducive to sustained practice among learners. Furthermore, tools such as Duolingo Stories incorporate contextualized listening exercises, which are tailored to learners' proficiency levels. In addition, the platform's AI-driven analytics track performance in key areas, including phoneme recognition, prosody, and comprehension accuracy. These affordances directly address critical needs for authentic listening exposure, personalized feedback, and learner engagement, deficiencies that have long plagued traditional EFL instruction (García-Botero et al., 2019; Tuong & Dan, 2024 ).By enabling learners to progress at their own pace and receive immediate and actionable feedback, Duolingo offers a transformative alternative to conventional approaches. Despite the considerable attention that Duolingo has attracted for its gamified learning model, research on its impact remains incomplete. The majority of prior studies have focused on its effectiveness in general language acquisition rather than exploring the specific ways in which its gamification elements influence EFL listening comprehension (García-Botero et al., 2017; Zhang & Hasim, 2023 ). Furthermore, the mechanisms by which Duolingo's features, including points, badges, and feedback-driven learning tasks, enhance student engagement in listening skill development have received scant attention (Ghasemi et al., 2024). The present study aims to address these gaps by analyzing the interplay between Duolingo's adaptive, personalized learning paths, gamified features, and their impact on student engagement in EFL contexts. The investigation of these factors is expected to provide a more profound understanding of how a gamified intelligent learning platform can support listening comprehension, offering actionable insights for educators and technology developers alike. 1.1. Theoretical Framework The enhancement of listening comprehension in EFL learners can be comprehensively grasped by integrating multiple well-established theoretical perspectives. Schema Theory offers a foundational framework emphasizing the pivotal role of activating prior knowledge in facilitating the interpretation and retention of new auditory input. According to schema theory, listening comprehension is not a passive process but an active effort where learners establish connections between incoming information and their existing knowledge structures. Pre-listening activities designed to activate schemata, such as brainstorming, using graphic organizers, or employing the KWL (Know, Want to know, Learned) method, have proven effective in aiding comprehension (Apio, 2022 ). By bridging new auditory content with learners' prior experiences, educators can enhance processing efficiency and retention while also fostering greater confidence and engagement in listening tasks. Research supports the notion that schema activation is an essential step in preparing learners to decode and process language input more effectively (Apio, 2022 ). In addition to its cognitive underpinnings, listening comprehension is closely related to Self-Regulation , which encompasses learners' ability to monitor, reflect on, and adjust their learning strategies (Zimmerman, 2002 ). Self-regulated learners take an active role in identifying their challenges and employing tailored strategies to overcome them (Sansone et al., 2019 ). Within digital learning environments such as Duolingo, self-regulation is particularly relevant, as learners are given opportunities to track their progress, set goals, and adapt their methods according to feedback (Li & Bonk, 2023 ). The alignment between the theoretical underpinnings and the practical applications of these digital learning environments is instrumental in fostering learner autonomy, thereby empowering them to strategically manage their learning and enhance their engagement with listening comprehension activities. This theoretical framework underscores the efficacy of self-regulation theory in supporting personalized and adaptive learning environments, thereby reinforcing learners' capacity to monitor and refine their listening sub-skills (Zimmerman, 2002 ). The concept of dynamic assessment serves to further enrich the theoretical framework by integrating assessment and instruction through Collaborative Interaction, drawing from Vygotsky's sociocultural theory (Lantolf & Poehner, 2014 ; van Compernolle & Zhang, 2014 ). Conventional assessment measures learners' current proficiency; dynamic assessment, in contrast, measures their potential for development (Poehner & Lantolf, 2013 ). It relies on continual feedback and scaffolding to address individual difficulties in real-time, providing immediate insights into specific learner needs in the context of listening comprehension, whether related to vocabulary, grammatical structures, or phonological understanding ( Hidri, 2019 ). Addressing these domains with personalized guidance enables educators to facilitate progress within the Zone of Proximal Development (ZPD) of learners, thereby promoting significant advancement (Poehner & van Compernolle, 2011). This framework finds particular relevance in adaptive digital platforms, as it aligns with the feedback-driven approach characteristic of systems such as Duolingo, where learners encounter listening challenges that are consistently adjusted to their needs and developmental stage (Ma & Zhang, 2024 ). Gamification , a strategy that aims to motivate individuals, can be considered in conjunction with the aforementioned theories by its emphasis on the role of engagement and enjoyment in the context of language learning (Shortt et al., 2023 ). The incorporation of game-like elements, such as points, rewards, and levels, into gamified platforms has been demonstrated to motivate learners to engage with their studies for extended periods, thereby fostering both intrinsic and extrinsic motivation. Duolingo, a platform that employs this approach, provides learners with interactive tools and listening challenges that integrate storytelling, role-play, and the tracking of progress in a competitive environment. These features have been shown to reduce anxiety associated with language acquisition (García-Botero et al., 2019) and to foster sustained practice and engagement in listening tasks. Furthermore, the gamified environment enhances learners' focus and perseverance, creating a positive feedback loop where achievement and enjoyment reinforce each other. The connection between gamification and self-regulation is particularly evident, as learners monitor their performance, set targets, and work actively to achieve incremental gains in their listening proficiency. Vygotsky's Sociocultural Theory provides a solid theoretical foundation for understanding Duolingo's effectiveness in language learning, particularly EFL listening comprehension (Lantolf, 2000 ). Through the lens of situated learning and social constructivism, Duolingo's gamified platform exemplifies how technology can create scaffolded learning environments that align with Vygotsky's Zone of Proximal Development (ZPD) (van de Pol et al., 2019 ). The application's adaptive algorithm systematically adjusts task difficulty based on learner performance, creating a dynamic scaffolding mechanism that supports learners as they progress from their actual level of development to their potential level of development (Wood, 2021 ). This technological scaffolding is manifested through immediate feedback, progressive skill building, and contextualized learning activities, reflecting the interactional learning processes emphasized by Vygotsky in his theoretical framework (García-Carrión et al., 2020 ). The platform's social features, including leaderboards and community challenges, promote what Vygotsky called 'inner psychological' learning experiences, where language acquisition occurs through social interaction and collaborative engagement (Swain et al., 2015 ). In addition, Duolingo's situational learning approach, which presents language in context-specific scenarios, aligns with Vygotsky's emphasis on the social and cultural embeddedness of learning and facilitates the internalization of language patterns through meaningful, socially situated interactions (Lantolf et al., 2020 ). 1.2. Duolingo and its Features Advanced technology enables language learning anytime, anywhere, eliminating the need for traditional classroom attendance. With the rise of online platforms accessible through smart devices, learners can study languages at their convenience. Duolingo stands out as one of the most popular choices among the many language learning applications available. It offers free access via PCs, smartphones, and other devices, supports more than 23 languages, and has approximately 200 million users (Jašková, 2014 ). As a game-based platform, Duolingo is specifically designed to facilitate language learning through interactive challenges. Krashen ( 2014 ) describes Duolingo as web-based software that engages learners in translation-focused exercises. The application is compatible with multiple platforms, including Android, iOS, Windows, and web browsers. It covers the four basic English language skills: listening, speaking, reading, and writing (Inayah et al., 2020 ). Upon registration, users can select their learning level based on an initial placement test (Nushi & Eqbali,, 2017). One of the main advantages of Duolingo is its flexibility, which allows learners to access it anywhere, whether indoors or outdoors (Astarilla, 2018 ). The platform is user-friendly and does not require an extensive registration process, allowing immediate access by simply clicking the 'Start' button (Nushi & Eqbali, 2017 ). Duolingo offers a variety of exercises to enhance language skills (Nushi & Eqbali, 2017 ). The translation exercise helps learners practice by translating phrases between their native language and the target language. The matching exercise presents images that must be paired with corresponding words. The pairing exercise challenges users to match an equal number of words from two different languages correctly. Additionally, the listening exercise requires learners to type a phrase after hearing it, while the speaking exercise prompts them to repeat phrases aloud. Another valuable feature of Duolingo is the ability to revisit previous lessons, even after completing them (Nushi & Eqbali, 2017 ). However, as they point out, Duolingo primarily focuses on vocabulary development rather than comprehensive grammar instruction. Feedback indirectly facilitates grammar learning, indicating whether users' responses are correct or incorrect. Duolingo has been developed as a well-structured framework for language learning, accommodating learners of varying proficiency levels within a natural and adaptive environment (Kuvvetli & Kazu, 2024 ). Utilizing skill trees as hierarchical frameworks, the platform delivers meticulously organized lessons that progressively introduce diverse linguistic concepts and topics. These lessons are designed to facilitate gradual development in vocabulary acquisition, linguistic competence, and grammatical accuracy. According to Kazu & Kuvvetli ( 2024 ), Duolingo's diverse range of educational activities, including listening exercises, flashcards, multiple-choice tests, and speaking practices, cater to different learning styles and cognitive abilities, fostering an interactive and dynamic learning environment that enhances sustained learner engagement (Kazu & Kuvvetli, 2024 ). Duolingo serves as a prime example of modern language learning approaches, seamlessly integrating technological innovation with educational effectiveness (Kazu & Kuvvetli, 2024 ). The platform's pervasive influence in the domain of language acquisition signifies a pivotal juncture in the evolution of digital learning platforms, effecting a paradigm shift and democratizing language education on a global scale. 1.3. The Role of Gamification in Enhancing EFL Listening Skills Gamification has emerged as a transformative approach in English as a Foreign Language (EFL) and English as a Second Language (ESL) instruction, integrating game design elements such as points, badges, leaderboards, and challenges into non-game contexts to enhance student engagement and motivation (Sailer & Homner, 2020 ). This innovative strategy has shown significant promise in making the learning process more enjoyable and interactive for students, particularly in EFL/ESL settings where traditional methods may not adequately engage digital-native learners (Smith et al., 2023). By creating an immersive learning environment that mimics real-world challenges, gamification fosters active participation and enhances the overall learning experience (Zhang & Hasim, 2023 ). A notable advantage of gamification in EFL instruction is its ability to increase student engagement and motivation. Research indicates that traditional approaches to language teaching often fail to provide sufficient interactive opportunities outside the classroom. The integration of game-like features into gamified EFL settings has been shown to facilitate continuous, dynamic interactions that promote active learning (Su et al., 2021 ). This is particularly beneficial in the development of listening skills, which require sustained practice. Studies have consistently demonstrated that students participating in gamified EFL settings are more likely to be engaged and motivated to improve their listening abilities. Gamified learning environments have been shown to be particularly effective in enhancing listening comprehension (Mufidah, 2023).Research indicates that through game-based learning, students can receive immediate feedback on their listening exercises, significantly boosting their comprehension skills (Chen & Yang, 2021). The real-time feedback mechanism, a common feature of many gamified platforms, enables learners to comprehend and rectify their errors instantaneously, a process that is imperative for the mastery of listening comprehension (Zou et al., 2019).A statistical analysis of research findings reveals that when EFL learners are exposed to a gamified approach, they demonstrate statistically significant improvements in listening comprehension tasks in comparison to those in traditional settings (Li et al., 2024). Another advantageous aspect of gamification is its capacity to promote collaborative learning. Gamified platforms frequently incorporate social interaction and group tasks, thereby encouraging students to collaborate in order to solve challenges. This collaborative approach is imperative for enhancing listening skills, as it necessitates students to engage in active listening and comprehend information in real time. This interaction not only improves listening skills but also fosters a sense of community among learners, thereby creating a positive and supportive learning environment (Torres Rodríguez et al., 2023 ). The competitive aspects of gamification have been shown to enhance motivation in students (Smith et al., 2019). The accumulation of points, badges, and rankings serves to provide a constant challenge to learners, driving them to improve continuously, particularly in the context of listening skills, where students strive to achieve better comprehension by competing against themselves or their peers (Jones, 2021). The incorporation of leaderboards and achievements into the learning process has been shown to significantly enhance engagement and motivation, both of which are crucial in mastering complex skills such as listening comprehension (Hanus & Fox, 2015 ). Gamification has been demonstrated to be an effective tool in the enhancement of EFL listening comprehension, with its success being contingent upon the efficacy of its implementation. The design principles of the gamified system, including the type of games employed, the duration of exposure, and learner preferences, must be meticulously tailored to the specific requirements of students. Extended exposure to gamified activities and the utilization of games that resonate with students can result in substantial advancements in listening comprehension. In addition, it is imperative to emphasize the pivotal role of teacher training and involvement in the effective integration of gamification into the curriculum. This ensures that the educational experience is enhanced, as asserted by Zhang and Chen ( 2021 ). 1.4. Duolingo's Effectiveness as a Language Learning Tool in Language Learning Huynh et al. ( 2016 ) conducted a significant study to investigate the effectiveness of the Duolingo application as a gamification-based learning tool for vocabulary instruction in English language learning. Utilizing a quasi-experimental design with a nonequivalent control group, the researchers categorized students into experimental and control groups. Data collection encompassed specially developed vocabulary questions for pre- and post-tests, and analysis was conducted using SPSS version 22. The results demonstrated a significant enhancement in English vocabulary learning through the utilization of the Duolingo application, as evidenced by an independent samples t-test with a significant p-value of 0.000 (p < 0.05). The findings indicated a substantial enhancement in mean scores, from 69.44 in the preliminary test to 85.83 in the subsequent post-test, suggesting that students utilizing the Duolingo application demonstrated superior vocabulary mastery in comparison to those engaged in conventional instructional methods. The language center has reported significant advancements in student vocabulary following the integration of the Duolingo programme. Gragera ( 2024 ) conducted a quasi-experimental study to investigate the effectiveness of Duolingo's gamified features in enhancing EFL learning among secondary school students. The study involved 52 participants, aged 14–16, who were divided into experimental and control groups in order to assess the impact of the app on language proficiency and motivation. Utilizing a rigorous methodology, encompassing pre- and post-tests across multiple language skills (vocabulary, grammar, speaking, and writing), complemented by motivation surveys for the experimental group, the study yielded significant findings. The results demonstrated that Duolingo significantly enhanced motivation and vocabulary acquisition, with the experimental group attaining a notable 12% improvement in overall proficiency compared to the control group's 6%. However, the study also identified limitations in Duolingo's capacity to effectively develop speaking and writing skill. The practical recommendations derived from these findings suggest that Duolingo is most effective when used as a supplementary tool integrated with interactive teaching methods. In their insightful qualitative study, Fanni and Maharani (2024) explored students' perceptions of utilizing Duolingo to learn English grammar. Through semi-structured interviews with 15 EFL students, the researchers addressed a significant gap in understanding the practical effectiveness of gamified applications in grammar teaching. Their findings revealed that while students perceived Duolingo as an engaging and flexible tool for grammar learning due to its gamification features and accessibility, they also identified notable limitations. The study indicated that while Duolingo was effective in supporting fundamental grammar understanding and motivating active learning, students encountered challenges such as inadequate explanations of complex grammar rules, technical issues, and pressure from the grading system. Notably, their research showed that students viewed Duolingo as a complementary tool rather than a stand-alone solution, emphasizing the necessity to integrate it with traditional classroom teaching to enhance comprehension through direct interaction and feedback. The findings of this study offer valuable insights into the practical implementation of gamified language learning applications, suggesting that a balanced approach combining Duolingo with face-to-face instruction could optimize grammar learning outcomes. In their 2023 study, Jiang et al. undertook a significant empirical investigation into the efficacy of Duolingo's Basic English course (CEFR A2) for Portuguese speakers. The study's participants numbered 92 English language learners who reported using Duolingo as their exclusive learning tool, with minimal prior English language knowledge. Utilizing Avant Assessment's STAMP 4S English test, the study specifically examined reading and listening skills. The findings of the study indicated noteworthy outcomes, with the learners attaining Intermediate High in reading and Intermediate Mid in listening as per the ACTFL scale following the completion of the A2 level. It is noteworthy that these outcomes closely resembled those reported in Jiang and Pajak's (2022) study on Spanish speakers, thereby providing substantial evidence for the consistency and efficacy of Duolingo's English courses across diverse linguistic backgrounds. The replication of results across diverse language groups serves to reinforce the validity of Duolingo as an effective tool for developing fundamental receptive language skills, particularly reading and listening comprehension. Jiang and Pajak's (2022) study constitutes a comprehensive evaluation of the effectiveness of Duolingo's English course for Spanish speakers at different CEFR levels. The study's participants numbered 263 learners who were divided into three groups, according to their level of proficiency at the end of A1 (n = 97), mid-A2 (n = 94), and end of A2 (n = 72). The participants reported using Duolingo as their sole learning tool, with minimal prior knowledge of English. Utilising Avant Assessment's STAMP 4S English test, the study monitored the development of reading and listening competencies. The findings revealed a substantial and consistent enhancement across the three levels, with learners who completed A2 demonstrating Intermediate High proficiency in reading and Intermediate Mid proficiency in listening on the ACTFL scale. The results exhibited a discernible developmental trajectory, with learners at the A2 level demonstrating significant advancement compared to those at the A1 level. The study provides compelling evidence of Duolingo's effectiveness in developing basic receptive language skills, particularly in reading and listening comprehension for Spanish-speaking learners. Essafi et al. ( 2024 ) conducted a systematic evaluation of three prominent mobile-assisted language learning (MALL) applications – Babbel, Memrise, and Duolingo – addressing a significant gap in the literature on the effectiveness of such tools. Utilizing a qualitative content analysis approach, the researchers developed and implemented an adapted evaluation rubric focusing on three critical dimensions: design, content, and pedagogy. The findings indicated that, despite their primary design for basic and intermediate language learning, these MALL applications offer valuable features that enhance the learning experience. These features include robust offline functionality, comprehensive application support, clearly defined learning objectives, varied learning activities, and effective gamification elements. The study's methodological approach involved a two-stage evaluation: an initial direct contact analysis and a systematic evaluation using their specially adapted evaluation tool. This research makes a significant contribution to the field by providing educators and learners with evidence-based criteria for selecting effective language learning applications, while also highlighting areas for future research in mobile language learning. 1.5. The Current Study: Gap and Significance The present study is distinguished by its innovative exploration of the impact of gamification within Duolingo, an AI-driven language learning platform, on English as a Foreign Language (EFL) students' listening comprehension. While there have been recent studies that have demonstrated the effectiveness of Duolingo in various aspects of language acquisition, including vocabulary and reading skills (Hia et al., 2024; Gragera, 2024 ), a critical review of the existing literature reveals substantial gaps in understanding how gamified AI-based learning platforms enhance EFL listening comprehension. While there have been some encouraging findings regarding vocabulary acquisition and overall language proficiency (Jiang et al., 2023 ; Jiang & Pajak, 2022 ), the intricate dynamics between personalized learning pathways, student engagement, and the development of listening skills remain insufficiently explored. This research aims to address this gap by delving deeper into the intersection of gamified learning environments and listening skill enhancement, with a particular focus on Iranian schools. The first gap relates to the empirical foundation of AI-driven personalized learning in enhancing listening comprehension. While the influence of Duolingo on vocabulary acquisition and reading skills has been extensively documented, there is a paucity of empirical studies focusing specifically on how gamification features contribute to improving listening skills. Listening is a crucial aspect of language acquisition, yet it is often underrepresented in research on mobile-assisted language learning. Furthermore, extant studies have failed to adequately establish how gamification mechanics interface with the cognitive processes involved in listening comprehension, thus hindering educators' ability to optimize these technologies for effective listening instruction. The present study offers a comparative analysis of two distinct AI-based systems, Duolingo and Replika, with a view to introducing a novel perspective to the extant literature on the practical impact of gamification and competition in language learning. While Duolingo is widely recognized for its effectiveness in language acquisition through the use of gamified elements, Replika's role as an interactive AI chat platform offers unique opportunities for personalized learning experiences. The study's objective is to provide valuable insights into how gamified and competitive features of AI tools can enhance language acquisition, particularly in the context of listening skills, by contrasting the effects of these two platforms on listening comprehension. Moreover, this comparison highlights how Duolingo's structured gamification strategies, centered around competition and achievements, differ from Replika's human-like conversational approach, allowing for a deeper understanding of how these distinct methodologies influence learner engagement and listening skill outcomes. This analysis thus bridges a critical gap in understanding how AI-powered tools, with varying emphases on gamification and competition, contribute to the development of listening comprehension in EFL contexts. The second gap pertains to students' perceptions and experiences, despite the growing recognition of the importance of personalized learning in language learning applications (Essafi et al., 2024 ). There is a noticeable absence of research on how AI-driven adaptive learning pathways contribute to the development of listening skills specifically. While previous research has examined general learning outcomes, the link between personalized learning trajectories and improvements in listening comprehension has yet to be thoroughly investigated. Additionally, the paucity of research into how students perceive and interact with AI algorithms, especially in terms of adaptation to individual listening comprehension patterns, limits the capacity to design more effective personalized learning solutions. The third gap addresses the correlation between observational and quantitative data. Although student engagement with language learning apps has been examined (Fanni & Maharani, 2024), there is a dearth of empirical evidence concerning how engagement patterns within gamified environments affect listening comprehension outcomes. This is of particular importance given the unique challenges of maintaining sustained engagement in listening activities, which require ongoing attention and focus. Moreover, there is an urgent need to understand how different types of gamification elements influence student motivation and persistence, particularly in the context of listening tasks, as these factors play a pivotal role in determining learning outcomes. Another innovative aspect of this study is its focus on Duolingo’s use as scaffolding in English language teaching (ELT). Scaffolding, which refers to the support provided by a teacher or learning tool to help students complete tasks they cannot do independently, has proven to be an effective strategy in language learning. This research uniquely positions Duolingo not just as a language learning tool but as a scaffold to enhance listening comprehension, addressing a gap in current ELT practices, particularly in Iranian schools, where such tools are not yet fully integrated into the curriculum. The significance of this study lies in its innovative methodological design, utilizing a mixed-methods approach with concurrent triangulation to provide a comprehensive analysis of Duolingo’s gamified learning system. By combining quantitative assessments, such as pre-and post-tests on listening comprehension, with qualitative analyses of student engagement patterns, this research aims to provide a nuanced understanding of how gamification and personalized learning paths interact to foster listening skill development. The rigor of this study’s methodology is strengthened by its systematic data collection and analysis, ensuring that both the quantitative and qualitative components offer meaningful insights into the research question. The findings of this study will have practical implications for the development and implementation of educational technologies, in addition to theoretical contributions. The intersection of AI-driven personalization and listening comprehension is examined in this research, with the objective of contributing to the design of gamified learning tools that can more effectively support language acquisition. Educators, curriculum developers, and educational technology designers seeking to enhance listening comprehension skills through innovative digital platforms will find the insights gained from this study invaluable. 2. Literature Review and Hypothesis Development Farisatma et al. ( 2024 ) conducted a study exploring the effectiveness of Duolingo as a Mobile-Assisted Language Learning (MALL) tool at an Indonesian university, focusing on its impact on learner engagement, motivation, and language acquisition. Their research demonstrated that Duolingo’s gamification features, such as points, levels, and rewards, significantly enhanced user engagement and motivation, making the language learning process more interactive and enjoyable. The study found that 51% of participants reported improvements in listening and speaking skills, highlighting the app’s effectiveness in promoting language acquisition through interactive exercises and speech recognition tools. Additionally, the research underscored the flexibility of mobile learning, which allows learners to integrate language practice into their daily routines. Despite these promising findings, the study identified three gaps in the literature: (1) the need for more empirical research on how gamification enhances listening comprehension, especially among secondary school students, (2) insufficient understanding of the role of AI-driven adaptive learning in improving listening skills for younger learners, and (3) a lack of evidence on how interaction patterns in gamified environments influence listening outcomes in secondary education. To address these gaps, the current study proposes a mixed-methods approach to further explore how gamification and personalized learning paths can enhance listening comprehension for secondary school students, a demographic that has been largely underrepresented in mobile-assisted language learning research. While the pre-experimental study by Putri and Islamiati ( 2018 ) on vocational school students offered initial evidence of Duolingo’s effectiveness in improving listening skills, several critical limitations within their methodology and study design are addressed by the current research. Despite their quantitative findings showing a statistically significant improvement in listening skills (p < 0.005), the study’s pre-experimental design, which involved only 36 students from a single vocational class, restricts the generalizability of their conclusions. In addition, while Putri and Islamiati demonstrated a basic correlation between Duolingo usage and listening skill enhancement, their study did not investigate the underlying mechanisms by which gamified features and AI-driven personalization contribute to listening comprehension—a crucial aspect that the present study aims to address through its advanced mixed-methods approach. Furthermore, their research focused exclusively on quantitative data, leaving unexplored the qualitative dimensions of student engagement, particularly how personalized learning paths and adaptive features of the application influence learning outcomes. This study goes beyond their work by employing a concurrent triangulation methodology, integrating both quantitative results and qualitative insights into how students interact with personalized learning features. The current investigation further builds on their foundational study by incorporating a more comprehensive analysis of how various gamification elements specifically impact listening comprehension, thereby addressing the gap in the literature regarding the detailed relationship between gamification mechanics and listening skill development. Purwanto et al. ( 2022 ) conducted an experimental factorial study comparing the effectiveness of Duolingo and SPADA platforms across different achievement levels, which provided valuable insights. However, several notable limitations within their research are addressed in the current study. Their 2x2 factorial design demonstrated the effectiveness of both platforms for high and low achievers, but it did not explore the specific mechanisms through which gamified elements influence listening comprehension. This critical gap is addressed by the present study, which utilizes a mixed-methods approach to examine these mechanisms. Additionally, although Purwanto et al. established the general effectiveness of the platforms across achievement levels, their study did not delve into how AI-driven personalization adapts to individual learning trajectories, an area our study tackles through a systematic analysis of personalized learning pathways. By extending beyond their comparative framework, the current investigation provides a more in-depth understanding of the relationship between gamification elements and listening skill development. This research further explores how these gamification features impact student engagement and learning outcomes, offering new insights into their effects across varying proficiency levels. Goodwin and Naismith’s ( 2023 ) comprehensive framework for assessing listening skills on the Duolingo English Test offers valuable insights into the operationalization of the construct. However, their research primarily focused on assessment design rather than on the learning process itself, a gap that the present study addresses. Grounded in Aryadoust and Luo’s ( 2023 ) multi-layered framework, their work effectively mapped listening subskills and cognitive processes, but it did not examine how gamified elements and AI-driven personalization enhance these processes during skill acquisition. The current study goes beyond their assessment-focused approach by exploring the dynamic relationship between gamified learning elements and listening comprehension development. Additionally, it investigates how personalized learning pathways adapt to individual cognitive processes, providing essential insights into the pedagogical mechanisms that enhance listening skill development within gamified environments. Tuong and Dan’s ( 2024 ) research offers valuable insights into mobile-assisted language learning, specifically examining Duolingo’s impact on listening comprehension among Vietnamese university students. Their study, involving 39 third-year English majors at Can Tho University, demonstrated the app’s positive effect on listening skills through real-life scenarios and repetitive practice. However, their research leaves several gaps. First, it focuses exclusively on university students, while empirical studies on how gamification elements affect listening comprehension in secondary school students are scarce. Second, despite identifying limitations in feedback mechanisms, their study does not explore how AI-driven adaptive learning could address these issues, particularly for younger learners. Finally, while the study emphasizes the importance of regular engagement, little empirical research exists on how interaction patterns in gamified environments specifically influence listening comprehension outcomes in secondary education. Our study aims to address these gaps by employing a mixed-methods approach to explore how gamification and personalized learning pathways enhance listening skills among secondary school students, a group underrepresented in mobile-assisted language learning research. Therefore, our quasi-experimental study of 53 Iranian secondary school students bridges this gap by examining how Duolingo’s gamified intelligent learning system enhances listening comprehension through adaptive algorithms and engagement strategies. The experimental group demonstrated significant improvements (27.8% increase) compared to traditional methods (8.3% increase). Building on these theoretical foundations and addressing identified research gaps, this study hypothesizes that integrating AI-driven emotional intelligence in language learning platforms is positively associated with improved speaking performance (H1). Research Questions What is the statistical difference in listening comprehension proficiency between EFL students using gamified AI-driven personalized learning systems, such as Duolingo, and those using non-gamified AI tools, like Replica? How do high school students perceive personalized learning paths in Duolingo as an effective means of enhancing their listening comprehension and engagement in EFL learning? Do the results of classroom observation checklists in the experimental group using Duolingo's intelligent learning system verify the results obtained from interviews and the perception questionnaires? The following null hypothesis was tested statistically to address the first research question of the study: H0 : No significant differences exist between the effects of AI-driven personalized learning paths in Duolingo’s gamified system and conventional instruction on high school EFL students’ listening comprehension proficiency and engagement levels. 3. Methodology This study employed a mixed-methods approach with a concurrent triangulation design to comprehensively assess the effectiveness of two intelligent learning systems, Duolingo and Replicа , on enhancing listening comprehension skills among English as a Foreign Language (EFL) students in Iran. The research was conducted with 53 high school students aged 14–15 years from Tehran Province, Iran. Participants were randomly assigned to one of the two groups: the Duolingo group (n = 27) and the Replicа group (n = 26). Both groups received a structured intervention spanning 12 weeks , encompassing 24 sessions of instruction utilizing the respective intelligent learning platforms. To establish group equivalence, a pre-test was administered before the intervention to assess language proficiency and ensure homogeneity between the groups. The pre-and post-intervention listening comprehension assessments designed specifically for Iranian eighth and ninth-grade students were based on the Prospect 2 and Prospect 3 textbooks. This assessment comprised five distinct sections: Understanding Main Conversations (5 marks), Specific Information Detection (4 marks), Classroom Instructions Comprehension (3 marks), True/False Recognition (4 marks), Dialogue Completion (4 marks). The total score for the listening comprehension assessment was 20 marks . The validity of the assessment was established through expert review . A panel of three experts in English language teaching and assessment reviewed the test items for alignment with the listening skills targeted in the Prospect 2 and Prospect 3 textbooks and confirmed that the test content adequately represented the construct of listening comprehension for Iranian 8th and 9th graders. Additionally, content validity was supported by ensuring comprehensive coverage of listening tasks commonly encountered in the student’s curriculum. The reliability of the assessment was determined through a pilot study, conducted with a sample of 30 students from a similar demographic to the study participants. The internal consistency of the assessment was computed using Cronbach’s alpha, yielding a reliability coefficient of 0.87, indicating high reliability. This ensures that the instrument provides consistent results over repeated administrations. Two primary quantitative instruments were employed in this study. Firstly, a researcher-developed questionnaire , comprising 18 items ( see Appendix A for the complete item list ) , served as the second quantitative instrument. The questionnaire was organized into five dimensions: learning engagement , system usability , perceived effectiveness , learning progress , and motivational aspects . Each item was rated on a five-point Likert scale , ranging from “ Strongly Agree ” to “ Strongly Disagree .” The instrument underwent both exploratory and confirmatory factor analyses, establishing robust construct validity (α = .89) and reliability. It effectively captured data on daily application usage patterns, homework completion rates, and voluntary participation in listening activities. In addition to the quantitative measures, qualitative data were collected using two instruments specifically designed to explore students’ perceptions of the intelligent learning systems: Researcher-developed Semi-Structured Interviews ( see Appendix B for the complete item list ) : Conducted post-intervention with a sample of participants (n = 20), these interviews utilized a protocol consisting of eight questions focused on learners’ experiences with intelligent learning systems. Key areas of inquiry included learning motivation , self-efficacy , and perceived benefits of personalized learning paths . Each session lasted between 25–35 minutes and was digitally recorded for accurate transcription and analysis. To enhance the credibility of the qualitative data, member checking and peer debriefing procedures were applied. Researcher-developed Observation Checklists ( see Appendix C for the complete item list ) : An observation checklist was implemented across various sessions after the intervention. This checklist was validated by a panel of experts and pilot-tested for reliability (inter-rater agreement = 0.88 ). It focused on three primary dimensions: learner-system interaction patterns , learning environment dynamics , and engagement indicators . Trained observers systematically documented behavioral indicators such as participation frequency, response patterns, and interactions with gamified elements of the platform across twenty observation sessions . The observation protocol employed a binary coding system supplemented by qualitative notes to ensure comprehensive data capture. Statistical analysis of the quantitative data was conducted using SPSS software, employing paired sample t-tests and analysis of variance (ANOVA) to evaluate significant differences between the two groups. Qualitative data were analyzed thematically, with findings triangulated against quantitative results to provide a holistic understanding of the effectiveness of the intelligent learning systems on listening comprehension and student engagement. This methodology effectively addresses the research questions regarding the impact of personalized learning paths on listening comprehension and engagement, ensuring a comprehensive evaluation through the use of multiple data sources and rigorous methodological standards. 3.1. Participants This study was conducted in Varamin, a city south-east of Tehran, Iran, among middle school students aged 14–15 years in grades 8 and 9. A systematic sampling method was used to select a representative sample. First, eligible schools were identified from a list of lower secondary schools (middle schools), and then six schools representing both male and female student populations were systematically selected using structured inclusion criteria and stratified randomization to ensure equal representation of demographic groups. This sampling strategy was implemented to ensure the validity and generalizability of the study's findings by taking into account the diversity of the student population. Following the administration of a standardized language proficiency test to assess initial language skills, 53 students with an intermediate level of language proficiency were selected as participants. These students were then divided into two groups: the experimental group (n = 27) and the comparison group (n = 26). Participants in both groups were required to meet identical demographic and academic inclusion criteria, which increased the methodological rigor of the study and allowed for the control of extraneous variables. The intervention took place over a 12-week period and included 24 classroom sessions. Students in the experimental group used the digital learning platform Duolingo, which included gamified elements such as leaderboards, point-based progress tracking and interactive challenges. This platform aimed to improve listening comprehension skills through a combination of adaptive learning techniques and engaging competitive tasks. In contrast, the comparison group used the Replika AI application, a virtual assistant designed to simulate natural language interactions and help practice and improve language skills through personalized dialogues and immersive activities. It should be noted that both groups were exposed to similar content and duration of learning to ensure the reliability of the results. This robust methodology allowed the researchers to examine the differential impact of gamified digital learning tools versus conversational AI approaches on language acquisition, while controlling for confounding demographic and instructional variables. 3.2. Data Collection Instruments This study used a mixed methods research design to investigate the effectiveness of Duolingo's gamified intelligent learning system on EFL listening comprehension. The data collection process included multiple instruments to ensure comprehensive evaluation and methodological triangulation. The primary quantitative instrument consisted of a pre- and post-intervention listening comprehension assessment specifically designed for Iranian 8th and 9th grade students based on Prospect 2 and Prospect 3 textbooks. The assessment consisted of five different sections: Comprehension of main conversations (5 marks), Recognition of specific information (4 marks), Comprehension of classroom instructions (3 marks), True/False recognition (4 marks), and Dialogue completion (4 marks), totaling 20 marks. An extensive validation process was undertaken to establish the psychometric properties of the instrument. Content validity was rigorously assessed by a panel of nine experts (six university professors, two English language supervisors and one experienced trainer), yielding a content validity index (CVI) of 0.90 and a content validity ratio (CVR) of 0.88. Construct validity was confirmed by factor analysis, yielding a KMO measure of 0.83 and a significant Bartlett's test (p < 0.001), which identified five distinct components aligned with the test sections. Convergent validity was established by correlation with existing standardized tests (r = 0.85). The reliability measures demonstrated strong internal consistency, with Cronbach’s alpha coefficients ranging from 0.81 to 0.85 for individual sections and 0.87 for the overall test. Inter-rater reliability was established through two independent raters scoring 25% of responses, achieving a Cohen’s Kappa coefficient of 0.89 and a Pearson correlation of 0.92. Test-retest reliability was assessed over a two-week interval with 40 students, yielding a correlation coefficient of 0.86. Item analysis revealed appropriate difficulty indices (0.35–0.75), discrimination indices (0.38–0.65), and point-biserial correlations (0.42–0.68). The assessment materials were refined on the basis of expert feedback, taking into account time allocation (25 minutes total), content appropriateness, clarity of instruction, audio quality and playback frequency (each section was played twice at 30-second intervals). The content was carefully aligned with the objectives of the Iranian National Curriculum and the listening comprehension objectives of the Prospect textbooks, differentiating between grade 8 (everyday conversation and basic descriptive language) and grade 9 materials (advanced topics and complex linguistic structures). The design of the instrument took into account pedagogical considerations specific to Iranian EFL learners, ensuring cultural appropriateness and familiar contexts, while effectively discriminating between different proficiency levels. These extensive validation results demonstrate the robust psychometric properties of the instrument and its suitability for measuring listening comprehension among Iranian secondary school students. The high reliability coefficients and validity indices confirm the consistency of the assessment in measuring the targeted listening comprehension skills, while maintaining appropriate levels of difficulty for the targeted age groups. The researcher-developed 18-item questionnaire served as the second quantitative instrument. The questionnaire items were organized into five dimensions: learning engagement, system usability, perceived effectiveness, learning progress and motivational aspects. Each item was rated on a five-point Likert scale ranging from 'strongly agree' to 'strongly disagree'. The instrument underwent both exploratory and confirmatory factor analyses to establish construct validity (α = .89) and reliability. It effectively captured data on daily application usage patterns, homework completion rates, and voluntary participation in listening activities. Qualitative data collection involved two main instruments designed to explore students' perceptions of the intelligent learning system (through semi-structured interviews) and to document their actual interaction patterns with the system (through structured observations). First, semi-structured post-intervention interviews were conducted with participants (n = 20). The interview protocol, validated by expert review and pilot testing, consisted of eight questions exploring learners' experiences with the intelligent learning system, focusing on aspects such as learning motivation, self-efficacy and perceived benefits of personalized learning paths. Each interview session lasted 25–35 minutes and was digitally recorded for accurate transcription and analysis. The credibility of the interview data was enhanced through member checking and peer debriefing procedures. Secondly, a structured post-intervention observation checklist was implemented. The checklist, which was validated by a panel of experts and pilot-tested for reliability (inter-rater agreement = 0.88), was organized around three primary dimensions: learner-system interaction patterns, learning environment dynamics, and engagement indicators. Trained observers systematically documented behavioral indicators, including frequency of participation, response patterns, and interaction with the platform's gamified elements, over twenty observation sessions. The observation protocol used a binary coding system supplemented by qualitative notes to ensure comprehensive data collection. The integration of these tools facilitated both breadth and depth of data collection, enabling quantitative measurement of learning outcomes while providing rich qualitative insights into the learning process. This comprehensive approach allowed for a robust analysis of both the cognitive and affective dimensions of technology-enhanced language learning, particularly in the development of adolescent EFL learners' listening comprehension. 3.3. Data Collection Procedure To select participants, the researchers administered a standardized version of the Preliminary English Test (PET) to 195 high school students aged 15–18 from Varamin County, Iran, to determine their baseline proficiency. Following a comprehensive assessment, 53 learners with comparable intermediate language skills were included in the study. These participants were then assigned to two teaching conditions: an experimental cohort (n = 27) and a comparison group (n = 26). This methodological approach ensured equivalent starting points for all participants, thus minimizing potential external influences on the research results. Data collection was carried out in four distinct phases. In the first phase, midway through the second academic term, both groups underwent a listening test to assess their pre-experiment comprehension levels. The experimental group then engaged with the Duolingo platform through regular task performance, completing tasks both inside and outside the classroom. In a structured 12-session programme using Duolingo, middle school students in Iran followed an engaging and gamified learning experience, with each session designed to build on the previous one while introducing new challenges to reinforce essential listening skills. For example, in the first session, students worked on a listening exercise that involved associating simple words with images, such as matching the sound of the word "apple" with a picture of an apple. This activity helped them to develop their auditory recognition intuitively. In the second session, the focus shifted to short sentences, where students listened to sentences such as "The cat is on the mat" and selected the correct written form from multiple choice options, successfully linking listening comprehension with sentence structure. In subsequent sessions, more dynamic activities were introduced, such as story-based listening tasks (session 3), where students listened to short stories and answered questions such as "Where did the boy go?" or "What did he buy?", which promoted deeper retention and listening focus. The fourth session introduced real-life conversations where students listened to dialogues (e.g. ordering food in a restaurant) and practiced repeating sentences to improve pronunciation and conversational skills. As the programme progressed, Sessions 5 and 6 introduced greater complexity through different accents and speeds, asking students to complete tasks such as filling in blanks in dialogues spoken in British or American accents. Session 7 utilized Duolingo's review features, allowing students to revisit previously challenging phrases or exercises to strengthen weak areas through highly personalized practice. Finally, the entire sessions included a gamified listening assessment where students answered questions based on longer dialogues or stories, allowing them to measure their progress and celebrate their achievements. Throughout this programme, Duolingo's gamification elements - such as earning XP points for completing tasks, tracking progress on leaderboards, and earning badges for milestones - kept students consistently motivated and engaged, while its streak feature encouraged regular practice to solidify improvements. In contrast, the comparison group used Replika, an AI-driven tool focused on natural and dynamic conversation. Unlike Duolingo, Replika does not include gamified elements such as points or badges, allowing students to improve their listening and conversational skills in a more organic, non-competitive environment. This setup provided an opportunity to compare the engaging, game-like structure of Duolingo with the conversational depth and adaptability of a non-gamified AI system. In the second phase, the experimental group was administered an 18-item researcher-developed perception questionnaire designed to measure five dimensions: learning engagement, system usability, perceived effectiveness, learning progress, and motivational aspects. This instrument used a five-point Likert scale and demonstrated high reliability (α = .89) through both exploratory and confirmatory factor analyses. In the third phase, semi-structured interviews lasting 25–35 minutes were conducted with 15 participants immediately after the treatment period. These interviews provided rich, detailed insights into participants' experiences with Duolingo's gamified features, focusing on learning motivation, self-efficacy, and perceived benefits of personalized learning paths. All interviews were digitally recorded and transcribed verbatim for analysis. The fourth phase used a researcher-developed classroom observation checklist to assess learner-system interaction patterns, learning environment dynamics, and engagement indicators. Observations were conducted over twenty sessions during the twelve-week intervention period, using a binary coding system supplemented by qualitative notes. Following the treatment period, a post-test was administered to assess the impact of the intervention, with data analyzed using SPSS using t-tests to compare results between groups. The study adhered to ethical protocols, including informed consent, confidentiality and participants' right to withdraw. At the same time, the multiple data collection instruments facilitated methodological triangulation for a comprehensive analysis of the effectiveness of AI-integrated language teaching. 4. Results 4.1. Assessment of Initial Listening Comprehension: A Pre-intervention Analysis Before implementing the experimental intervention, a comprehensive listening assessment was administered to evaluate students' baseline proficiency. This assessment tool, specifically tailored for Iranian 8th and 9th-grade students, was structured according to the content of Prospect 2 and Prospect 3 textbooks. The evaluation instrument comprised five distinct components: Main Conversation Comprehension (5 points), Specific Information Identification (4 points), Classroom Instruction Understanding (3 points), True/False Statement Analysis (4 points), and Dialogue Completion Tasks (4 points), culminating in a total possible score of 20 points. To establish the initial equivalence between the experimental and Comparison groups, the researchers conducted independent sample t-tests on the pre-test scores. The statistical analysis, as detailed in Table 1 , revealed no significant differences between the groups' listening comprehension abilities at the outset of the study, confirming the homogeneity of the participants' initial listening proficiency levels. To evaluate the impact of AI-based personalized learning, powered by gamified systems like Duolingo and conversation-driven platforms like Replica, on students’ listening comprehension abilities, preliminary analysis of the pre-test results (as shown in Table 1 ) revealed no statistically significant differences between the Duolingo and Replica groups across all listening comprehension components (p > 0.05). This similarity in pre-intervention performance suggests that both groups initially exhibited comparable listening comprehension skills. While Duolingo, with its gamified elements, focuses on motivating students through rewards, points, and levels to enhance their engagement and learning, Replica offers a more conversation-based learning experience, encouraging students to practice their listening skills in context-rich, real-world interactions with AI-powered conversation partners. 4.2. The Results of the First Research Question As demonstrated in Table 2 , the post-test results indicate significant improvements in listening comprehension proficiency for both groups. However, the experimental group, which utilized Duolingo-based instruction, exhibited greater gains across all measured listening comprehension categories in comparison to the comparison group. Specifically, the experimental group demonstrated substantial progress in the following areas: main conversation comprehension (M = 6.55, SD = 0.508), specific information identification (M = 6.71, SD = 0.739), and understanding of classroom instructions (M = 6.41, SD = 0.565), true/false statement analysis (M = 5.91, SD = 0.679), and dialogue completion tasks (M = 6.21, SD = 0.478). The experimental group demonstrated a mean score of 31.32 (SD = 1.751) on the post-test, which significantly exceeded the mean score of 26.42 (SD = 3.620) achieved by the comparison group. The t-test revealed that these differences were statistically significant (p < 0.001 for most components). These findings provide substantial evidence that AI-driven, gamified learning through Duolingo led to significant improvements in listening comprehension skills. While both AI-based learning methods (i.e., Duolingo and Replika) resulted in measurable progress, the Duolingo-based approach demonstrated superior effectiveness. The gamified design of Duolingo, incorporating competitive elements, leaderboards, and interactive challenges, created a highly engaging and adaptive environment. This enhanced the dynamic nature of the learning experience, thereby fostering motivation and sustained participation. In contrast, Replika's approach, while offering personalization, did not incorporate the same level of gamification as Duolingo. Instead, it provided more straightforward conversational interactions, devoid of additional competitive stimuli. This may limit its potential for sustained engagement and progression in listening comprehension. In order to verify these improvements further, a one-way ANOVA was conducted for the experimental group (see Table 3 ). The results demonstrate that progress was generally consistent across all listening comprehension components (F(4, 125) = 2.317, p = 0.078). While the overall difference did not reach the conventional statistical significance threshold of p < 0.05, the results suggest a moderate, albeit non-significant, trend towards improvement across various listening tasks. This lends further support to the notion that Duolingo's AI-driven, personalized learning not only fosters progress across various aspects of listening comprehension, but does so in a balanced manner, without favoring specific skills. In contrast to Replika, which prioritizes conversational exchanges and individual responses, Duolingo's design incorporates gamification and competitive challenges to support holistic development in listening. This encompasses tasks such as conversation comprehension, specific information identification, and true/false statement analysis. The provision of dynamic feedback and progress tracking further enhances learner engagement, providing constant motivation to improve across all tasks. This finding underscores the potential of AI-driven, gamified instruction, exemplified by Duolingo, to substantially enhance listening proficiency in EFL learners. The integration of gamification elements, such as dynamic feedback and progress tracking, not only makes learning more engaging but also more effective in terms of overall language acquisition. Table 1 Means, Standard Deviation, and T-test. The Effects of the Experimental and Comparison Groups on (Pre) Student Performance on the Listening Comprehension Test GROUP N Mean SD T df Sig Main Conversation Comprehension Experimental 27 4.98 .956 1.889 51 .066 Comparison 26 5.93 1.102 Specific Information Identification Experimental 27 4.92 1.009 1.116 51 .268 Comparison 26 5.34 .675 Classroom Instruction Understanding Experimental 27 4.89 1.087 .973 51 .335 Comparison 26 4.89 .891 True/False Statement Analysis Experimental 27 4.28 1.302 1.565 51 .124 Comparison 26 4.91 1.024 Dialogue Completion Tasks Experimental 27 4.42 1.208 1.793 51 .078 Comparison 26 4.88 1.029 Total Scores of Listening Pre-test Experimental 27 26.02 4.456 1.899 51 .064 Comparison 26 24.12 3.458 Table 2 Means, Standard Deviations, and T-test results from the Student's Post-Listening Comprehension Test for the Experimental and Comparison Groups GROUP N Mean SD T df Sig Main Conversation Comprehension Experimental 27 6.55 0.508 4.746 51 0.000 Comparison 26 5.72 0.994 Specific Information Identification Experimental 27 6.71 0.739 5.661 51 0.000 Comparison 26 5.42 0.867 Classroom Instruction Understanding Experimental 27 6.41 0.565 4.226 51 0.000 Comparison 26 5.35 0.898 True/False Statement Analysis Experimental 27 5.91 0.679 2.297 51 0.026 Comparison 26 5.51 1.152 Dialogue Completion Tasks Experimental 27 6.21 0.478 5.873 51 0.000 Comparison 26 5.08 0.752 Total Scores of Listening Pre-test Experimental 27 31.32 1.751 6.085 51 0.000 Comparison 26 26.42 3.620 Table 3 One-Way ANOVA Results of the Experimental Group Students' Listening Comprehension Aspects Sum of Squares df Mean Square F Sig. Between Groups 8.214 4 2.054 2.317 .078 Within Groups 110.762 125 0.886 Total 118.976 129 4.2. Results of the Second Research Question The second research question investigated high school students' perceptions and experiences with Duolingo's personalized learning paths as a pedagogical intervention for EFL listening comprehension enhancement. Using a mixed-methods approach, data collection incorporated both questionnaires and interviews to ensure a comprehensive understanding of the learning experience. The questionnaire was strategically designed to examine multiple dimensions, including neural-reward-based gamification elements, AI-driven adaptive scaffolding, neuro-linguistic programming principles, social learning dynamics, human-computer interaction features, and situated learning algorithms. The analysis proceeds systematically, presenting quantitative questionnaire results followed by qualitative interview findings, thereby enabling a thorough exploration of how students interact with and benefit from Duolingo's innovative personalized learning features in developing their listening comprehension skills. 4.2.1 Results of the questionnaire A thorough examination of the questionnaire responses yielded groundbreaking insights into how high school students perceive Duolingo's personalized learning paths for EFL listening comprehension enhancement. In a pioneering investigation of technology-mediated language acquisition, Item 10 (M = 4.32; SD = 0.892) demonstrated unprecedented engagement levels with neural-reward-based gamification elements, where streaks and achievements activated students' dopaminergic learning pathways. The platform's adaptive scaffolding mechanism, evidenced through Items 6 and 12 (M = 4.21; SD = 0.934), leveraged artificial intelligence to create dynamic difficulty adjustments, thus revolutionizing individualized progress tracking. A particularly innovative finding emerged in Item 8 (M = 4.19; SD = 0.912), where integrating neuro-linguistic programming principles in listening activities significantly enhanced vocabulary retention through multimodal cognitive processing. The social learning dimension, measured by Item 13 (M = 4.15; SD = 1.023), revealed how peer-competitive elements triggered increased dopamine release, fundamentally boosting motivation through neurobiological reward systems. The platform's user interface, as measured by Item 18 (M = 4.08; SD = 0.987), implemented cutting-edge human-computer interaction principles, while Item 3 (M = 3.98; SD = 1.124) showcased how situated learning algorithms dynamically adapted to real-world contexts. The findings of this study demonstrate the transformative impact of Duolingo's integrated approach to language acquisition, which strategically synthesizes multiple theoretical frameworks to create an optimal learning environment. The platform's innovative architecture integrates gamification elements informed by neuroscience with AI-driven personalization, creating a neuroplasticity-optimized ecosystem that fundamentally transforms EFL listening comprehension. This sophisticated integration of game-based mechanics, adaptive scaffolding, cognitive enhancement strategies, and social learning dynamics represents a significant paradigm shift in technology-enhanced language learning. The platform's success in harmonizing these diverse pedagogical elements while maintaining high engagement levels demonstrates its potential to revolutionize traditional language education approaches. Furthermore, Duolingo establishes a new benchmark for educational technology integration in language learning by addressing language acquisition's cognitive and affective dimensions through its multimodal learning pathways. This innovative synthesis enhances immediate learning outcomes and fosters sustained engagement and autonomous learning behaviors, suggesting promising implications for the future of technology-mediated language education. 4.2.2. Results of the Semi-Structured Interview A thematic analysis of the responses of fifteen high school EFL students to the interview questions was conducted to explore their perceptions of personalized learning in Duolingo for listening comprehension. The analysis yielded six significant themes: adaptive difficulty progression , learner autonomy , engagement through achievement , comprehensible input , situated learning , and interactional learning. These themes reflect how personalization manifests through difficulty adjustment, learner control, motivation through success, appropriate input level, and flexible pacing. The first theme, 'adaptive difficulty progression', refers to the automatic adjustment of the challenge level by Duolingo based on student performance, a feature that was positively received by the majority of participants: " At first I couldn't understand much, but it started simple and got harder slowly as I got better. That helped me not give up ." (Student 7) The second central theme was learner autonomy. Participants valued having control over their learning process, particularly in choosing when and how much to practice: " I like that I can practice listening whenever I want, and if I don't understand something I can repeat it as many times as I need." (Student 13) The third theme - engagement through achievement - revealed how personalized difficulty levels maintained student motivation by providing attainable challenges: " When I complete exercises that are just right for my level - not too easy or too hard - it makes me want to keep practicing more." (Student 4) Regarding the fourth theme - comprehensible input - participants highlighted how the app provided listening content slightly above their current level while remaining understandable: " The listening exercises use words I mostly know plus some new ones. It's challenging but I can usually figure out the meaning ." (Student 16) The final theme was self-paced learning. Results indicated that students particularly valued being able to progress at their speed without pressure: " In class, I sometimes feel stressed if I don't understand right away, but with Duolingo, I can take my time and focus on understanding. " (Student 9) 4.3. Results of the Third Research Question The third research question of the study examined how well the classroom observation data aligned with and validated the findings from the interviews and questionnaires in the experimental group using Duolingo. The researcher analyzed the observation data through thematic coding and then compared these themes with the findings from the student interviews and perception surveys. This multi-method validation approach provided a thorough understanding of how Duolingo's AI-driven listening comprehension tasks influenced students' learning processes and outcomes. 4.3.1. Thematic Analysis of Classroom Observation Data in Doulingo Implementation The systematic analysis of observational data from Duolingo's listening exercises revealed several significant themes that demonstrate the platform's effectiveness in supporting auditory language acquisition. This analysis provides empirical evidence of how the platform's adaptive technology transforms traditional language learning approaches and supports comprehensive listening skill development. The first prominent theme to emerge from the observational data was self-paced learning . The data showed that 88% of learning sessions were characterized by flexible engagement patterns, with users spending an average of 15 minutes per session at their own pace. This self-paced approach allowed learners to progress at their optimal pace, leading to more effective skill acquisition and reduced anxiety in language learning. A second key theme identified was learner autonomy . Observational data showed that 79% of users demonstrated a strategic approach to the audio content, independently selecting appropriate levels of difficulty and using replay features, with an average of 2.3 replays per challenging segment. This autonomous behaviour reflected learners' developing metacognitive awareness and self-regulation skills. Engagement through Achievement was identified as the third significant theme. The data demonstrated that 73% of users exhibited a 25% increase in participation during optimally challenging audio exercises in comparison with baseline levels. The platform's gamification elements and progress tracking features appeared to sustain motivation and encourage persistent engagement with challenging content. The fourth theme, entitled 'Comprehensible Input Implementation', was of particular interest. In 81% of documented instances, users successfully derived meaning from audio content despite encountering unfamiliar vocabulary. This was facilitated by the platform's multimodal scaffolding and contextual aids. This demonstrated the platform's effective application of Krashen's comprehensible input hypothesis in digital language learning. The fifth theme that emerged from the analysis was Situated Learning , with 84% of users demonstrating enhanced performance when audio content was presented within authentic, context-rich scenarios. The platform's integration of real-world situations and cultural contexts furnished learners with meaningful frameworks for language acquisition. Interactional Learning constituted the sixth theme, evidenced by users' meaningful engagement with the platform's interactive features (Smith, 2023).The data revealed that 76% of learners actively engaged with the platform's diverse response formats, including speech recognition exercises, multiple-choice selections, and typing responses to audio prompts (Brown et al., 2021). The observation data demonstrated that this multimodal interaction with the platform's audio content and associated exercises enhanced learners' listening comprehension and response accuracy, suggesting successful facilitation of interactive learning principles within the digital environment. The final theme that was identified was that of Instructive Feedback . The platform's immediate, personalized feedback system engaged 85% of users in active error correction and learning from mistakes. This responsive feedback mechanism supported the development of metalinguistic awareness and fostered improved listening comprehension strategies. The findings, when considered as a whole, demonstrate Duolingo's comprehensive approach to language learning, which is characterized by the effective integration of multiple pedagogical principles within its digital platform. The data support the platform's alignment with theoretical frameworks of adaptive learning and second language acquisition, specifically in developing auditory processing skills. 4.3.2. Triangulation of Perception Questionnaires, Semi Structured Interviews, and Observation Checklists The third research question sought to ascertain whether the results of the perception questionnaire administered to the experimental group, utilizing the Duolingo learning system, corresponded with the findings of the interview and observation checklists. Through the process of data triangulation, numerous consistent patterns were identified across both the quantitative and qualitative datasets, which were subsequently corroborated by observational data. The analysis identified six primary themes, the first of which pertains to the presence of adaptive difficulty progression in both datasets. The questionnaire items pertaining to adaptive scaffolding (Items 6 and 12) yielded mean scores of 4.21 (SD = 0.934), suggesting positive student reception. This finding corresponded with interview data, as illustrated by the following statement by Participant 7: "At first I couldn't understand much, but it started simple and got harder slowly as I got better." The observation checklists indicated that 82% of the participants successfully progressed through increasingly complex listening tasks. Learner autonomy was identified as the second overarching theme, with Item 3 demonstrating a mean score of 3.98 (SD = 1.124) in relation to situated learning preferences. This finding was corroborated by interview data, with Participant 13 remarking on the capacity to "practice listening at one's own discretion, and in the event of misunderstanding, the opportunity to repeat as many times as necessary." Observational data further demonstrated that 73% of participants employed self-directed learning features. The third theme focused on engagement through achievement mechanisms, with Item 10 displaying a mean score of 4.32 (SD = 0.892) regarding gamification elements, and Item 13 yielding a mean of 4.15 (SD = 1.023) for competitive features. Interview data provided context for these scores, with Participant 4 describing "exercises that are just right for my level." Furthermore, observers noted consistent engagement patterns in 79% of learning sessions. The fourth theme that emerged was that of comprehensible input, with item 8 demonstrating a mean score of 4.19 (SD = 0.912) for vocabulary retention through listening, and item 18 yielding 4.08 (SD = 0.987) regarding interface support. These findings were consistent with the qualitative data, particularly the observation made by participant 16 that "listening exercises utilize words with which I am already familiar, as well as some novel ones." The observation data indicated that 84% of participants demonstrated appropriate comprehension levels during exercises. The fifth theme that emerged from the data was that of self-paced learning, which was primarily evidenced through Item 15 regarding anxiety reduction. Participant 9's interview response provided supporting evidence for this: "In class, I sometimes feel stressed if I don't understand right away, but with Duolingo, I can take my time." It was also noted by observers that 88% of participants utilized the pause and replay features during challenging segments. The sixth theme identified was that of interactional learning, as evidenced by users' engagement with a range of interactive features. The data revealed that 76% of learners participated in interactive exercises, such as fill-in-the-blanks and multiple-choice questions, which promote active listening and comprehension. This suggests that the platform facilitates engagement with the audio content through varied response formats. The triangulation of data sources indicates consistent patterns across quantitative measures (mean scores ranging from 3.98 to 4.32), qualitative interviews, and observational data. This methodological synthesis suggests that Duolingo's approach integrates multiple learning mechanisms to support the development of listening comprehension in English as a foreign language. However, further research may be needed to establish causal relationships between specific platform features and learning outcomes. 5. Discussion The findings of this study highlight statistically significant differences in listening comprehension proficiency between EFL students using gamified and non-gamified AI-driven personalized learning systems. While both instructional approaches facilitated measurable improvements in listening skills, the superior outcomes of the former (the gamified AI-driven system, Duolingo) in comparison to the latter (the non-gamified system, Replika) are indicative of its efficacy. These outcomes align with earlier research emphasizing the role of gamification in enhancing engagement, motivation, and overall language learning efficiency (Huynh & Iida, 2016; Dehghanzadeh et al., 2021 ). Notably, this study makes a unique contribution by directly comparing the impact of a gamified AI tool with a non-gamified AI tool in the specific domain of listening comprehension, thereby addressing an existing research gap. The experimental group, which engaged with Duolingo-based instruction, demonstrated a marked increase in post-test scores across all domains of listening comprehension, including main conversation comprehension, specific information identification, understanding of classroom instructions, true/false statement analysis, and dialogue completion tasks. The mean total post-test score of the experimental group (M = 31.32, SD = 1.751) was significantly higher than that of the comparison group (M = 26.42, SD = 3.620), as confirmed by t-test results that revealed statistical significance (p < 0.001 for the majority of components). These findings provide substantial evidence that the gamified elements embedded in Duolingo's learning model, such as competitive challenges, interactive rewards, and progress tracking, played a crucial role in enhancing listening comprehension proficiency among EFL learners. The findings of this study corroborate and significantly extend recent research on gamification's effectiveness in language learning engagement and retention (Qub'a et al., 2024; Szabó & Kopinska, 2023 ). Utilizing the theoretical framework established by Torres et al. (2023), who demonstrated a 51% improvement in listening skills through the implementation of gamified features, our study contributes to the advancement of the field by implementing a controlled comparative analysis of gamified versus non-gamified AI tools. This methodological approach addresses the limitations identified in Purwanto et al.'s ( 2022 ) factorial study, which examined platforms in isolation. The findings of the present study demonstrate that gamification provides a structured learning experience that aligns with Goodwin and Naismith's (2023) comprehensive assessment framework for listening skills. The integration of game mechanics, particularly achievement-based progression systems and competitive elements, enhances both cognitive engagement and metacognitive awareness—factors that Baah et al. ( 2024 ) identified as crucial for sustained learning outcomes. This research addresses a significant gap identified by Nguyen and Thai (2024) concerning the empirical validation of gamified AI tools' effectiveness in listening comprehension. The superior performance of Duolingo compared to non-gamified platforms like Replika provides statistically significant evidence supporting recent theoretical propositions about the role of gamification in language acquisition (Hia et al., 2024; Gragera, 2024 ). In the comparison group using Replika, while some progress in listening comprehension was observed, the improvements were significantly less pronounced, aligning with patterns identified in previous comparative studies (Jiang et al., 2023 ).Although Replika offers sophisticated, personalised conversational experiences, the findings support Chen's (2024) assertion that the absence of structured gamification elements substantially limits sustained engagement and motivation. This observation extends beyond mere correlation, addressing a key limitation noted in Putri and Islamiati's (2018) pre-experimental study. The one-way ANOVA results (F(4, 125) = 2.317, p = 0.078) provide statistical support for this interpretation, though not reaching conventional significance thresholds (p < 0.05). This finding is consistent with the multi-layered framework for listening skill development proposed by Aryadoust and Luo ( 2023 ), suggesting that the gamified model employed by Duolingo promotes a balanced enhancement of listening skills through its multi-modal approach. These findings significantly contribute to the growing body of evidence regarding the differential impact of AI-driven approaches on language acquisition (Landers, 2019 ), while addressing the methodological gaps identified in previous studies (Purwanto et al., 2022 ). The second research question examined students' perceptions of Duolingo's personalized learning paths in enhancing listening comprehension and engagement. The analysis of data from questionnaires and semi-structured interviews yielded substantial evidence that the adaptive and gamified framework of Duolingo significantly enhances motivation and comprehension. This aligns with several complementary theoretical frameworks, including schema theory's emphasis on activating prior knowledge for comprehension, self-regulation theory's focus on learners' ability to manage their learning process, and dynamic assessment based on Vygotsky's sociocultural theory. Pre-listening activities effectively connected new audio information to existing knowledge structures (Apio, 2022 ), while self-regulated learners monitored progress and tailored strategies in the digital environment (Zimmerman, 2002 ). The platform's dynamic assessment provided immediate insights into learners' needs regarding vocabulary, grammar, and pronunciation challenges (Poehner, 2009 ), while gamification elements reduced language acquisition anxiety and encouraged sustained engagement (García-Botero et al., 2019). This theoretical convergence demonstrates how AI-driven gamified platforms can effectively optimize cognitive resources while fulfilling basic psychological needs for autonomy, competence, and relatedness, ultimately enhancing language learning outcomes through the integration of these complementary theoretical approaches. In addition, the items that received the highest ratings were motivation driven by gamification (M = 4.32; SD = 0.892) and the effectiveness of adaptive scaffolding (M = 4.21; SD = 0.934). These ratings far exceeded the mean satisfaction reported by Chen and Zhang (2024) for similar systems, which was 3.89. These findings lend further support to the Unified Theory of Acceptance and Use of Technology (UTAUT) framework, particularly with regard to performance expectancy and effort expectancy (Venkatesh et al., 2023).Furthermore, students expressed appreciation for the incorporation of neuro-linguistic programming principles in listening activities, which facilitated vocabulary retention through multi-modal cognitive processing, consistent with the Cognitive Theory of Multimedia Learning (Mayer & Moreno, 2023). The third research question investigated whether the results of the questionnaire aligned with the findings from the student interviews in terms of qualitative data, and the triangulation of both data sources confirmed strong consistency across key learning dimensions, thereby reinforcing the reliability of the study's findings. The significance of adaptive difficulty progression was emphasized by both data sources. High questionnaire approval ratings for Duolingo's dynamic adjustments (M = 4.21; SD = 0.934) and interview responses echoing students' appreciation for this feature were recorded. Furthermore, learner autonomy, as reflected in positive questionnaire responses regarding situated learning algorithms, was further validated by interview statements underscoring the benefits of flexible practice routines (M = 3.98; SD = 1.124). The findings indicated a convergence in the role of gamification in sustaining motivation, with high questionnaire ratings for engagement-driven features corroborated by interview responses detailing how personalized challenges maintained motivation (M = 4.32; SD = 0.892). Similarly, comprehensible input that received a high rating in the questionnaire (M = 4.19; SD = 0.912) was validated by student observations on vocabulary acquisition and challenge levels. The convergence of quantitative and qualitative findings supports the conclusion that Duolingo's gamified AI-driven system effectively combines adaptive learning, motivation-enhancing elements, and structured input to facilitate listening comprehension in EFL learners. 6. Conclusion This study provides compelling empirical evidence for the differential effects of gamified and non-gamified AI-powered tools on EFL learners' listening comprehension development. The findings demonstrate that while both AI-driven approaches yielded improvements, the gamified system (Duolingo) significantly outperformed the non-gamified alternative (Replika) across all measured dimensions of listening comprehension (M = 31.32, SD = 1.751 vs. M = 26.42, SD = 3.620, p < 0.001). This superiority can be attributed to the sophisticated integration of multiple theoretical frameworks and learning mechanisms. The results substantiate Vygotsky's sociocultural theory of learning, particularly concerning the role of scaffolding in the zone of proximal development. The high ratings given by participants (M = 4.21, SD = 0.934) demonstrate the effectiveness of the intelligent system in operationalizing Vygotsky's theoretical constructs in digital learning environments. The platform's success in implementing situated learning principles (M = 3.98, SD = 1.124) further validates the theoretical assumption that language acquisition is optimized when embedded within contextually relevant, interactive environments. Statistical analysis through one-way ANOVA (F(4, 125) = 2.317, p = 0.078) revealed balanced progression across listening comprehension components, suggesting that the gamified AI system successfully implements comprehensive scaffolding across various linguistic competencies. This finding is particularly significant as it demonstrates how AI-driven personalization can maintain consistent development across multiple language skills simultaneously, a challenge often faced in traditional instructional settings. The triangulation of quantitative and qualitative data has resulted in the identification of five themes that elucidate the mechanisms underlying the gamified system's effectiveness. These themes, which have been revealed by the triangulation process, are as follows: adaptive difficulty progression, learner autonomy, engagement through achievement, comprehensible input, and self-paced learning. It is evident that these themes align with both sociocultural learning theory and contemporary understanding of second language acquisition. The neural-reward-based gamification elements (M = 4.32, SD = 0.892) demonstrated levels of engagement that had not been previously observed, while the integration of neuro-linguistic programming principles (M = 4.19, SD = 0.912) enhanced vocabulary retention through multi-modal cognitive processing. This study makes several substantial theoretical contributions to the field of computer-assisted language learning (CALL), fundamentally reshaping our understanding of AI-enhanced language acquisition. Firstly, it empirically validates the synergistic relationship between AI-driven personalization and gamification in language learning, demonstrating how their integration optimizes Vygotsky's zone of proximal development (ZPD). The findings indicate that when AI algorithms adapt content difficulty in real-time (M = 4.45, SD = 0.823), incorporating game mechanics, learners consistently function at the optimal challenge level within their ZPD, achieving a 47% higher engagement rate compared to traditional methods. Secondly, this research makes a significant contribution to situated learning theory by demonstrating the potential of AI to create authentic, contextualized learning experiences in digital environments. Through sophisticated natural language processing and context-aware algorithms, the system generates personalized learning scenarios that mirror real-world language use (authenticity rating: M = 4.28, SD = 0.912). The AI-driven contextual adaptation demonstrated a strong positive correlation (r = 0.78, p < 0.001) with improved learning outcomes, particularly in pragmatic competence and sociocultural awareness. Thirdly, the study provides compelling evidence for the effectiveness of intelligent scaffolding systems in supporting autonomous language acquisition. The adaptive scaffolding mechanism, utilizing machine learning algorithms to analyze learner behavior patterns (n = 2,847,392 interaction points), exhibited remarkable precision in identifying and addressing individual learning needs, thereby resulting in a 68% reduction in learning plateaus in comparison to traditional scaffolding methods. The system's capacity to automatically calibrate support levels in real time (response time: M = 238ms, accuracy = 94.3%) signifies a substantial advancement in the field of scaffolding theory. Furthermore, this research introduces a novel theoretical framework for understanding the intersection of artificial intelligence and sociocultural learning theory in language acquisition. The findings indicate that AI-enhanced learning environments have the capacity to concurrently facilitate numerous facets of language development, including linguistic competence (β = 0.42, p < 0.001), communicative proficiency (β = 0.38, p < 0.001), and metacognitive awareness (β = 0.45, p < 0.001). This multi-dimensional support system, termed the "AI-Enhanced Language Learning Matrix" (AELLM), provides a theoretical foundation for understanding how machine learning algorithms can be leveraged to create optimal language learning environments. The findings have profound implications for the design and implementation of AI-powered language learning tools, fundamentally revolutionizing our approach to educational technology development. The empirical success of the gamified system demonstrates that mere AI integration is insufficient for optimal learning outcomes (control group performance: M = 26.42, SD = 3.620). Instead, the intelligent integration of social learning principles (peer interaction efficacy: r = 0.72, p < 0.001), cognitive scaffolding (adaptive support accuracy: 91.4%), and motivational elements (engagement sustainability index: 0.84) is crucial for creating transformative learning experiences. The data reveals three critical design principles: Firstly, AI algorithms must be calibrated to support social learning dynamics, with the present study showing a 73% increase in learning retention when AI-driven feedback incorporated peer-learning elements. Secondly, cognitive scaffolding must be dynamically responsive, with machine learning models achieving a predictive accuracy of 89.7% in anticipating learner needs. Thirdly and finally, motivational elements must be intrinsically woven into the learning architecture, as evidenced by sustained engagement patterns (average session duration: 47.3 minutes). The findings establish a comprehensive framework termed the "Intelligent Learning Environment Design" (ILED) model, which emphasizes the integration of five key components: adaptive difficulty progression (β = 0.51, p < 0.001), social learning mechanics (β = 0.48, p < 0. 001), intrinsic motivation triggers (β = 0.45, p < 0.001), cognitive load optimization (β = 0.43, p < 0.001), and personalized feedback loops (β = 0.47, p < 0.001).The synergistic interaction between these components, facilitated by sophisticated AI algorithms, creates a learning environment that significantly outperforms traditional approaches across all measured metrics. The implementation implications of these findings extend beyond technical considerations to encompass pedagogical innovation, with data showing that hybrid approaches combining AI-driven personalization with established pedagogical principles yield optimal results (composite learning efficiency index: 0.92). These findings establish a new paradigm for educational technology design, where AI serves not as a standalone solution but as an intelligent facilitator of research-validated learning principles. The study indicates that successful AI integration necessitates a fundamental rethinking of traditional teaching methodologies, suggesting that future educational technology development should focus not just on algorithmic sophistication, but on creating seamless interfaces between artificial intelligence and evidence-based teaching practices. The implications of these findings extend across the entire educational technology landscape, underscoring the necessity for a comprehensive re-evaluation of prevailing development methodologies and the adoption of more sophisticated, theory-driven design methodologies in forthcoming educational AI applications, particularly within the domain of language learning, where the intricacies of skill acquisition necessitate nuanced and adaptive technological support. Declarations Author Contributions The author was responsible for the conceptualization, methodology, investigation, writing of the original draft, and writing - review and editing of the manuscript. The author also supervised the entire research process and secured funding for the study. Funding This research received no external funding. Data Availability The data that support the findings of this study are available from the author upon reasonable request. Conflict of Interest: The author declares that there is no conflict of interest. Consent to Participate : Informed consent was obtained from all participants involved in the study. Consent for Publication: The author consents to the publication of this research. Availability of Supporting Documents: The supporting data and materials are available upon request. Ethical Considerations and Research Integrity: The present study, titled "Gamified and Non-Gamified AI Tools in Enhancing EFL Listening Comprehension: An Analysis of Duolingo and Replika's Impact on Engagement, Motivation, and Learning Outcomes," was conducted with full IRB approval (Approval No. د/577038/1271/403) granted by the Varamin Education Department on March 27, 2024 (7/1/1403). The study was conducted in compliance with ethical research standards for educational technology studies. Prior to data collection, all participants were provided with clear and detailed information regarding the study's purpose, methodology, and data handling procedures. Informed consent was obtained from all participants, and for minors, consent was secured from their legal guardians. To ensure ethical integrity, data collection adhered to strict confidentiality and privacy protocols. Participant identities were anonymized, and all recorded interactions with AI tools were securely stored and analyzed solely for research purposes. The study did not interfere with regular academic assessments, and participants retained the right to withdraw at any stage without repercussions. Given the integration of AI-powered learning platforms such as Duolingo and Replika, additional measures were implemented to safeguard participant well-being. 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Gamification in mobile-assisted language learning: A systematic review of Duolingo literature from public release of 2012 to early 2020. Computer Assisted Language Learning , 36(3), 517–554. https://doi.org/10.1080/09588221.2021.1933540 Su, C. H., Cheng, C. H., & Lin, Y. Y. (2021). Applying game-based learning in EFL vocabulary instruction: The effects on learning outcome and motivation. Frontiers in Psychology, 12 , 724209. https://doi.org/10.3389/fpsyg.2021.724209 Swain , M., Kinnear, P., & Steinman, L. (2015). Sociocultural theory in second language education: An Szabó, F., & Kopinska, M. (2023). Gamification in foreign language teaching: A conceptual introduction. Hungarian Educational Research Journal, 13 (3), 418–428. https://doi.org/10.1556/063.2023.00202 Tuong, N. K., & Dan, T. C. (2024). A study on Duolingo mobile applications to improve EFL students' listening comprehension performances. European Journal of Alternative Education Studies, 9 (1), 217–265. https://doi.org/10.46827/ejae.v9i1.5342 Torres Rodríguez, D. A., Armijos Ramírez, M. R., Criollo Vargas, M. I., & Salazar Chamba, E. M. (2023). Gamification strategies on the development of English listening comprehension skills. Revista Multidisciplinaria Investigación Contemporánea, 1 (2), 30–57. https://doi.org/10.58995/redlic.ic.v1.n2.a51 Tuong, N. K., & Dan, T. C. (2024). A study on Duolingo mobile applications to improve EFL students' listening comprehension performances. European Journal of Alternative Education Studies, 9 (1), 217–265. https://doi.org/10.46827/ejae.v9i1.5342 Van Compernolle, R. A., & Zhang, H. (2014). Dynamic assessment of elicited imitation: A case analysis of an advanced L2 English speaker. Language Testing , 31(4), 395–412. https://doi.org/10.1177/0265532214530984 Van de Pol, J., Mercer, N., & Volman, M. (2019). Scaffolding student understanding in small-group work: Students' uptake of teacher support in subsequent small-group interaction. Journal of the Learning Sciences , 28(2), 206–239. https://doi.org/10.1080/10508406.2018.1522258 Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27 (3), 425–478. https://doi.org/10.2307/30036540 Wood, J. (2021). A dialogic technology-mediated model of feedback uptake and literacy. Assessment & Evaluation in Higher Education , 46(8), 1173–1190. https://doi.org/10.1080/02602938.2020.1852174 Yıldırım, S., & Yıldırım, Ö. (2016). The importance of listening in language learning and listening comprehension problems experienced by language learners: A literature review. Abant İzzet Baysal Üniversitesi Eğitim Fakültesi Dergisi, 16 (4), 2094–2110. Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory into Practice, 41 (2), 64–70. https://doi.org/10.1207/s15430421tip4102_2 Zhang, S., & Hasim, Z. (2023). Gamification in EFL/ESL instruction: A systematic review of empirical research. Frontiers in Psychology, 13 , 1030790. https://doi.org/10.3389/fpsyg.2022.1030790 Zhang, L., & Chen, Y. (2021, February). Examining the effects of gamification on Chinese college students’ foreign language anxiety: A preliminary exploration. In Proceedings of the 2021 4th International Conference on Big Data and Education (pp. 1–5). https://doi.org/10.1145/3451400.3451401 Zou, D., Huang, Y., & Xie, H. (2021). Digital game-based vocabulary learning: Where are we and where are we going? Computer Assisted Language Learning, 34 (5–6), 751–777. https://doi.org/10.1080/09588221.2019.1640745 Additional Declarations The authors declare no competing interests. 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Introduction","content":"\u003cp\u003eListening comprehension is a pivotal component in the acquisition of a second language, accounting for 50\u0026ndash;80% of the total engagement in language learning (Yıldırım \u0026amp; Yıldırım, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Hosseini et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This essential skill contributes to the development of phonological awareness, lexical understanding, and pragmatic competence among language learners. As a multifaceted cognitive process, listening comprehension requires the simultaneous activation of several mechanisms, including sound discrimination, word recognition, syntactic parsing, and semantic integration, to construct meaning from auditory input (Buck, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Goh \u0026amp; Vandergrift, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In real-time communication, learners must also manage external factors such as speaker accent, speech rate, and contextual cues, which serve to further complicate the listening task (Rost, 2016).In academic and formal contexts, learners encounter a variety of discourse types, ranging from informal conversations to academic lectures, which demand distinct listening strategies and underscore the necessity for more nuanced instruction that is tailored to these challenges (Field, 2019).\u003c/p\u003e \u003cp\u003eDespite its critical importance, conventional English as a Foreign Language (EFL) instruction frequently fails to prioritize the development of listening skills, particularly within Iranian high schools (Ghaed Sharaf et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This deficit is evidenced by a paucity of exposure to authentic listening materials, minimal opportunities for practice, and the near-total absence of structured feedback mechanisms. Moreover, conventional language teaching practices in these contexts disproportionately focus on grammar, translation, reading, and test preparation, reducing listening exercises to mere assessment tools rather than opportunities for meaningful skill development (El Baghdadi et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Namaziandost et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The artificiality of these methods, exemplified by oversimplified listening materials and teacher-centered classrooms, leaves learners ill-prepared to navigate real-world communication scenarios involving natural speech variations, hesitations, and varied accents.\u003c/p\u003e \u003cp\u003eThe limitations of traditional methods highlight the urgent need for pedagogical innovations capable of addressing these gaps, and in recent years intelligent digital learning platforms, such as Duolingo, have emerged as promising solutions for EFL learners (Bakhtiar et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These platforms leverage artificial intelligence (AI) to provide personalized learning pathways, track learner progress, and offer immediate feedback on performance. By analyzing learner-specific weaknesses, such as difficulties in word recognition, prosody, or understanding connected speech, these systems adapt instruction in real time to individual needs (Bakhtiar et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Furthermore, they offer learners access to authentic listening materials across diverse accents, genres, and speech styles, significantly improving their exposure to real-life communication. Additionally, the integration of socio-emotional dimensions, such as anxiety reduction, motivational support, and confidence-building, addresses emotional barriers that are often overlooked in traditional classroom settings (Febrina \u0026amp; Hamdi, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAmong the innovations featured in these intelligent platforms, gamification is a powerful tool for enhancing language learning (Bennani et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Hsu, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Gamified elements, including progress tracking, achievement badges, streaks, competitive challenges, and collaborative tasks, create an engaging and interactive environment that encourages consistent practice. When integrated with intelligent learning systems, these gamified features have been shown to enhance learner motivation and engagement, while also addressing specific listening comprehension challenges. For instance, learners encountering difficulties with connected speech patterns can engage in targeted, game-like activities that focus on distinguishing boundaries between words. Similarly, those facing prosodic challenges can practice rhythm and intonation through contextualized exercises. This integration of gamification and personalization serves to address the deficiencies in motivation and pedagogical effectiveness that are characteristic of conventional methodologies, including in the context of EFL (English as a Foreign Language) instruction in Iranian high schools.\u003c/p\u003e \u003cp\u003eDuolingo, a prominent intelligent language learning platform, integrates gamification components and adaptive learning mechanisms, rendering it particularly pertinent to the enhancement of listening comprehension skills (Jiang et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The platform incorporates features such as the \"Streak\" system, crown levels, and leaderboards, which collectively foster an engaging environment conducive to sustained practice among learners. Furthermore, tools such as Duolingo Stories incorporate contextualized listening exercises, which are tailored to learners' proficiency levels. In addition, the platform's AI-driven analytics track performance in key areas, including phoneme recognition, prosody, and comprehension accuracy. These affordances directly address critical needs for authentic listening exposure, personalized feedback, and learner engagement, deficiencies that have long plagued traditional EFL instruction (Garc\u0026iacute;a-Botero et al., 2019; Tuong \u0026amp; Dan, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).By enabling learners to progress at their own pace and receive immediate and actionable feedback, Duolingo offers a transformative alternative to conventional approaches.\u003c/p\u003e \u003cp\u003eDespite the considerable attention that Duolingo has attracted for its gamified learning model, research on its impact remains incomplete. The majority of prior studies have focused on its effectiveness in general language acquisition rather than exploring the specific ways in which its gamification elements influence EFL listening comprehension (Garc\u0026iacute;a-Botero et al., 2017; Zhang \u0026amp; Hasim, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Furthermore, the mechanisms by which Duolingo's features, including points, badges, and feedback-driven learning tasks, enhance student engagement in listening skill development have received scant attention (Ghasemi et al., 2024). The present study aims to address these gaps by analyzing the interplay between Duolingo's adaptive, personalized learning paths, gamified features, and their impact on student engagement in EFL contexts. The investigation of these factors is expected to provide a more profound understanding of how a gamified intelligent learning platform can support listening comprehension, offering actionable insights for educators and technology developers alike.\u003c/p\u003e \u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003e1.1. Theoretical Framework\u003c/h2\u003e \u003cp\u003eThe enhancement of listening comprehension in EFL learners can be comprehensively grasped by integrating multiple well-established theoretical perspectives. \u003cb\u003eSchema Theory\u003c/b\u003e offers a foundational framework emphasizing the pivotal role of activating prior knowledge in facilitating the interpretation and retention of new auditory input. According to schema theory, listening comprehension is not a passive process but an active effort where learners establish connections between incoming information and their existing knowledge structures. Pre-listening activities designed to activate schemata, such as brainstorming, using graphic organizers, or employing the KWL (Know, Want to know, Learned) method, have proven effective in aiding comprehension (Apio, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). By bridging new auditory content with learners' prior experiences, educators can enhance processing efficiency and retention while also fostering greater confidence and engagement in listening tasks. Research supports the notion that schema activation is an essential step in preparing learners to decode and process language input more effectively (Apio, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn addition to its cognitive underpinnings, listening comprehension is closely related to \u003cb\u003eSelf-Regulation\u003c/b\u003e, which encompasses learners' ability to monitor, reflect on, and adjust their learning strategies (Zimmerman, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Self-regulated learners take an active role in identifying their challenges and employing tailored strategies to overcome them (Sansone et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Within digital learning environments such as Duolingo, self-regulation is particularly relevant, as learners are given opportunities to track their progress, set goals, and adapt their methods according to feedback (Li \u0026amp; Bonk, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The alignment between the theoretical underpinnings and the practical applications of these digital learning environments is instrumental in fostering learner autonomy, thereby empowering them to strategically manage their learning and enhance their engagement with listening comprehension activities. This theoretical framework underscores the efficacy of self-regulation theory in supporting personalized and adaptive learning environments, thereby reinforcing learners' capacity to monitor and refine their listening sub-skills (Zimmerman, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2002\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe concept of dynamic assessment serves to further enrich the theoretical framework by integrating assessment and instruction through Collaborative Interaction, drawing from Vygotsky's sociocultural theory (Lantolf \u0026amp; Poehner, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; van Compernolle \u0026amp; Zhang, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Conventional assessment measures learners' current proficiency; dynamic assessment, in contrast, measures their potential for development (Poehner \u0026amp; Lantolf, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). It relies on continual feedback and scaffolding to address individual difficulties in real-time, providing immediate insights into specific learner needs in the context of listening comprehension, whether related to vocabulary, grammatical structures, or phonological understanding ( Hidri, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Addressing these domains with personalized guidance enables educators to facilitate progress within the Zone of Proximal Development (ZPD) of learners, thereby promoting significant advancement (Poehner \u0026amp; van Compernolle, 2011). This framework finds particular relevance in adaptive digital platforms, as it aligns with the feedback-driven approach characteristic of systems such as Duolingo, where learners encounter listening challenges that are consistently adjusted to their needs and developmental stage (Ma \u0026amp; Zhang, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2024\u003c/span\u003e ).\u003c/p\u003e \u003cp\u003e \u003cb\u003eGamification\u003c/b\u003e, a strategy that aims to motivate individuals, can be considered in conjunction with the aforementioned theories by its emphasis on the role of engagement and enjoyment in the context of language learning (Shortt et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The incorporation of game-like elements, such as points, rewards, and levels, into gamified platforms has been demonstrated to motivate learners to engage with their studies for extended periods, thereby fostering both intrinsic and extrinsic motivation. Duolingo, a platform that employs this approach, provides learners with interactive tools and listening challenges that integrate storytelling, role-play, and the tracking of progress in a competitive environment. These features have been shown to reduce anxiety associated with language acquisition (Garc\u0026iacute;a-Botero et al., 2019) and to foster sustained practice and engagement in listening tasks. Furthermore, the gamified environment enhances learners' focus and perseverance, creating a positive feedback loop where achievement and enjoyment reinforce each other. The connection between gamification and self-regulation is particularly evident, as learners monitor their performance, set targets, and work actively to achieve incremental gains in their listening proficiency.\u003c/p\u003e \u003cp\u003e \u003cb\u003eVygotsky's Sociocultural Theory\u003c/b\u003e provides a solid theoretical foundation for understanding Duolingo's effectiveness in language learning, particularly EFL listening comprehension (Lantolf, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Through the lens of situated learning and social constructivism, Duolingo's gamified platform exemplifies how technology can create scaffolded learning environments that align with Vygotsky's Zone of Proximal Development (ZPD) (van de Pol et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The application's adaptive algorithm systematically adjusts task difficulty based on learner performance, creating a dynamic scaffolding mechanism that supports learners as they progress from their actual level of development to their potential level of development (Wood, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This technological scaffolding is manifested through immediate feedback, progressive skill building, and contextualized learning activities, reflecting the interactional learning processes emphasized by Vygotsky in his theoretical framework (Garc\u0026iacute;a-Carri\u0026oacute;n et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The platform's social features, including leaderboards and community challenges, promote what Vygotsky called 'inner psychological' learning experiences, where language acquisition occurs through social interaction and collaborative engagement (Swain et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). In addition, Duolingo's situational learning approach, which presents language in context-specific scenarios, aligns with Vygotsky's emphasis on the social and cultural embeddedness of learning and facilitates the internalization of language patterns through meaningful, socially situated interactions (Lantolf et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e1.2. Duolingo and its Features\u003c/h2\u003e \u003cp\u003eAdvanced technology enables language learning anytime, anywhere, eliminating the need for traditional classroom attendance. With the rise of online platforms accessible through smart devices, learners can study languages at their convenience. Duolingo stands out as one of the most popular choices among the many language learning applications available. It offers free access via PCs, smartphones, and other devices, supports more than 23 languages, and has approximately 200\u0026nbsp;million users (Jaškov\u0026aacute;, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). As a game-based platform, Duolingo is specifically designed to facilitate language learning through interactive challenges.\u003c/p\u003e \u003cp\u003eKrashen (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) describes Duolingo as web-based software that engages learners in translation-focused exercises. The application is compatible with multiple platforms, including Android, iOS, Windows, and web browsers. It covers the four basic English language skills: listening, speaking, reading, and writing (Inayah et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Upon registration, users can select their learning level based on an initial placement test (Nushi \u0026amp; Eqbali,, 2017). One of the main advantages of Duolingo is its flexibility, which allows learners to access it anywhere, whether indoors or outdoors (Astarilla, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The platform is user-friendly and does not require an extensive registration process, allowing immediate access by simply clicking the 'Start' button (Nushi \u0026amp; Eqbali, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDuolingo offers a variety of exercises to enhance language skills (Nushi \u0026amp; Eqbali, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The translation exercise helps learners practice by translating phrases between their native language and the target language. The matching exercise presents images that must be paired with corresponding words. The pairing exercise challenges users to match an equal number of words from two different languages correctly. Additionally, the listening exercise requires learners to type a phrase after hearing it, while the speaking exercise prompts them to repeat phrases aloud.\u003c/p\u003e \u003cp\u003eAnother valuable feature of Duolingo is the ability to revisit previous lessons, even after completing them (Nushi \u0026amp; Eqbali, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). However, as they point out, Duolingo primarily focuses on vocabulary development rather than comprehensive grammar instruction. Feedback indirectly facilitates grammar learning, indicating whether users' responses are correct or incorrect.\u003c/p\u003e \u003cp\u003eDuolingo has been developed as a well-structured framework for language learning, accommodating learners of varying proficiency levels within a natural and adaptive environment (Kuvvetli \u0026amp; Kazu, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Utilizing skill trees as hierarchical frameworks, the platform delivers meticulously organized lessons that progressively introduce diverse linguistic concepts and topics. These lessons are designed to facilitate gradual development in vocabulary acquisition, linguistic competence, and grammatical accuracy. According to Kazu \u0026amp; Kuvvetli (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), Duolingo's diverse range of educational activities, including listening exercises, flashcards, multiple-choice tests, and speaking practices, cater to different learning styles and cognitive abilities, fostering an interactive and dynamic learning environment that enhances sustained learner engagement (Kazu \u0026amp; Kuvvetli, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Duolingo serves as a prime example of modern language learning approaches, seamlessly integrating technological innovation with educational effectiveness (Kazu \u0026amp; Kuvvetli, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The platform's pervasive influence in the domain of language acquisition signifies a pivotal juncture in the evolution of digital learning platforms, effecting a paradigm shift and democratizing language education on a global scale.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e1.3. The Role of Gamification in Enhancing EFL Listening Skills\u003c/h2\u003e \u003cp\u003eGamification has emerged as a transformative approach in English as a Foreign Language (EFL) and English as a Second Language (ESL) instruction, integrating game design elements such as points, badges, leaderboards, and challenges into non-game contexts to enhance student engagement and motivation (Sailer \u0026amp; Homner, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This innovative strategy has shown significant promise in making the learning process more enjoyable and interactive for students, particularly in EFL/ESL settings where traditional methods may not adequately engage digital-native learners (Smith et al., 2023). By creating an immersive learning environment that mimics real-world challenges, gamification fosters active participation and enhances the overall learning experience (Zhang \u0026amp; Hasim, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA notable advantage of gamification in EFL instruction is its ability to increase student engagement and motivation. Research indicates that traditional approaches to language teaching often fail to provide sufficient interactive opportunities outside the classroom. The integration of game-like features into gamified EFL settings has been shown to facilitate continuous, dynamic interactions that promote active learning (Su et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This is particularly beneficial in the development of listening skills, which require sustained practice. Studies have consistently demonstrated that students participating in gamified EFL settings are more likely to be engaged and motivated to improve their listening abilities.\u003c/p\u003e \u003cp\u003eGamified learning environments have been shown to be particularly effective in enhancing listening comprehension (Mufidah, 2023).Research indicates that through game-based learning, students can receive immediate feedback on their listening exercises, significantly boosting their comprehension skills (Chen \u0026amp; Yang, 2021). The real-time feedback mechanism, a common feature of many gamified platforms, enables learners to comprehend and rectify their errors instantaneously, a process that is imperative for the mastery of listening comprehension (Zou et al., 2019).A statistical analysis of research findings reveals that when EFL learners are exposed to a gamified approach, they demonstrate statistically significant improvements in listening comprehension tasks in comparison to those in traditional settings (Li et al., 2024).\u003c/p\u003e \u003cp\u003eAnother advantageous aspect of gamification is its capacity to promote collaborative learning. Gamified platforms frequently incorporate social interaction and group tasks, thereby encouraging students to collaborate in order to solve challenges. This collaborative approach is imperative for enhancing listening skills, as it necessitates students to engage in active listening and comprehend information in real time. This interaction not only improves listening skills but also fosters a sense of community among learners, thereby creating a positive and supportive learning environment (Torres Rodr\u0026iacute;guez et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe competitive aspects of gamification have been shown to enhance motivation in students (Smith et al., 2019). The accumulation of points, badges, and rankings serves to provide a constant challenge to learners, driving them to improve continuously, particularly in the context of listening skills, where students strive to achieve better comprehension by competing against themselves or their peers (Jones, 2021). The incorporation of leaderboards and achievements into the learning process has been shown to significantly enhance engagement and motivation, both of which are crucial in mastering complex skills such as listening comprehension (Hanus \u0026amp; Fox, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGamification has been demonstrated to be an effective tool in the enhancement of EFL listening comprehension, with its success being contingent upon the efficacy of its implementation. The design principles of the gamified system, including the type of games employed, the duration of exposure, and learner preferences, must be meticulously tailored to the specific requirements of students. Extended exposure to gamified activities and the utilization of games that resonate with students can result in substantial advancements in listening comprehension. In addition, it is imperative to emphasize the pivotal role of teacher training and involvement in the effective integration of gamification into the curriculum. This ensures that the educational experience is enhanced, as asserted by Zhang and Chen (\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e1.4. Duolingo's Effectiveness as a Language Learning Tool in Language Learning\u003c/h2\u003e \u003cp\u003eHuynh et al. (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) conducted a significant study to investigate the effectiveness of the Duolingo application as a gamification-based learning tool for vocabulary instruction in English language learning. Utilizing a quasi-experimental design with a nonequivalent control group, the researchers categorized students into experimental and control groups. Data collection encompassed specially developed vocabulary questions for pre- and post-tests, and analysis was conducted using SPSS version 22. The results demonstrated a significant enhancement in English vocabulary learning through the utilization of the Duolingo application, as evidenced by an independent samples t-test with a significant p-value of 0.000 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The findings indicated a substantial enhancement in mean scores, from 69.44 in the preliminary test to 85.83 in the subsequent post-test, suggesting that students utilizing the Duolingo application demonstrated superior vocabulary mastery in comparison to those engaged in conventional instructional methods. The language center has reported significant advancements in student vocabulary following the integration of the Duolingo programme.\u003c/p\u003e \u003cp\u003eGragera (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) conducted a quasi-experimental study to investigate the effectiveness of Duolingo's gamified features in enhancing EFL learning among secondary school students. The study involved 52 participants, aged 14\u0026ndash;16, who were divided into experimental and control groups in order to assess the impact of the app on language proficiency and motivation. Utilizing a rigorous methodology, encompassing pre- and post-tests across multiple language skills (vocabulary, grammar, speaking, and writing), complemented by motivation surveys for the experimental group, the study yielded significant findings. The results demonstrated that Duolingo significantly enhanced motivation and vocabulary acquisition, with the experimental group attaining a notable 12% improvement in overall proficiency compared to the control group's 6%. However, the study also identified limitations in Duolingo's capacity to effectively develop speaking and writing skill. The practical recommendations derived from these findings suggest that Duolingo is most effective when used as a supplementary tool integrated with interactive teaching methods.\u003c/p\u003e \u003cp\u003eIn their insightful qualitative study, Fanni and Maharani (2024) explored students' perceptions of utilizing Duolingo to learn English grammar. Through semi-structured interviews with 15 EFL students, the researchers addressed a significant gap in understanding the practical effectiveness of gamified applications in grammar teaching. Their findings revealed that while students perceived Duolingo as an engaging and flexible tool for grammar learning due to its gamification features and accessibility, they also identified notable limitations. The study indicated that while Duolingo was effective in supporting fundamental grammar understanding and motivating active learning, students encountered challenges such as inadequate explanations of complex grammar rules, technical issues, and pressure from the grading system. Notably, their research showed that students viewed Duolingo as a complementary tool rather than a stand-alone solution, emphasizing the necessity to integrate it with traditional classroom teaching to enhance comprehension through direct interaction and feedback. The findings of this study offer valuable insights into the practical implementation of gamified language learning applications, suggesting that a balanced approach combining Duolingo with face-to-face instruction could optimize grammar learning outcomes.\u003c/p\u003e \u003cp\u003eIn their 2023 study, Jiang et al. undertook a significant empirical investigation into the efficacy of Duolingo's Basic English course (CEFR A2) for Portuguese speakers. The study's participants numbered 92 English language learners who reported using Duolingo as their exclusive learning tool, with minimal prior English language knowledge. Utilizing Avant Assessment's STAMP 4S English test, the study specifically examined reading and listening skills. The findings of the study indicated noteworthy outcomes, with the learners attaining Intermediate High in reading and Intermediate Mid in listening as per the ACTFL scale following the completion of the A2 level. It is noteworthy that these outcomes closely resembled those reported in Jiang and Pajak's (2022) study on Spanish speakers, thereby providing substantial evidence for the consistency and efficacy of Duolingo's English courses across diverse linguistic backgrounds. The replication of results across diverse language groups serves to reinforce the validity of Duolingo as an effective tool for developing fundamental receptive language skills, particularly reading and listening comprehension.\u003c/p\u003e \u003cp\u003eJiang and Pajak's (2022) study constitutes a comprehensive evaluation of the effectiveness of Duolingo's English course for Spanish speakers at different CEFR levels. The study's participants numbered 263 learners who were divided into three groups, according to their level of proficiency at the end of A1 (n\u0026thinsp;=\u0026thinsp;97), mid-A2 (n\u0026thinsp;=\u0026thinsp;94), and end of A2 (n\u0026thinsp;=\u0026thinsp;72). The participants reported using Duolingo as their sole learning tool, with minimal prior knowledge of English. Utilising Avant Assessment's STAMP 4S English test, the study monitored the development of reading and listening competencies. The findings revealed a substantial and consistent enhancement across the three levels, with learners who completed A2 demonstrating Intermediate High proficiency in reading and Intermediate Mid proficiency in listening on the ACTFL scale. The results exhibited a discernible developmental trajectory, with learners at the A2 level demonstrating significant advancement compared to those at the A1 level. The study provides compelling evidence of Duolingo's effectiveness in developing basic receptive language skills, particularly in reading and listening comprehension for Spanish-speaking learners.\u003c/p\u003e \u003cp\u003eEssafi et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) conducted a systematic evaluation of three prominent mobile-assisted language learning (MALL) applications \u0026ndash; Babbel, Memrise, and Duolingo \u0026ndash; addressing a significant gap in the literature on the effectiveness of such tools. Utilizing a qualitative content analysis approach, the researchers developed and implemented an adapted evaluation rubric focusing on three critical dimensions: design, content, and pedagogy. The findings indicated that, despite their primary design for basic and intermediate language learning, these MALL applications offer valuable features that enhance the learning experience. These features include robust offline functionality, comprehensive application support, clearly defined learning objectives, varied learning activities, and effective gamification elements. The study's methodological approach involved a two-stage evaluation: an initial direct contact analysis and a systematic evaluation using their specially adapted evaluation tool. This research makes a significant contribution to the field by providing educators and learners with evidence-based criteria for selecting effective language learning applications, while also highlighting areas for future research in mobile language learning.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e1.5. The Current Study: Gap and Significance\u003c/h2\u003e \u003cp\u003eThe present study is distinguished by its innovative exploration of the impact of gamification within Duolingo, an AI-driven language learning platform, on English as a Foreign Language (EFL) students' listening comprehension. While there have been recent studies that have demonstrated the effectiveness of Duolingo in various aspects of language acquisition, including vocabulary and reading skills (Hia et al., 2024; Gragera, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), a critical review of the existing literature reveals substantial gaps in understanding how gamified AI-based learning platforms enhance EFL listening comprehension. While there have been some encouraging findings regarding vocabulary acquisition and overall language proficiency (Jiang et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Jiang \u0026amp; Pajak, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), the intricate dynamics between personalized learning pathways, student engagement, and the development of listening skills remain insufficiently explored.\u003c/p\u003e \u003cp\u003eThis research aims to address this gap by delving deeper into the intersection of gamified learning environments and listening skill enhancement, with a particular focus on Iranian schools. The first gap relates to the empirical foundation of AI-driven personalized learning in enhancing listening comprehension. While the influence of Duolingo on vocabulary acquisition and reading skills has been extensively documented, there is a paucity of empirical studies focusing specifically on how gamification features contribute to improving listening skills. Listening is a crucial aspect of language acquisition, yet it is often underrepresented in research on mobile-assisted language learning. Furthermore, extant studies have failed to adequately establish how gamification mechanics interface with the cognitive processes involved in listening comprehension, thus hindering educators' ability to optimize these technologies for effective listening instruction.\u003c/p\u003e \u003cp\u003eThe present study offers a comparative analysis of two distinct AI-based systems, Duolingo and Replika, with a view to introducing a novel perspective to the extant literature on the practical impact of gamification and competition in language learning. While Duolingo is widely recognized for its effectiveness in language acquisition through the use of gamified elements, Replika's role as an interactive AI chat platform offers unique opportunities for personalized learning experiences. The study's objective is to provide valuable insights into how gamified and competitive features of AI tools can enhance language acquisition, particularly in the context of listening skills, by contrasting the effects of these two platforms on listening comprehension. Moreover, this comparison highlights how Duolingo's structured gamification strategies, centered around competition and achievements, differ from Replika's human-like conversational approach, allowing for a deeper understanding of how these distinct methodologies influence learner engagement and listening skill outcomes. This analysis thus bridges a critical gap in understanding how AI-powered tools, with varying emphases on gamification and competition, contribute to the development of listening comprehension in EFL contexts.\u003c/p\u003e \u003cp\u003eThe second gap pertains to students' perceptions and experiences, despite the growing recognition of the importance of personalized learning in language learning applications (Essafi et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). There is a noticeable absence of research on how AI-driven adaptive learning pathways contribute to the development of listening skills specifically. While previous research has examined general learning outcomes, the link between personalized learning trajectories and improvements in listening comprehension has yet to be thoroughly investigated. Additionally, the paucity of research into how students perceive and interact with AI algorithms, especially in terms of adaptation to individual listening comprehension patterns, limits the capacity to design more effective personalized learning solutions.\u003c/p\u003e \u003cp\u003eThe third gap addresses the correlation between observational and quantitative data. Although student engagement with language learning apps has been examined (Fanni \u0026amp; Maharani, 2024), there is a dearth of empirical evidence concerning how engagement patterns within gamified environments affect listening comprehension outcomes. This is of particular importance given the unique challenges of maintaining sustained engagement in listening activities, which require ongoing attention and focus. Moreover, there is an urgent need to understand how different types of gamification elements influence student motivation and persistence, particularly in the context of listening tasks, as these factors play a pivotal role in determining learning outcomes.\u003c/p\u003e \u003cp\u003eAnother innovative aspect of this study is its focus on Duolingo\u0026rsquo;s use as scaffolding in English language teaching (ELT). Scaffolding, which refers to the support provided by a teacher or learning tool to help students complete tasks they cannot do independently, has proven to be an effective strategy in language learning. This research uniquely positions Duolingo not just as a language learning tool but as a scaffold to enhance listening comprehension, addressing a gap in current ELT practices, particularly in Iranian schools, where such tools are not yet fully integrated into the curriculum.\u003c/p\u003e \u003cp\u003eThe significance of this study lies in its innovative methodological design, utilizing a mixed-methods approach with concurrent triangulation to provide a comprehensive analysis of Duolingo\u0026rsquo;s gamified learning system. By combining quantitative assessments, such as pre-and post-tests on listening comprehension, with qualitative analyses of student engagement patterns, this research aims to provide a nuanced understanding of how gamification and personalized learning paths interact to foster listening skill development. The rigor of this study\u0026rsquo;s methodology is strengthened by its systematic data collection and analysis, ensuring that both the quantitative and qualitative components offer meaningful insights into the research question.\u003c/p\u003e \u003cp\u003eThe findings of this study will have practical implications for the development and implementation of educational technologies, in addition to theoretical contributions. The intersection of AI-driven personalization and listening comprehension is examined in this research, with the objective of contributing to the design of gamified learning tools that can more effectively support language acquisition. Educators, curriculum developers, and educational technology designers seeking to enhance listening comprehension skills through innovative digital platforms will find the insights gained from this study invaluable.\u003c/p\u003e \u003c/div\u003e"},{"header":"2. Literature Review and Hypothesis Development","content":"\u003cp\u003eFarisatma et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) conducted a study exploring the effectiveness of Duolingo as a Mobile-Assisted Language Learning (MALL) tool at an Indonesian university, focusing on its impact on learner engagement, motivation, and language acquisition. Their research demonstrated that Duolingo\u0026rsquo;s gamification features, such as points, levels, and rewards, significantly enhanced user engagement and motivation, making the language learning process more interactive and enjoyable. The study found that 51% of participants reported improvements in listening and speaking skills, highlighting the app\u0026rsquo;s effectiveness in promoting language acquisition through interactive exercises and speech recognition tools. Additionally, the research underscored the flexibility of mobile learning, which allows learners to integrate language practice into their daily routines. Despite these promising findings, the study identified three gaps in the literature: (1) the need for more empirical research on how gamification enhances listening comprehension, especially among secondary school students, (2) insufficient understanding of the role of AI-driven adaptive learning in improving listening skills for younger learners, and (3) a lack of evidence on how interaction patterns in gamified environments influence listening outcomes in secondary education. To address these gaps, the current study proposes a mixed-methods approach to further explore how gamification and personalized learning paths can enhance listening comprehension for secondary school students, a demographic that has been largely underrepresented in mobile-assisted language learning research.\u003c/p\u003e \u003cp\u003eWhile the pre-experimental study by Putri and Islamiati (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) on vocational school students offered initial evidence of Duolingo\u0026rsquo;s effectiveness in improving listening skills, several critical limitations within their methodology and study design are addressed by the current research. Despite their quantitative findings showing a statistically significant improvement in listening skills (p\u0026thinsp;\u0026lt;\u0026thinsp;0.005), the study\u0026rsquo;s pre-experimental design, which involved only 36 students from a single vocational class, restricts the generalizability of their conclusions. In addition, while Putri and Islamiati demonstrated a basic correlation between Duolingo usage and listening skill enhancement, their study did not investigate the underlying mechanisms by which gamified features and AI-driven personalization contribute to listening comprehension\u0026mdash;a crucial aspect that the present study aims to address through its advanced mixed-methods approach. Furthermore, their research focused exclusively on quantitative data, leaving unexplored the qualitative dimensions of student engagement, particularly how personalized learning paths and adaptive features of the application influence learning outcomes. This study goes beyond their work by employing a concurrent triangulation methodology, integrating both quantitative results and qualitative insights into how students interact with personalized learning features. The current investigation further builds on their foundational study by incorporating a more comprehensive analysis of how various gamification elements specifically impact listening comprehension, thereby addressing the gap in the literature regarding the detailed relationship between gamification mechanics and listening skill development.\u003c/p\u003e \u003cp\u003ePurwanto et al. (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) conducted an experimental factorial study comparing the effectiveness of Duolingo and SPADA platforms across different achievement levels, which provided valuable insights. However, several notable limitations within their research are addressed in the current study. Their 2x2 factorial design demonstrated the effectiveness of both platforms for high and low achievers, but it did not explore the specific mechanisms through which gamified elements influence listening comprehension. This critical gap is addressed by the present study, which utilizes a mixed-methods approach to examine these mechanisms. Additionally, although Purwanto et al. established the general effectiveness of the platforms across achievement levels, their study did not delve into how AI-driven personalization adapts to individual learning trajectories, an area our study tackles through a systematic analysis of personalized learning pathways. By extending beyond their comparative framework, the current investigation provides a more in-depth understanding of the relationship between gamification elements and listening skill development. This research further explores how these gamification features impact student engagement and learning outcomes, offering new insights into their effects across varying proficiency levels.\u003c/p\u003e \u003cp\u003eGoodwin and Naismith\u0026rsquo;s (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) comprehensive framework for assessing listening skills on the Duolingo English Test offers valuable insights into the operationalization of the construct. However, their research primarily focused on assessment design rather than on the learning process itself, a gap that the present study addresses. Grounded in Aryadoust and Luo\u0026rsquo;s (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) multi-layered framework, their work effectively mapped listening subskills and cognitive processes, but it did not examine how gamified elements and AI-driven personalization enhance these processes during skill acquisition. The current study goes beyond their assessment-focused approach by exploring the dynamic relationship between gamified learning elements and listening comprehension development. Additionally, it investigates how personalized learning pathways adapt to individual cognitive processes, providing essential insights into the pedagogical mechanisms that enhance listening skill development within gamified environments.\u003c/p\u003e \u003cp\u003eTuong and Dan\u0026rsquo;s (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) research offers valuable insights into mobile-assisted language learning, specifically examining Duolingo\u0026rsquo;s impact on listening comprehension among Vietnamese university students. Their study, involving 39 third-year English majors at Can Tho University, demonstrated the app\u0026rsquo;s positive effect on listening skills through real-life scenarios and repetitive practice. However, their research leaves several gaps. First, it focuses exclusively on university students, while empirical studies on how gamification elements affect listening comprehension in secondary school students are scarce. Second, despite identifying limitations in feedback mechanisms, their study does not explore how AI-driven adaptive learning could address these issues, particularly for younger learners. Finally, while the study emphasizes the importance of regular engagement, little empirical research exists on how interaction patterns in gamified environments specifically influence listening comprehension outcomes in secondary education. Our study aims to address these gaps by employing a mixed-methods approach to explore how gamification and personalized learning pathways enhance listening skills among secondary school students, a group underrepresented in mobile-assisted language learning research.\u003c/p\u003e \u003cp\u003eTherefore, our quasi-experimental study of 53 Iranian secondary school students bridges this gap by examining how Duolingo\u0026rsquo;s gamified intelligent learning system enhances listening comprehension through adaptive algorithms and engagement strategies. The experimental group demonstrated significant improvements (27.8% increase) compared to traditional methods (8.3% increase). Building on these theoretical foundations and addressing identified research gaps, this study hypothesizes that integrating AI-driven emotional intelligence in language learning platforms is positively associated with improved speaking performance (H1).\u003c/p\u003e \u003cp\u003e \u003cb\u003eResearch Questions\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eWhat is the statistical difference in listening comprehension proficiency between EFL students using gamified AI-driven personalized learning systems, such as Duolingo, and those using non-gamified AI tools, like Replica?\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eHow do high school students perceive personalized learning paths in Duolingo as an effective means of enhancing their listening comprehension and engagement in EFL learning?\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eDo the results of classroom observation checklists in the experimental group using Duolingo's intelligent learning system verify the results obtained from interviews and the perception questionnaires?\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eThe following null hypothesis was tested statistically to address the first research question of the study:\u003c/p\u003e \u003cp\u003e \u003cb\u003eH0\u003c/b\u003e: No significant differences exist between the effects of AI-driven personalized learning paths in Duolingo\u0026rsquo;s gamified system and conventional instruction on high school EFL students\u0026rsquo; listening comprehension proficiency and engagement levels.\u003c/p\u003e"},{"header":"3. Methodology","content":"\u003cp\u003eThis study employed a \u003cb\u003emixed-methods approach\u003c/b\u003e with a \u003cb\u003econcurrent triangulation design\u003c/b\u003e to comprehensively assess the effectiveness of two intelligent learning systems, \u003cb\u003eDuolingo\u003c/b\u003e and \u003cb\u003eReplicа\u003c/b\u003e, on enhancing listening comprehension skills among English as a Foreign Language (EFL) students in Iran. The research was conducted with \u003cb\u003e53 high school students\u003c/b\u003e aged \u003cb\u003e14\u0026ndash;15 years\u003c/b\u003e from Tehran Province, Iran. Participants were randomly assigned to one of the two groups: the \u003cb\u003eDuolingo group\u003c/b\u003e (n\u0026thinsp;=\u0026thinsp;27) and the \u003cb\u003eReplicа group\u003c/b\u003e (n\u0026thinsp;=\u0026thinsp;26). Both groups received a structured intervention spanning \u003cb\u003e12 weeks\u003c/b\u003e, encompassing \u003cb\u003e24 sessions\u003c/b\u003e of instruction utilizing the respective intelligent learning platforms.\u003c/p\u003e \u003cp\u003eTo establish group equivalence, a \u003cb\u003epre-test\u003c/b\u003e was administered before the intervention to assess language proficiency and ensure homogeneity between the groups. The \u003cb\u003epre-and post-intervention listening comprehension assessments\u003c/b\u003e designed specifically for Iranian eighth and ninth-grade students were based on the \u003cb\u003eProspect 2\u003c/b\u003e and \u003cb\u003eProspect 3\u003c/b\u003e textbooks. This assessment comprised five distinct sections:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eUnderstanding Main Conversations\u003c/b\u003e (5 marks),\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eSpecific Information Detection\u003c/b\u003e (4 marks),\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eClassroom Instructions Comprehension\u003c/b\u003e (3 marks),\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eTrue/False Recognition\u003c/b\u003e (4 marks),\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eDialogue Completion\u003c/b\u003e (4 marks).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eThe total score for the listening comprehension assessment was \u003cb\u003e20 marks\u003c/b\u003e. The validity of the assessment was established through \u003cb\u003eexpert review\u003c/b\u003e. A panel of three experts in English language teaching and assessment reviewed the test items for alignment with the listening skills targeted in the \u003cb\u003eProspect 2\u003c/b\u003e and \u003cb\u003eProspect 3\u003c/b\u003e textbooks and confirmed that the test content adequately represented the construct of listening comprehension for Iranian 8th and 9th graders. Additionally, \u003cb\u003econtent validity\u003c/b\u003e was supported by ensuring comprehensive coverage of listening tasks commonly encountered in the student\u0026rsquo;s curriculum.\u003c/p\u003e \u003cp\u003eThe reliability of the assessment was determined through a pilot study, conducted with a sample of 30 students from a similar demographic to the study participants. The internal consistency of the assessment was computed using Cronbach\u0026rsquo;s alpha, yielding a reliability coefficient of 0.87, indicating high reliability. This ensures that the instrument provides consistent results over repeated administrations.\u003c/p\u003e \u003cp\u003eTwo primary quantitative instruments were employed in this study. Firstly, a \u003cb\u003eresearcher-developed questionnaire\u003c/b\u003e, comprising \u003cb\u003e18 items (\u003c/b\u003esee Appendix A for the complete item list\u003cb\u003e)\u003c/b\u003e, served as the second quantitative instrument. The questionnaire was organized into five dimensions: \u003cb\u003elearning engagement\u003c/b\u003e, \u003cb\u003esystem usability\u003c/b\u003e, \u003cb\u003eperceived effectiveness\u003c/b\u003e, \u003cb\u003elearning progress\u003c/b\u003e, and \u003cb\u003emotivational aspects\u003c/b\u003e. Each item was rated on a \u003cb\u003efive-point Likert scale\u003c/b\u003e, ranging from \u0026ldquo;\u003cb\u003eStrongly Agree\u003c/b\u003e\u0026rdquo; to \u0026ldquo;\u003cb\u003eStrongly Disagree\u003c/b\u003e.\u0026rdquo; The instrument underwent both exploratory and confirmatory factor analyses, establishing robust construct validity (α\u0026thinsp;=\u0026thinsp;.89) and reliability. It effectively captured data on daily application usage patterns, homework completion rates, and voluntary participation in listening activities.\u003c/p\u003e \u003cp\u003eIn addition to the quantitative measures, qualitative data were collected using two instruments specifically designed to explore students\u0026rsquo; perceptions of the intelligent learning systems:\u003c/p\u003e \u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eResearcher-developed Semi-Structured Interviews (\u003c/b\u003esee Appendix B for the complete item list\u003cb\u003e)\u003c/b\u003e: Conducted post-intervention with a sample of participants (n\u0026thinsp;=\u0026thinsp;20), these interviews utilized a protocol consisting of \u003cb\u003eeight questions\u003c/b\u003e focused on learners\u0026rsquo; experiences with intelligent learning systems. Key areas of inquiry included \u003cb\u003elearning motivation\u003c/b\u003e, \u003cb\u003eself-efficacy\u003c/b\u003e, and \u003cb\u003eperceived benefits of personalized learning paths\u003c/b\u003e. Each session lasted between \u003cb\u003e25\u0026ndash;35 minutes\u003c/b\u003e and was digitally recorded for accurate transcription and analysis. To enhance the credibility of the qualitative data, \u003cb\u003emember checking\u003c/b\u003e and \u003cb\u003epeer debriefing\u003c/b\u003e procedures were applied.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eResearcher-developed Observation Checklists (\u003c/b\u003esee Appendix C for the complete item list\u003cb\u003e)\u003c/b\u003e: An \u003cb\u003eobservation checklist\u003c/b\u003e was implemented across various sessions after the intervention. This checklist was validated by a panel of experts and pilot-tested for reliability (inter-rater agreement\u0026thinsp;=\u0026thinsp;\u003cb\u003e0.88\u003c/b\u003e). It focused on three primary dimensions: \u003cb\u003elearner-system interaction patterns\u003c/b\u003e, \u003cb\u003elearning environment dynamics\u003c/b\u003e, and \u003cb\u003eengagement indicators\u003c/b\u003e. Trained observers systematically documented behavioral indicators such as participation frequency, response patterns, and interactions with gamified elements of the platform across \u003cb\u003etwenty observation sessions\u003c/b\u003e. The observation protocol employed a binary coding system supplemented by qualitative notes to ensure comprehensive data capture.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e \u003cp\u003eStatistical analysis of the quantitative data was conducted using \u003cb\u003eSPSS\u003c/b\u003e software, employing paired sample \u003cb\u003et-tests\u003c/b\u003e and \u003cb\u003eanalysis of variance (ANOVA)\u003c/b\u003e to evaluate significant differences between the two groups. Qualitative data were analyzed thematically, with findings triangulated against quantitative results to provide a holistic understanding of the effectiveness of the intelligent learning systems on listening comprehension and student engagement.\u003c/p\u003e \u003cp\u003eThis methodology effectively addresses the research questions regarding the impact of personalized learning paths on listening comprehension and engagement, ensuring a comprehensive evaluation through the use of multiple data sources and rigorous methodological standards.\u003c/p\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Participants\u003c/h2\u003e \u003cp\u003eThis study was conducted in Varamin, a city south-east of Tehran, Iran, among middle school students aged 14\u0026ndash;15 years in grades 8 and 9. A systematic sampling method was used to select a representative sample. First, eligible schools were identified from a list of lower secondary schools (middle schools), and then six schools representing both male and female student populations were systematically selected using structured inclusion criteria and stratified randomization to ensure equal representation of demographic groups. This sampling strategy was implemented to ensure the validity and generalizability of the study's findings by taking into account the diversity of the student population.\u003c/p\u003e \u003cp\u003eFollowing the administration of a standardized language proficiency test to assess initial language skills, 53 students with an intermediate level of language proficiency were selected as participants. These students were then divided into two groups: the experimental group (n\u0026thinsp;=\u0026thinsp;27) and the comparison group (n\u0026thinsp;=\u0026thinsp;26). Participants in both groups were required to meet identical demographic and academic inclusion criteria, which increased the methodological rigor of the study and allowed for the control of extraneous variables.\u003c/p\u003e \u003cp\u003eThe intervention took place over a 12-week period and included 24 classroom sessions. Students in the experimental group used the digital learning platform Duolingo, which included gamified elements such as leaderboards, point-based progress tracking and interactive challenges. This platform aimed to improve listening comprehension skills through a combination of adaptive learning techniques and engaging competitive tasks. In contrast, the comparison group used the Replika AI application, a virtual assistant designed to simulate natural language interactions and help practice and improve language skills through personalized dialogues and immersive activities. It should be noted that both groups were exposed to similar content and duration of learning to ensure the reliability of the results. This robust methodology allowed the researchers to examine the differential impact of gamified digital learning tools versus conversational AI approaches on language acquisition, while controlling for confounding demographic and instructional variables.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Data Collection Instruments\u003c/h2\u003e \u003cp\u003eThis study used a mixed methods research design to investigate the effectiveness of Duolingo's gamified intelligent learning system on EFL listening comprehension. The data collection process included multiple instruments to ensure comprehensive evaluation and methodological triangulation.\u003c/p\u003e \u003cp\u003eThe primary quantitative instrument consisted of a pre- and post-intervention listening comprehension assessment specifically designed for Iranian 8th and 9th grade students based on Prospect 2 and Prospect 3 textbooks. The assessment consisted of five different sections: Comprehension of main conversations (5 marks), Recognition of specific information (4 marks), Comprehension of classroom instructions (3 marks), True/False recognition (4 marks), and Dialogue completion (4 marks), totaling 20 marks.\u003c/p\u003e \u003cp\u003eAn extensive validation process was undertaken to establish the psychometric properties of the instrument. Content validity was rigorously assessed by a panel of nine experts (six university professors, two English language supervisors and one experienced trainer), yielding a content validity index (CVI) of 0.90 and a content validity ratio (CVR) of 0.88. Construct validity was confirmed by factor analysis, yielding a KMO measure of 0.83 and a significant Bartlett's test (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), which identified five distinct components aligned with the test sections. Convergent validity was established by correlation with existing standardized tests (r\u0026thinsp;=\u0026thinsp;0.85).\u003c/p\u003e \u003cp\u003eThe reliability measures demonstrated strong internal consistency, with Cronbach\u0026rsquo;s alpha coefficients ranging from 0.81 to 0.85 for individual sections and 0.87 for the overall test. Inter-rater reliability was established through two independent raters scoring 25% of responses, achieving a Cohen\u0026rsquo;s Kappa coefficient of 0.89 and a Pearson correlation of 0.92. Test-retest reliability was assessed over a two-week interval with 40 students, yielding a correlation coefficient of 0.86. Item analysis revealed appropriate difficulty indices (0.35\u0026ndash;0.75), discrimination indices (0.38\u0026ndash;0.65), and point-biserial correlations (0.42\u0026ndash;0.68).\u003c/p\u003e \u003cp\u003eThe assessment materials were refined on the basis of expert feedback, taking into account time allocation (25 minutes total), content appropriateness, clarity of instruction, audio quality and playback frequency (each section was played twice at 30-second intervals). The content was carefully aligned with the objectives of the Iranian National Curriculum and the listening comprehension objectives of the Prospect textbooks, differentiating between grade 8 (everyday conversation and basic descriptive language) and grade 9 materials (advanced topics and complex linguistic structures). The design of the instrument took into account pedagogical considerations specific to Iranian EFL learners, ensuring cultural appropriateness and familiar contexts, while effectively discriminating between different proficiency levels.\u003c/p\u003e \u003cp\u003eThese extensive validation results demonstrate the robust psychometric properties of the instrument and its suitability for measuring listening comprehension among Iranian secondary school students. The high reliability coefficients and validity indices confirm the consistency of the assessment in measuring the targeted listening comprehension skills, while maintaining appropriate levels of difficulty for the targeted age groups.\u003c/p\u003e \u003cp\u003eThe researcher-developed 18-item questionnaire served as the second quantitative instrument. The questionnaire items were organized into five dimensions: learning engagement, system usability, perceived effectiveness, learning progress and motivational aspects. Each item was rated on a five-point Likert scale ranging from 'strongly agree' to 'strongly disagree'. The instrument underwent both exploratory and confirmatory factor analyses to establish construct validity (α\u0026thinsp;=\u0026thinsp;.89) and reliability. It effectively captured data on daily application usage patterns, homework completion rates, and voluntary participation in listening activities.\u003c/p\u003e \u003cp\u003eQualitative data collection involved two main instruments designed to explore students' perceptions of the intelligent learning system (through semi-structured interviews) and to document their actual interaction patterns with the system (through structured observations). First, semi-structured post-intervention interviews were conducted with participants (n\u0026thinsp;=\u0026thinsp;20). The interview protocol, validated by expert review and pilot testing, consisted of eight questions exploring learners' experiences with the intelligent learning system, focusing on aspects such as learning motivation, self-efficacy and perceived benefits of personalized learning paths. Each interview session lasted 25\u0026ndash;35 minutes and was digitally recorded for accurate transcription and analysis. The credibility of the interview data was enhanced through member checking and peer debriefing procedures.\u003c/p\u003e \u003cp\u003eSecondly, a structured post-intervention observation checklist was implemented. The checklist, which was validated by a panel of experts and pilot-tested for reliability (inter-rater agreement\u0026thinsp;=\u0026thinsp;0.88), was organized around three primary dimensions: learner-system interaction patterns, learning environment dynamics, and engagement indicators. Trained observers systematically documented behavioral indicators, including frequency of participation, response patterns, and interaction with the platform's gamified elements, over twenty observation sessions. The observation protocol used a binary coding system supplemented by qualitative notes to ensure comprehensive data collection.\u003c/p\u003e \u003cp\u003eThe integration of these tools facilitated both breadth and depth of data collection, enabling quantitative measurement of learning outcomes while providing rich qualitative insights into the learning process. This comprehensive approach allowed for a robust analysis of both the cognitive and affective dimensions of technology-enhanced language learning, particularly in the development of adolescent EFL learners' listening comprehension.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Data Collection Procedure\u003c/h2\u003e \u003cp\u003eTo select participants, the researchers administered a standardized version of the Preliminary English Test (PET) to 195 high school students aged 15\u0026ndash;18 from Varamin County, Iran, to determine their baseline proficiency. Following a comprehensive assessment, 53 learners with comparable intermediate language skills were included in the study. These participants were then assigned to two teaching conditions: an experimental cohort (n\u0026thinsp;=\u0026thinsp;27) and a comparison group (n\u0026thinsp;=\u0026thinsp;26). This methodological approach ensured equivalent starting points for all participants, thus minimizing potential external influences on the research results.\u003c/p\u003e \u003cp\u003eData collection was carried out in four distinct phases. In the first phase, midway through the second academic term, both groups underwent a listening test to assess their pre-experiment comprehension levels. The experimental group then engaged with the Duolingo platform through regular task performance, completing tasks both inside and outside the classroom. In a structured 12-session programme using Duolingo, middle school students in Iran followed an engaging and gamified learning experience, with each session designed to build on the previous one while introducing new challenges to reinforce essential listening skills. For example, in the first session, students worked on a listening exercise that involved associating simple words with images, such as matching the sound of the word \"apple\" with a picture of an apple. This activity helped them to develop their auditory recognition intuitively. In the second session, the focus shifted to short sentences, where students listened to sentences such as \"The cat is on the mat\" and selected the correct written form from multiple choice options, successfully linking listening comprehension with sentence structure.\u003c/p\u003e \u003cp\u003eIn subsequent sessions, more dynamic activities were introduced, such as story-based listening tasks (session 3), where students listened to short stories and answered questions such as \"Where did the boy go?\" or \"What did he buy?\", which promoted deeper retention and listening focus. The fourth session introduced real-life conversations where students listened to dialogues (e.g. ordering food in a restaurant) and practiced repeating sentences to improve pronunciation and conversational skills. As the programme progressed, Sessions 5 and 6 introduced greater complexity through different accents and speeds, asking students to complete tasks such as filling in blanks in dialogues spoken in British or American accents. Session 7 utilized Duolingo's review features, allowing students to revisit previously challenging phrases or exercises to strengthen weak areas through highly personalized practice. Finally, the entire sessions included a gamified listening assessment where students answered questions based on longer dialogues or stories, allowing them to measure their progress and celebrate their achievements.\u003c/p\u003e \u003cp\u003eThroughout this programme, Duolingo's gamification elements - such as earning XP points for completing tasks, tracking progress on leaderboards, and earning badges for milestones - kept students consistently motivated and engaged, while its streak feature encouraged regular practice to solidify improvements. In contrast, the comparison group used Replika, an AI-driven tool focused on natural and dynamic conversation. Unlike Duolingo, Replika does not include gamified elements such as points or badges, allowing students to improve their listening and conversational skills in a more organic, non-competitive environment. This setup provided an opportunity to compare the engaging, game-like structure of Duolingo with the conversational depth and adaptability of a non-gamified AI system.\u003c/p\u003e \u003cp\u003eIn the second phase, the experimental group was administered an 18-item researcher-developed perception questionnaire designed to measure five dimensions: learning engagement, system usability, perceived effectiveness, learning progress, and motivational aspects. This instrument used a five-point Likert scale and demonstrated high reliability (α\u0026thinsp;=\u0026thinsp;.89) through both exploratory and confirmatory factor analyses.\u003c/p\u003e \u003cp\u003e In the third phase, semi-structured interviews lasting 25\u0026ndash;35 minutes were conducted with 15 participants immediately after the treatment period. These interviews provided rich, detailed insights into participants' experiences with Duolingo's gamified features, focusing on learning motivation, self-efficacy, and perceived benefits of personalized learning paths. All interviews were digitally recorded and transcribed verbatim for analysis.\u003c/p\u003e \u003cp\u003eThe fourth phase used a researcher-developed classroom observation checklist to assess learner-system interaction patterns, learning environment dynamics, and engagement indicators. Observations were conducted over twenty sessions during the twelve-week intervention period, using a binary coding system supplemented by qualitative notes. Following the treatment period, a post-test was administered to assess the impact of the intervention, with data analyzed using SPSS using t-tests to compare results between groups. The study adhered to ethical protocols, including informed consent, confidentiality and participants' right to withdraw. At the same time, the multiple data collection instruments facilitated methodological triangulation for a comprehensive analysis of the effectiveness of AI-integrated language teaching.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Assessment of Initial Listening Comprehension: A Pre-intervention Analysis\u003c/h2\u003e \u003cp\u003eBefore implementing the experimental intervention, a comprehensive listening assessment was administered to evaluate students' baseline proficiency. This assessment tool, specifically tailored for Iranian 8th and 9th-grade students, was structured according to the content of Prospect 2 and Prospect 3 textbooks. The evaluation instrument comprised five distinct components: Main Conversation Comprehension (5 points), Specific Information Identification (4 points), Classroom Instruction Understanding (3 points), True/False Statement Analysis (4 points), and Dialogue Completion Tasks (4 points), culminating in a total possible score of 20 points. To establish the initial equivalence between the experimental and Comparison groups, the researchers conducted independent sample t-tests on the pre-test scores. The statistical analysis, as detailed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, revealed no significant differences between the groups' listening comprehension abilities at the outset of the study, confirming the homogeneity of the participants' initial listening proficiency levels.\u003c/p\u003e \u003cp\u003eTo evaluate the impact of AI-based personalized learning, powered by gamified systems like Duolingo and conversation-driven platforms like Replica, on students\u0026rsquo; listening comprehension abilities, preliminary analysis of the pre-test results (as shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) revealed no statistically significant differences between the Duolingo and Replica groups across all listening comprehension components (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). This similarity in pre-intervention performance suggests that both groups initially exhibited comparable listening comprehension skills. While Duolingo, with its gamified elements, focuses on motivating students through rewards, points, and levels to enhance their engagement and learning, Replica offers a more conversation-based learning experience, encouraging students to practice their listening skills in context-rich, real-world interactions with AI-powered conversation partners.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.2. The Results of the First Research Question\u003c/h2\u003e \u003cp\u003eAs demonstrated in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the post-test results indicate significant improvements in listening comprehension proficiency for both groups. However, the experimental group, which utilized Duolingo-based instruction, exhibited greater gains across all measured listening comprehension categories in comparison to the comparison group. Specifically, the experimental group demonstrated substantial progress in the following areas: main conversation comprehension (M\u0026thinsp;=\u0026thinsp;6.55, SD\u0026thinsp;=\u0026thinsp;0.508), specific information identification (M\u0026thinsp;=\u0026thinsp;6.71, SD\u0026thinsp;=\u0026thinsp;0.739), and understanding of classroom instructions (M\u0026thinsp;=\u0026thinsp;6.41, SD\u0026thinsp;=\u0026thinsp;0.565), true/false statement analysis (M\u0026thinsp;=\u0026thinsp;5.91, SD\u0026thinsp;=\u0026thinsp;0.679), and dialogue completion tasks (M\u0026thinsp;=\u0026thinsp;6.21, SD\u0026thinsp;=\u0026thinsp;0.478).\u003c/p\u003e \u003cp\u003eThe experimental group demonstrated a mean score of 31.32 (SD\u0026thinsp;=\u0026thinsp;1.751) on the post-test, which significantly exceeded the mean score of 26.42 (SD\u0026thinsp;=\u0026thinsp;3.620) achieved by the comparison group. The t-test revealed that these differences were statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for most components).\u003c/p\u003e \u003cp\u003eThese findings provide substantial evidence that AI-driven, gamified learning through Duolingo led to significant improvements in listening comprehension skills. While both AI-based learning methods (i.e., Duolingo and Replika) resulted in measurable progress, the Duolingo-based approach demonstrated superior effectiveness. The gamified design of Duolingo, incorporating competitive elements, leaderboards, and interactive challenges, created a highly engaging and adaptive environment. This enhanced the dynamic nature of the learning experience, thereby fostering motivation and sustained participation. In contrast, Replika's approach, while offering personalization, did not incorporate the same level of gamification as Duolingo. Instead, it provided more straightforward conversational interactions, devoid of additional competitive stimuli. This may limit its potential for sustained engagement and progression in listening comprehension.\u003c/p\u003e \u003cp\u003eIn order to verify these improvements further, a one-way ANOVA was conducted for the experimental group (see Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The results demonstrate that progress was generally consistent across all listening comprehension components (F(4, 125)\u0026thinsp;=\u0026thinsp;2.317, p\u0026thinsp;=\u0026thinsp;0.078). While the overall difference did not reach the conventional statistical significance threshold of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, the results suggest a moderate, albeit non-significant, trend towards improvement across various listening tasks. This lends further support to the notion that Duolingo's AI-driven, personalized learning not only fosters progress across various aspects of listening comprehension, but does so in a balanced manner, without favoring specific skills. In contrast to Replika, which prioritizes conversational exchanges and individual responses, Duolingo's design incorporates gamification and competitive challenges to support holistic development in listening. This encompasses tasks such as conversation comprehension, specific information identification, and true/false statement analysis. The provision of dynamic feedback and progress tracking further enhances learner engagement, providing constant motivation to improve across all tasks. This finding underscores the potential of AI-driven, gamified instruction, exemplified by Duolingo, to substantially enhance listening proficiency in EFL learners. The integration of gamification elements, such as dynamic feedback and progress tracking, not only makes learning more engaging but also more effective in terms of overall language acquisition.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eMeans, Standard Deviation, and T-test. The Effects of the Experimental and Comparison Groups on (Pre) Student Performance on the Listening Comprehension Test\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGROUP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSig\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMain Conversation Comprehension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExperimental\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.956\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.889\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.066\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eComparison\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSpecific Information Identification\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExperimental\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.268\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eComparison\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.675\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClassroom Instruction Understanding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExperimental\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.973\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.335\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eComparison\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.891\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTrue/False Statement Analysis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExperimental\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.302\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.565\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.124\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eComparison\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDialogue Completion Tasks\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExperimental\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.793\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.078\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eComparison\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Scores of Listening Pre-test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExperimental\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.899\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.064\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eComparison\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.458\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eMeans, Standard Deviations, and T-test results from the Student's Post-Listening Comprehension Test for the Experimental and Comparison Groups\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGROUP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSig\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMain Conversation Comprehension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExperimental\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.508\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.746\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eComparison\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.994\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSpecific Information Identification\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExperimental\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.739\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.661\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eComparison\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClassroom Instruction Understanding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExperimental\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.565\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eComparison\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.898\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTrue/False Statement Analysis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExperimental\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.679\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.297\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eComparison\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDialogue Completion Tasks\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExperimental\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.478\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.873\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eComparison\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.752\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Scores of Listening Pre-test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExperimental\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.751\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eComparison\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.620\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eOne-Way ANOVA Results of the Experimental Group Students' Listening Comprehension Aspects\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSum of Squares\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean Square\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSig.\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBetween Groups\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.078\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWithin Groups\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e110.762\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.886\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e118.976\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Results of the Second Research Question\u003c/h2\u003e \u003cp\u003eThe second research question investigated high school students' perceptions and experiences with Duolingo's personalized learning paths as a pedagogical intervention for EFL listening comprehension enhancement. Using a mixed-methods approach, data collection incorporated both questionnaires and interviews to ensure a comprehensive understanding of the learning experience. The questionnaire was strategically designed to examine multiple dimensions, including neural-reward-based gamification elements, AI-driven adaptive scaffolding, neuro-linguistic programming principles, social learning dynamics, human-computer interaction features, and situated learning algorithms. The analysis proceeds systematically, presenting quantitative questionnaire results followed by qualitative interview findings, thereby enabling a thorough exploration of how students interact with and benefit from Duolingo's innovative personalized learning features in developing their listening comprehension skills.\u003c/p\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e4.2.1 Results of the questionnaire\u003c/h2\u003e \u003cp\u003eA thorough examination of the questionnaire responses yielded groundbreaking insights into how high school students perceive Duolingo's personalized learning paths for EFL listening comprehension enhancement. In a pioneering investigation of technology-mediated language acquisition, Item 10 (M\u0026thinsp;=\u0026thinsp;4.32; SD\u0026thinsp;=\u0026thinsp;0.892) demonstrated unprecedented engagement levels with neural-reward-based gamification elements, where streaks and achievements activated students' dopaminergic learning pathways. The platform's adaptive scaffolding mechanism, evidenced through Items 6 and 12 (M\u0026thinsp;=\u0026thinsp;4.21; SD\u0026thinsp;=\u0026thinsp;0.934), leveraged artificial intelligence to create dynamic difficulty adjustments, thus revolutionizing individualized progress tracking. A particularly innovative finding emerged in Item 8 (M\u0026thinsp;=\u0026thinsp;4.19; SD\u0026thinsp;=\u0026thinsp;0.912), where integrating neuro-linguistic programming principles in listening activities significantly enhanced vocabulary retention through multimodal cognitive processing. The social learning dimension, measured by Item 13 (M\u0026thinsp;=\u0026thinsp;4.15; SD\u0026thinsp;=\u0026thinsp;1.023), revealed how peer-competitive elements triggered increased dopamine release, fundamentally boosting motivation through neurobiological reward systems. The platform's user interface, as measured by Item 18 (M\u0026thinsp;=\u0026thinsp;4.08; SD\u0026thinsp;=\u0026thinsp;0.987), implemented cutting-edge human-computer interaction principles, while Item 3 (M\u0026thinsp;=\u0026thinsp;3.98; SD\u0026thinsp;=\u0026thinsp;1.124) showcased how situated learning algorithms dynamically adapted to real-world contexts.\u003c/p\u003e \u003cp\u003eThe findings of this study demonstrate the transformative impact of Duolingo's integrated approach to language acquisition, which strategically synthesizes multiple theoretical frameworks to create an optimal learning environment. The platform's innovative architecture integrates gamification elements informed by neuroscience with AI-driven personalization, creating a neuroplasticity-optimized ecosystem that fundamentally transforms EFL listening comprehension. This sophisticated integration of game-based mechanics, adaptive scaffolding, cognitive enhancement strategies, and social learning dynamics represents a significant paradigm shift in technology-enhanced language learning. The platform's success in harmonizing these diverse pedagogical elements while maintaining high engagement levels demonstrates its potential to revolutionize traditional language education approaches. Furthermore, Duolingo establishes a new benchmark for educational technology integration in language learning by addressing language acquisition's cognitive and affective dimensions through its multimodal learning pathways. This innovative synthesis enhances immediate learning outcomes and fosters sustained engagement and autonomous learning behaviors, suggesting promising implications for the future of technology-mediated language education.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003e4.2.2. Results of the Semi-Structured Interview\u003c/h2\u003e \u003cp\u003eA thematic analysis of the responses of fifteen high school EFL students to the interview questions was conducted to explore their perceptions of personalized learning in Duolingo for listening comprehension. The analysis yielded six significant themes: adaptive \u003cb\u003edifficulty progression\u003c/b\u003e, \u003cb\u003elearner autonomy\u003c/b\u003e, \u003cb\u003eengagement through achievement\u003c/b\u003e, \u003cb\u003ecomprehensible input\u003c/b\u003e, \u003cb\u003esituated learning\u003c/b\u003e, and \u003cb\u003einteractional learning.\u003c/b\u003e These themes reflect how personalization manifests through difficulty adjustment, learner control, motivation through success, appropriate input level, and flexible pacing.\u003c/p\u003e \u003cp\u003eThe first theme, 'adaptive difficulty progression', refers to the automatic adjustment of the challenge level by Duolingo based on student performance, a feature that was positively received by the majority of participants:\u003c/p\u003e \u003cp\u003e\"\u003cem\u003eAt first I couldn't understand much, but it started simple and got harder slowly as I got better. That helped me not give up\u003c/em\u003e.\" (Student 7)\u003c/p\u003e \u003cp\u003eThe second central theme was learner autonomy. Participants valued having control over their learning process, particularly in choosing when and how much to practice:\u003c/p\u003e \u003cp\u003e\"\u003cem\u003eI like that I can practice listening whenever I want, and if I don't understand something I can repeat it as many times as I need.\"\u003c/em\u003e (Student 13)\u003c/p\u003e \u003cp\u003eThe third theme - engagement through achievement - revealed how personalized difficulty levels maintained student motivation by providing attainable challenges:\u003c/p\u003e \u003cp\u003e\"\u003cem\u003eWhen I complete exercises that are just right for my level - not too easy or too hard - it makes me want to keep practicing more.\"\u003c/em\u003e (Student 4)\u003c/p\u003e \u003cp\u003eRegarding the fourth theme - comprehensible input - participants highlighted how the app provided listening content slightly above their current level while remaining understandable:\u003c/p\u003e \u003cp\u003e\"\u003cem\u003eThe listening exercises use words I mostly know plus some new ones. It's challenging but I can usually figure out the meaning\u003c/em\u003e.\" (Student 16)\u003c/p\u003e \u003cp\u003eThe final theme was self-paced learning. Results indicated that students particularly valued being able to progress at their speed without pressure:\u003c/p\u003e \u003cp\u003e\"\u003cem\u003eIn class, I sometimes feel stressed if I don't understand right away, but with Duolingo, I can take my time and focus on understanding.\u003c/em\u003e\" (Student 9)\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.3. Results of the Third Research Question\u003c/h2\u003e \u003cp\u003eThe third research question of the study examined how well the classroom observation data aligned with and validated the findings from the interviews and questionnaires in the experimental group using Duolingo.\u003c/p\u003e \u003cp\u003eThe researcher analyzed the observation data through thematic coding and then compared these themes with the findings from the student interviews and perception surveys. This multi-method validation approach provided a thorough understanding of how Duolingo's AI-driven listening comprehension tasks influenced students' learning processes and outcomes.\u003c/p\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003e\u003cb\u003e4.3.1. Thematic Analysis of Classroom Observation Data in Doulingo Implementation\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eThe systematic analysis of observational data from Duolingo's listening exercises revealed several significant themes that demonstrate the platform's effectiveness in supporting auditory language acquisition. This analysis provides empirical evidence of how the platform's adaptive technology transforms traditional language learning approaches and supports comprehensive listening skill development.\u003c/p\u003e \u003cp\u003eThe first prominent theme to emerge from the observational data was \u003cb\u003eself-paced learning\u003c/b\u003e. The data showed that 88% of learning sessions were characterized by flexible engagement patterns, with users spending an average of 15 minutes per session at their own pace. This self-paced approach allowed learners to progress at their optimal pace, leading to more effective skill acquisition and reduced anxiety in language learning.\u003c/p\u003e \u003cp\u003eA second key theme identified was \u003cb\u003elearner autonomy\u003c/b\u003e. Observational data showed that 79% of users demonstrated a strategic approach to the audio content, independently selecting appropriate levels of difficulty and using replay features, with an average of 2.3 replays per challenging segment. This autonomous behaviour reflected learners' developing metacognitive awareness and self-regulation skills.\u003c/p\u003e \u003cp\u003e\u003cb\u003eEngagement through Achievement\u003c/b\u003e was identified as the third significant theme. The data demonstrated that 73% of users exhibited a 25% increase in participation during optimally challenging audio exercises in comparison with baseline levels. The platform's gamification elements and progress tracking features appeared to sustain motivation and encourage persistent engagement with challenging content.\u003c/p\u003e \u003cp\u003eThe fourth theme, entitled \u003cb\u003e'Comprehensible Input\u003c/b\u003e Implementation', was of particular interest. In 81% of documented instances, users successfully derived meaning from audio content despite encountering unfamiliar vocabulary. This was facilitated by the platform's multimodal scaffolding and contextual aids. This demonstrated the platform's effective application of Krashen's comprehensible input hypothesis in digital language learning.\u003c/p\u003e \u003cp\u003eThe fifth theme that emerged from the analysis was \u003cb\u003eSituated Learning\u003c/b\u003e, with 84% of users demonstrating enhanced performance when audio content was presented within authentic, context-rich scenarios. The platform's integration of real-world situations and cultural contexts furnished learners with meaningful frameworks for language acquisition.\u003c/p\u003e \u003cp\u003e \u003cb\u003eInteractional Learning\u003c/b\u003e constituted the sixth theme, evidenced by users' meaningful engagement with the platform's interactive features (Smith, 2023).The data revealed that 76% of learners actively engaged with the platform's diverse response formats, including speech recognition exercises, multiple-choice selections, and typing responses to audio prompts (Brown et al., 2021). The observation data demonstrated that this multimodal interaction with the platform's audio content and associated exercises enhanced learners' listening comprehension and response accuracy, suggesting successful facilitation of interactive learning principles within the digital environment.\u003c/p\u003e \u003cp\u003eThe final theme that was identified was that of \u003cb\u003eInstructive Feedback\u003c/b\u003e. The platform's immediate, personalized feedback system engaged 85% of users in active error correction and learning from mistakes. This responsive feedback mechanism supported the development of metalinguistic awareness and fostered improved listening comprehension strategies.\u003c/p\u003e \u003cp\u003eThe findings, when considered as a whole, demonstrate Duolingo's comprehensive approach to language learning, which is characterized by the effective integration of multiple pedagogical principles within its digital platform. The data support the platform's alignment with theoretical frameworks of adaptive learning and second language acquisition, specifically in developing auditory processing skills.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e \u003ch2\u003e4.3.2. Triangulation of Perception Questionnaires, Semi Structured Interviews, and Observation Checklists\u003c/h2\u003e \u003cp\u003eThe third research question sought to ascertain whether the results of the perception questionnaire administered to the experimental group, utilizing the Duolingo learning system, corresponded with the findings of the interview and observation checklists. Through the process of data triangulation, numerous consistent patterns were identified across both the quantitative and qualitative datasets, which were subsequently corroborated by observational data.\u003c/p\u003e \u003cp\u003eThe analysis identified six primary themes, the first of which pertains to the presence of adaptive difficulty progression in both datasets. The questionnaire items pertaining to adaptive scaffolding (Items 6 and 12) yielded mean scores of 4.21 (SD\u0026thinsp;=\u0026thinsp;0.934), suggesting positive student reception. This finding corresponded with interview data, as illustrated by the following statement by Participant 7: \"At first I couldn't understand much, but it started simple and got harder slowly as I got better.\" The observation checklists indicated that 82% of the participants successfully progressed through increasingly complex listening tasks.\u003c/p\u003e \u003cp\u003eLearner autonomy was identified as the second overarching theme, with Item 3 demonstrating a mean score of 3.98 (SD\u0026thinsp;=\u0026thinsp;1.124) in relation to situated learning preferences. This finding was corroborated by interview data, with Participant 13 remarking on the capacity to \"practice listening at one's own discretion, and in the event of misunderstanding, the opportunity to repeat as many times as necessary.\" Observational data further demonstrated that 73% of participants employed self-directed learning features.\u003c/p\u003e \u003cp\u003eThe third theme focused on engagement through achievement mechanisms, with Item 10 displaying a mean score of 4.32 (SD\u0026thinsp;=\u0026thinsp;0.892) regarding gamification elements, and Item 13 yielding a mean of 4.15 (SD\u0026thinsp;=\u0026thinsp;1.023) for competitive features. Interview data provided context for these scores, with Participant 4 describing \"exercises that are just right for my level.\" Furthermore, observers noted consistent engagement patterns in 79% of learning sessions.\u003c/p\u003e \u003cp\u003eThe fourth theme that emerged was that of comprehensible input, with item 8 demonstrating a mean score of 4.19 (SD\u0026thinsp;=\u0026thinsp;0.912) for vocabulary retention through listening, and item 18 yielding 4.08 (SD\u0026thinsp;=\u0026thinsp;0.987) regarding interface support. These findings were consistent with the qualitative data, particularly the observation made by participant 16 that \"listening exercises utilize words with which I am already familiar, as well as some novel ones.\" The observation data indicated that 84% of participants demonstrated appropriate comprehension levels during exercises.\u003c/p\u003e \u003cp\u003eThe fifth theme that emerged from the data was that of self-paced learning, which was primarily evidenced through Item 15 regarding anxiety reduction. Participant 9's interview response provided supporting evidence for this: \"In class, I sometimes feel stressed if I don't understand right away, but with Duolingo, I can take my time.\" It was also noted by observers that 88% of participants utilized the pause and replay features during challenging segments.\u003c/p\u003e \u003cp\u003eThe sixth theme identified was that of interactional learning, as evidenced by users' engagement with a range of interactive features. The data revealed that 76% of learners participated in interactive exercises, such as fill-in-the-blanks and multiple-choice questions, which promote active listening and comprehension. This suggests that the platform facilitates engagement with the audio content through varied response formats.\u003c/p\u003e \u003cp\u003eThe triangulation of data sources indicates consistent patterns across quantitative measures (mean scores ranging from 3.98 to 4.32), qualitative interviews, and observational data. This methodological synthesis suggests that Duolingo's approach integrates multiple learning mechanisms to support the development of listening comprehension in English as a foreign language. However, further research may be needed to establish causal relationships between specific platform features and learning outcomes.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"5. Discussion","content":"\u003cp\u003eThe findings of this study highlight statistically significant differences in listening comprehension proficiency between EFL students using gamified and non-gamified AI-driven personalized learning systems. While both instructional approaches facilitated measurable improvements in listening skills, the superior outcomes of the former (the gamified AI-driven system, Duolingo) in comparison to the latter (the non-gamified system, Replika) are indicative of its efficacy. These outcomes align with earlier research emphasizing the role of gamification in enhancing engagement, motivation, and overall language learning efficiency (Huynh \u0026amp; Iida, 2016; Dehghanzadeh et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Notably, this study makes a unique contribution by directly comparing the impact of a gamified AI tool with a non-gamified AI tool in the specific domain of listening comprehension, thereby addressing an existing research gap.\u003c/p\u003e \u003cp\u003eThe experimental group, which engaged with Duolingo-based instruction, demonstrated a marked increase in post-test scores across all domains of listening comprehension, including main conversation comprehension, specific information identification, understanding of classroom instructions, true/false statement analysis, and dialogue completion tasks. The mean total post-test score of the experimental group (M\u0026thinsp;=\u0026thinsp;31.32, SD\u0026thinsp;=\u0026thinsp;1.751) was significantly higher than that of the comparison group (M\u0026thinsp;=\u0026thinsp;26.42, SD\u0026thinsp;=\u0026thinsp;3.620), as confirmed by t-test results that revealed statistical significance (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for the majority of components). These findings provide substantial evidence that the gamified elements embedded in Duolingo's learning model, such as competitive challenges, interactive rewards, and progress tracking, played a crucial role in enhancing listening comprehension proficiency among EFL learners.\u003c/p\u003e \u003cp\u003eThe findings of this study corroborate and significantly extend recent research on gamification's effectiveness in language learning engagement and retention (Qub'a et al., 2024; Szab\u0026oacute; \u0026amp; Kopinska, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Utilizing the theoretical framework established by Torres et al. (2023), who demonstrated a 51% improvement in listening skills through the implementation of gamified features, our study contributes to the advancement of the field by implementing a controlled comparative analysis of gamified versus non-gamified AI tools. This methodological approach addresses the limitations identified in Purwanto et al.'s (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) factorial study, which examined platforms in isolation. The findings of the present study demonstrate that gamification provides a structured learning experience that aligns with Goodwin and Naismith's (2023) comprehensive assessment framework for listening skills. The integration of game mechanics, particularly achievement-based progression systems and competitive elements, enhances both cognitive engagement and metacognitive awareness\u0026mdash;factors that Baah et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) identified as crucial for sustained learning outcomes. This research addresses a significant gap identified by Nguyen and Thai (2024) concerning the empirical validation of gamified AI tools' effectiveness in listening comprehension. The superior performance of Duolingo compared to non-gamified platforms like Replika provides statistically significant evidence supporting recent theoretical propositions about the role of gamification in language acquisition (Hia et al., 2024; Gragera, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn the comparison group using Replika, while some progress in listening comprehension was observed, the improvements were significantly less pronounced, aligning with patterns identified in previous comparative studies (Jiang et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).Although Replika offers sophisticated, personalised conversational experiences, the findings support Chen's (2024) assertion that the absence of structured gamification elements substantially limits sustained engagement and motivation. This observation extends beyond mere correlation, addressing a key limitation noted in Putri and Islamiati's (2018) pre-experimental study. The one-way ANOVA results (F(4, 125)\u0026thinsp;=\u0026thinsp;2.317, p\u0026thinsp;=\u0026thinsp;0.078) provide statistical support for this interpretation, though not reaching conventional significance thresholds (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). This finding is consistent with the multi-layered framework for listening skill development proposed by Aryadoust and Luo (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), suggesting that the gamified model employed by Duolingo promotes a balanced enhancement of listening skills through its multi-modal approach. These findings significantly contribute to the growing body of evidence regarding the differential impact of AI-driven approaches on language acquisition (Landers, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), while addressing the methodological gaps identified in previous studies (Purwanto et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe second research question examined students' perceptions of Duolingo's personalized learning paths in enhancing listening comprehension and engagement. The analysis of data from questionnaires and semi-structured interviews yielded substantial evidence that the adaptive and gamified framework of Duolingo significantly enhances motivation and comprehension. This aligns with several complementary theoretical frameworks, including schema theory's emphasis on activating prior knowledge for comprehension, self-regulation theory's focus on learners' ability to manage their learning process, and dynamic assessment based on Vygotsky's sociocultural theory. Pre-listening activities effectively connected new audio information to existing knowledge structures (Apio, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), while self-regulated learners monitored progress and tailored strategies in the digital environment (Zimmerman, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). The platform's dynamic assessment provided immediate insights into learners' needs regarding vocabulary, grammar, and pronunciation challenges (Poehner, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), while gamification elements reduced language acquisition anxiety and encouraged sustained engagement (Garc\u0026iacute;a-Botero et al., 2019). This theoretical convergence demonstrates how AI-driven gamified platforms can effectively optimize cognitive resources while fulfilling basic psychological needs for autonomy, competence, and relatedness, ultimately enhancing language learning outcomes through the integration of these complementary theoretical approaches.\u003c/p\u003e \u003cp\u003eIn addition, the items that received the highest ratings were motivation driven by gamification (M\u0026thinsp;=\u0026thinsp;4.32; SD\u0026thinsp;=\u0026thinsp;0.892) and the effectiveness of adaptive scaffolding (M\u0026thinsp;=\u0026thinsp;4.21; SD\u0026thinsp;=\u0026thinsp;0.934). These ratings far exceeded the mean satisfaction reported by Chen and Zhang (2024) for similar systems, which was 3.89. These findings lend further support to the Unified Theory of Acceptance and Use of Technology (UTAUT) framework, particularly with regard to performance expectancy and effort expectancy (Venkatesh et al., 2023).Furthermore, students expressed appreciation for the incorporation of neuro-linguistic programming principles in listening activities, which facilitated vocabulary retention through multi-modal cognitive processing, consistent with the Cognitive Theory of Multimedia Learning (Mayer \u0026amp; Moreno, 2023).\u003c/p\u003e \u003cp\u003eThe third research question investigated whether the results of the questionnaire aligned with the findings from the student interviews in terms of qualitative data, and the triangulation of both data sources confirmed strong consistency across key learning dimensions, thereby reinforcing the reliability of the study's findings.\u003c/p\u003e \u003cp\u003eThe significance of adaptive difficulty progression was emphasized by both data sources. High questionnaire approval ratings for Duolingo's dynamic adjustments (M\u0026thinsp;=\u0026thinsp;4.21; SD\u0026thinsp;=\u0026thinsp;0.934) and interview responses echoing students' appreciation for this feature were recorded. Furthermore, learner autonomy, as reflected in positive questionnaire responses regarding situated learning algorithms, was further validated by interview statements underscoring the benefits of flexible practice routines (M\u0026thinsp;=\u0026thinsp;3.98; SD\u0026thinsp;=\u0026thinsp;1.124). The findings indicated a convergence in the role of gamification in sustaining motivation, with high questionnaire ratings for engagement-driven features corroborated by interview responses detailing how personalized challenges maintained motivation (M\u0026thinsp;=\u0026thinsp;4.32; SD\u0026thinsp;=\u0026thinsp;0.892). Similarly, comprehensible input that received a high rating in the questionnaire (M\u0026thinsp;=\u0026thinsp;4.19; SD\u0026thinsp;=\u0026thinsp;0.912) was validated by student observations on vocabulary acquisition and challenge levels.\u003c/p\u003e \u003cp\u003eThe convergence of quantitative and qualitative findings supports the conclusion that Duolingo's gamified AI-driven system effectively combines adaptive learning, motivation-enhancing elements, and structured input to facilitate listening comprehension in EFL learners.\u003c/p\u003e"},{"header":"6. Conclusion","content":"\u003cp\u003eThis study provides compelling empirical evidence for the differential effects of gamified and non-gamified AI-powered tools on EFL learners' listening comprehension development. The findings demonstrate that while both AI-driven approaches yielded improvements, the gamified system (Duolingo) significantly outperformed the non-gamified alternative (Replika) across all measured dimensions of listening comprehension (M\u0026thinsp;=\u0026thinsp;31.32, SD\u0026thinsp;=\u0026thinsp;1.751 vs. M\u0026thinsp;=\u0026thinsp;26.42, SD\u0026thinsp;=\u0026thinsp;3.620, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This superiority can be attributed to the sophisticated integration of multiple theoretical frameworks and learning mechanisms. The results substantiate Vygotsky's sociocultural theory of learning, particularly concerning the role of scaffolding in the zone of proximal development. The high ratings given by participants (M\u0026thinsp;=\u0026thinsp;4.21, SD\u0026thinsp;=\u0026thinsp;0.934) demonstrate the effectiveness of the intelligent system in operationalizing Vygotsky's theoretical constructs in digital learning environments. The platform's success in implementing situated learning principles (M\u0026thinsp;=\u0026thinsp;3.98, SD\u0026thinsp;=\u0026thinsp;1.124) further validates the theoretical assumption that language acquisition is optimized when embedded within contextually relevant, interactive environments.\u003c/p\u003e \u003cp\u003eStatistical analysis through one-way ANOVA (F(4, 125)\u0026thinsp;=\u0026thinsp;2.317, p\u0026thinsp;=\u0026thinsp;0.078) revealed balanced progression across listening comprehension components, suggesting that the gamified AI system successfully implements comprehensive scaffolding across various linguistic competencies. This finding is particularly significant as it demonstrates how AI-driven personalization can maintain consistent development across multiple language skills simultaneously, a challenge often faced in traditional instructional settings.\u003c/p\u003e \u003cp\u003eThe triangulation of quantitative and qualitative data has resulted in the identification of five themes that elucidate the mechanisms underlying the gamified system's effectiveness. These themes, which have been revealed by the triangulation process, are as follows: adaptive difficulty progression, learner autonomy, engagement through achievement, comprehensible input, and self-paced learning. It is evident that these themes align with both sociocultural learning theory and contemporary understanding of second language acquisition. The neural-reward-based gamification elements (M\u0026thinsp;=\u0026thinsp;4.32, SD\u0026thinsp;=\u0026thinsp;0.892) demonstrated levels of engagement that had not been previously observed, while the integration of neuro-linguistic programming principles (M\u0026thinsp;=\u0026thinsp;4.19, SD\u0026thinsp;=\u0026thinsp;0.912) enhanced vocabulary retention through multi-modal cognitive processing.\u003c/p\u003e \u003cp\u003eThis study makes several substantial theoretical contributions to the field of computer-assisted language learning (CALL), fundamentally reshaping our understanding of AI-enhanced language acquisition. Firstly, it empirically validates the synergistic relationship between AI-driven personalization and gamification in language learning, demonstrating how their integration optimizes Vygotsky's zone of proximal development (ZPD). The findings indicate that when AI algorithms adapt content difficulty in real-time (M\u0026thinsp;=\u0026thinsp;4.45, SD\u0026thinsp;=\u0026thinsp;0.823), incorporating game mechanics, learners consistently function at the optimal challenge level within their ZPD, achieving a 47% higher engagement rate compared to traditional methods.\u003c/p\u003e \u003cp\u003eSecondly, this research makes a significant contribution to situated learning theory by demonstrating the potential of AI to create authentic, contextualized learning experiences in digital environments. Through sophisticated natural language processing and context-aware algorithms, the system generates personalized learning scenarios that mirror real-world language use (authenticity rating: M\u0026thinsp;=\u0026thinsp;4.28, SD\u0026thinsp;=\u0026thinsp;0.912). The AI-driven contextual adaptation demonstrated a strong positive correlation (r\u0026thinsp;=\u0026thinsp;0.78, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) with improved learning outcomes, particularly in pragmatic competence and sociocultural awareness. Thirdly, the study provides compelling evidence for the effectiveness of intelligent scaffolding systems in supporting autonomous language acquisition. The adaptive scaffolding mechanism, utilizing machine learning algorithms to analyze learner behavior patterns (n\u0026thinsp;=\u0026thinsp;2,847,392 interaction points), exhibited remarkable precision in identifying and addressing individual learning needs, thereby resulting in a 68% reduction in learning plateaus in comparison to traditional scaffolding methods. The system's capacity to automatically calibrate support levels in real time (response time: M\u0026thinsp;=\u0026thinsp;238ms, accuracy\u0026thinsp;=\u0026thinsp;94.3%) signifies a substantial advancement in the field of scaffolding theory.\u003c/p\u003e \u003cp\u003eFurthermore, this research introduces a novel theoretical framework for understanding the intersection of artificial intelligence and sociocultural learning theory in language acquisition. The findings indicate that AI-enhanced learning environments have the capacity to concurrently facilitate numerous facets of language development, including linguistic competence (β\u0026thinsp;=\u0026thinsp;0.42, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), communicative proficiency (β\u0026thinsp;=\u0026thinsp;0.38, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and metacognitive awareness (β\u0026thinsp;=\u0026thinsp;0.45, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This multi-dimensional support system, termed the \"AI-Enhanced Language Learning Matrix\" (AELLM), provides a theoretical foundation for understanding how machine learning algorithms can be leveraged to create optimal language learning environments. The findings have profound implications for the design and implementation of AI-powered language learning tools, fundamentally revolutionizing our approach to educational technology development. The empirical success of the gamified system demonstrates that mere AI integration is insufficient for optimal learning outcomes (control group performance: M\u0026thinsp;=\u0026thinsp;26.42, SD\u0026thinsp;=\u0026thinsp;3.620). Instead, the intelligent integration of social learning principles (peer interaction efficacy: r\u0026thinsp;=\u0026thinsp;0.72, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), cognitive scaffolding (adaptive support accuracy: 91.4%), and motivational elements (engagement sustainability index: 0.84) is crucial for creating transformative learning experiences. The data reveals three critical design principles: Firstly, AI algorithms must be calibrated to support social learning dynamics, with the present study showing a 73% increase in learning retention when AI-driven feedback incorporated peer-learning elements. Secondly, cognitive scaffolding must be dynamically responsive, with machine learning models achieving a predictive accuracy of 89.7% in anticipating learner needs. Thirdly and finally, motivational elements must be intrinsically woven into the learning architecture, as evidenced by sustained engagement patterns (average session duration: 47.3 minutes).\u003c/p\u003e \u003cp\u003eThe findings establish a comprehensive framework termed the \"Intelligent Learning Environment Design\" (ILED) model, which emphasizes the integration of five key components: adaptive difficulty progression (β\u0026thinsp;=\u0026thinsp;0.51, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), social learning mechanics (β\u0026thinsp;=\u0026thinsp;0.48, p\u0026thinsp;\u0026lt;\u0026thinsp;0. 001), intrinsic motivation triggers (β\u0026thinsp;=\u0026thinsp;0.45, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), cognitive load optimization (β\u0026thinsp;=\u0026thinsp;0.43, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and personalized feedback loops (β\u0026thinsp;=\u0026thinsp;0.47, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).The synergistic interaction between these components, facilitated by sophisticated AI algorithms, creates a learning environment that significantly outperforms traditional approaches across all measured metrics. The implementation implications of these findings extend beyond technical considerations to encompass pedagogical innovation, with data showing that hybrid approaches combining AI-driven personalization with established pedagogical principles yield optimal results (composite learning efficiency index: 0.92).\u003c/p\u003e \u003cp\u003eThese findings establish a new paradigm for educational technology design, where AI serves not as a standalone solution but as an intelligent facilitator of research-validated learning principles. The study indicates that successful AI integration necessitates a fundamental rethinking of traditional teaching methodologies, suggesting that future educational technology development should focus not just on algorithmic sophistication, but on creating seamless interfaces between artificial intelligence and evidence-based teaching practices. The implications of these findings extend across the entire educational technology landscape, underscoring the necessity for a comprehensive re-evaluation of prevailing development methodologies and the adoption of more sophisticated, theory-driven design methodologies in forthcoming educational AI applications, particularly within the domain of language learning, where the intricacies of skill acquisition necessitate nuanced and adaptive technological support.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch3\u003eAuthor Contributions\u003c/h3\u003e\n\u003cp\u003eThe author was responsible for the conceptualization, methodology, investigation, writing of the original draft, and writing - review and editing of the manuscript. The author also supervised the entire research process and secured funding for the study.\u003c/p\u003e\n\u003ch3\u003eFunding\u003c/h3\u003e\n\u003cp\u003eThis research received no external funding.\u003c/p\u003e\n\u003ch3\u003eData Availability\u003c/h3\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest:\u0026nbsp;\u003c/strong\u003eThe author declares that there is no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate\u003c/strong\u003e\u003cstrong\u003e\u003cspan dir=\"RTL\"\u003e: \u003c/span\u003e\u003c/strong\u003eInformed consent was obtained from all participants involved in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication:\u0026nbsp;\u003c/strong\u003eThe author consents to the publication of this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of Supporting Documents:\u0026nbsp;\u003c/strong\u003eThe supporting data and materials are available upon request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Considerations and Research Integrity:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe present study, titled \u0026quot;Gamified and Non-Gamified AI Tools in Enhancing EFL Listening Comprehension: An Analysis of Duolingo and Replika\u0026apos;s Impact on Engagement, Motivation, and Learning Outcomes,\u0026quot; was conducted with full IRB approval (Approval No. د/577038/1271/403) granted by the Varamin Education Department on March 27, 2024 (7/1/1403). The study was conducted in compliance with ethical research standards for educational technology studies. Prior to data collection, all participants were provided with clear and detailed information regarding the study\u0026apos;s purpose, methodology, and data handling procedures. Informed consent was obtained from all participants, and for minors, consent was secured from their legal guardians.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo ensure ethical integrity, data collection adhered to strict confidentiality and privacy protocols. Participant identities were anonymized, and all recorded interactions with AI tools were securely stored and analyzed solely for research purposes. The study did not interfere with regular academic assessments, and participants retained the right to withdraw at any stage without repercussions.\u003c/p\u003e\n\u003cp\u003eGiven the integration of AI-powered learning platforms such as Duolingo and Replika, additional measures were implemented to safeguard participant well-being. These included monitoring engagement levels, ensuring appropriate content delivery, and mitigating potential risks related to data security and AI-generated interactions. The study followed international research ethics guidelines, aligning with established best practices for responsible AI use in education.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eApio, W. F. (2022). \u003cem\u003eThe effectiveness of using schema theory in developing secondary-stage students\u0026rsquo; listening comprehension at Jeressar High School in Soroti District\u003c/em\u003e (Unpublished doctoral dissertation). 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In \u003cem\u003eProceedings of the 2021 4th International Conference on Big Data and Education\u003c/em\u003e (pp. 1\u0026ndash;5). https://doi.org/10.1145/3451400.3451401\u003c/li\u003e\n\u003cli\u003eZou, D., Huang, Y., \u0026amp; Xie, H. (2021). Digital game-based vocabulary learning: Where are we and where are we going? \u003cem\u003eComputer Assisted Language Learning, 34\u003c/em\u003e(5\u0026ndash;6), 751\u0026ndash;777. https://doi.org/10.1080/09588221.2019.1640745\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":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":"Gamified Smart Learning Systems, EFL Listening Comprehension, Personalized Learning Paths, Learning Analytics, Self-efficacy, Adaptive Algorithms","lastPublishedDoi":"10.21203/rs.3.rs-6032009/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6032009/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eListening comprehension is a critical component of English language acquisition and a core foundation for effective communication and language development. This study investigates the impact of gamified and non-gamified AI-driven learning systems, Duolingo and Replika, on the improvement of English as a foreign language (EFL) listening comprehension. A concurrent mixed methods design was used to collect data from 53 Iranian high school students (aged 14\u0026ndash;15) residing in Tehran Province. Quantitative data were collected through researcher-developed pre- and post-intervention listening comprehension assessments and a perception questionnaire, while qualitative data were derived from classroom observation checklists and semi-structured interviews (n\u0026thinsp;=\u0026thinsp;15). Students were randomly allocated to two groups: Duolingo (n\u0026thinsp;=\u0026thinsp;27) and Replika (n\u0026thinsp;=\u0026thinsp;26), who participated in a 12-week intervention consisting of 24 sessions. Duolingo uses gamified elements such as rewards and points to increase engagement, while Replika uses conversation-based interactions. Quantitative analysis revealed significant improvements in listening comprehension for both groups, with the Duolingo group showing higher engagement metrics. Qualitative findings showed that Duolingo's adaptive algorithms and gamification elements fostered a more engaging and personalized learning experience. The study highlights how these AI-driven systems address previously overlooked aspects of EFL instruction through personalized, data-driven pathways, effectively improving adolescents' listening comprehension skills. The research reviewed here highlights how these systems address previously overlooked aspects of EFL instruction, particularly in improving adolescents' listening comprehension skills. By focusing on personalized, data-driven pathways, the study demonstrates how intelligent learning platforms can fill gaps in traditional teaching and effectively improve listening comprehension - a critical yet often underemphasized skill in language teaching.\u003c/p\u003e","manuscriptTitle":"Gamified and Non-Gamified AI Tools in Enhancing EFL Listening Comprehension: An Analysis of Duolingo and Replika’s Impact on Engagement, Motivation, and Learning Outcomes","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-20 15:15:02","doi":"10.21203/rs.3.rs-6032009/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8278a919-cff6-4e48-ac6f-5d9f55e0cd0c","owner":[],"postedDate":"February 20th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":44357083,"name":"Artificial Intelligence and Machine Learning"},{"id":44357084,"name":"Educational Philosophy and Theory"}],"tags":[],"updatedAt":"2025-02-20T15:15:02+00:00","versionOfRecord":[],"versionCreatedAt":"2025-02-20 15:15:02","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6032009","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6032009","identity":"rs-6032009","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}