Human-Centered Artificial Intelligence in EFL Listening: Emotional Adaptivity, Gamified Feedback, and Motivational Scaffolding toward Next-Generation Learning Systems

Human-Centered Artificial Intelligence in EFL Listening: Emotional Adaptivity, Gamified Feedback, and Motivational Scaffolding toward Next-Generation Learning Systems | 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 Human-Centered Artificial Intelligence in EFL Listening: Emotional Adaptivity, Gamified Feedback, and Motivational Scaffolding toward Next-Generation Learning Systems Aliakbar Tajik¹, Atefeh Karkhaneh² This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7827226/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 constitutes one of the most cognitively demanding yet underemphasized components of English language education, particularly within adolescent EFL classrooms. Addressing this overlooked area, the present mixed-methods research explores the influence of human-centered artificial intelligence (AI) on learners’ listening comprehension, engagement, and motivation. Two learning environments with distinct instructional architectures were compared under controlled classroom conditions: a gamified adaptive system structured around motivational feedback loops and progression tracking, and a conversational emotion-adaptive AI interface designed to foster reflective autonomy. Grounded in Schema Theory, Self-Regulation Theory, Dynamic Assessment within Sociocultural Theory , and Gamification Theory , the study integrates these perspectives through two constructivist frameworks—the AI-Enhanced Language Learning Matrix (AELLM) and the Intelligent Learning Environment Design (ILED) —and extends them by proposing Tajik’s Scaffolded Motivator Model (T-SMM) . Participants consisted of 53 Iranian high-school learners (aged 14–15) engaged in a 12-week instructional program involving 24 sessions. Quantitative outcomes revealed robust gains in listening proficiency and self-efficacy, while qualitative data highlighted sustained emotional engagement and adaptive autonomy within the human-centered learning design. The findings suggest that emotionally adaptive feedback and gamified motivational scaffolding act as key mediators in supporting deeper cognitive processing and consistent learner participation. By synthesizing theoretical and empirical insights, this study redefines motivational scaffolding as a critical mechanism driving the effectiveness of next-generation, AI-supported EFL listening instruction and offers tangible implications for future intelligent learning system design. Gamified Intelligent Learning Systems EFL Listening Comprehension Personalized Learning Paths Emotion-Adaptive Feedback Self-efficacy Adaptive Algorithms Tajik’s Scaffolded Motivator Model (T-SMM) 1. Introduction Listening comprehension constitutes one of the most cognitively demanding pillars of second language acquisition, accounting for roughly 50–80% of total communicative engagement in language learning (Yıldırım & Yıldırım, 2016 ; Hosseini et al., 2021 ). As a multidimensional cognitive process, it involves the rapid orchestration of phonological decoding, lexical access, syntactic parsing, and semantic integration to construct coherent meaning from incoming auditory input (Buck, 2001 ; Goh & Vandergrift, 2021 ). In real-time communication, learners must also cope with extralinguistic variations—such as accent, speech rate, and discourse context—that further complicate comprehension (Rost, 2016). Academic and professional environments intensify these challenges, exposing learners to diverse discourse types ranging from informal dialogue to discipline-specific lectures, each demanding distinct adaptive strategies (Field, 2010 ). Consequently, there is a growing pedagogical need for context-responsive, human-centered instruction that addresses not only linguistic decoding but also strategic adaptation to real-world communicative conditions. In the coming decade, the fusion of human-centered artificial intelligence, emotional adaptivity, and gamified motivational scaffolding is poised to redefine how adolescent learners develop receptive language skills, positioning EFL listening comprehension as a frontier of theoretical and pedagogical transformation. Despite its centrality, listening instruction remains underrepresented across many English as a Foreign Language (EFL) settings, especially in Iranian high schools, where classroom practices tend to prioritize grammar, translation, and standardized test preparation over communicative competence (Ghaed Sharaf et al., 2018 ; Namaziandost et al., 2019 ). Exposure to authentic listening materials is often minimal, structured feedback is scarce, and practice opportunities are limited (El Baghdadi et al., 2024 ). Listening activities frequently serve as isolated evaluation tools rather than cyclical skill-building processes, undermining their pedagogical potential. Moreover, the artificiality of instructional resources—marked by simplified recordings, reduced linguistic variation, and teacher-centered delivery—fails to mirror the complexities of genuine discourse. These limitations highlight a persistent gap between classroom practice and communicative demand, intensifying the need for innovative interventions that enrich auditory input, cultivate sustained engagement, and provide timely, personalized feedback. The constraints of traditional pedagogy have spurred growing interest in intelligent and emotionally adaptive learning innovations capable of delivering dynamic, learner-centered listening instruction. Human-centered AI environments featuring adaptive algorithms and gamified feedback mechanisms create personalized learning paths, offering immediate, focused responses to learners’ performance in areas such as phoneme recognition, prosodic control, and comprehension of connected speech (Bakhtiar et al., 2024 ; Tuong & Dan, 2024 ). These systems broaden exposure to authentic linguistic contexts across diverse accents, genres, and rates of speech, while addressing key socio-emotional needs such as confidence building, anxiety reduction, and motivation enhancement (Febrina & Hamdi, 2024 ). Gamification emerges as a crucial pedagogical element, embedding progression tracking, achievement markers, and interactive challenges to sustain learner commitment and iterative mastery (Bennani et al., 2022 ; Hsu, 2024 ). Within this landscape, emotion-adaptive and gamified feedback acts as an educational mediator—transforming listening practice from a passive task into an engaging, human-centered learning experience. Against this background, the present research conceptualizes intelligent platform design through two complementary theoretical blueprints, namely the AI-Enhanced Language Learning Matrix (AELLM) and the Intelligent Learning Environment Design (ILED), each integrating adaptive technology, sociocultural scaffolding, and motivational gamification principles in EFL listening contexts. Previous studies on AI-assisted language learning have predominantly explored general proficiency outcomes (Tajik, 2025 ; Gragera, 2024 ; Jiang et al., 2023 ; Jiang & Pajak, 2022 ), often overlooking the critical interrelations among gamified design, emotional feedback, and skill-specific listening development. Limited empirical work has compared gamified and non-gamified AI interfaces within adolescent learning environments, despite widespread acknowledgment of personalization as a determinant of effective learning (Essafi et al., 2024 ) and the proven influence of motivational design on engagement (Tajik, 2024 ). The present mixed-methods study addresses this gap by comparing two distinct AI learning systems under parallel classroom conditions: a gamified, adaptive system and a conversational, emotion-adaptive interface . The study involved 53 Iranian high-school students (aged 14–15) across a 12-week, 24-session program. Quantitative data were obtained from pre- and post-intervention listening tests and learner questionnaires, complemented by qualitative insights from classroom observations and semi-structured interviews. To the authors’ knowledge, this represents the first controlled comparison between gamified and non-gamified intelligent AI platforms for adolescent EFL listening comprehension. The research introduces triple-layered originality: (1) systematic integration of Schema Theory, Self-Regulation Theory, Dynamic Assessment within Sociocultural Theory, and Gamification Theory ; (2) application of the AELLM and ILED as conceptual models of AI-assisted listening pedagogy; and (3) formulation of Tajik’s Scaffolded Motivator Model (T-SMM) —an empirically grounded framework elucidating how emotional adaptivity and motivational scaffolding can sustain long-term engagement, enhance listening proficiency, and foster reflective autonomy. Collectively, these contributions advance theoretical understanding and provide actionable design guidelines for next-generation, human-centered AI learning systems . 1.1. Theoretical Framework 1.1.1 Schema Theory Schema Theory provides a foundational framework for understanding listening comprehension in EFL contexts. It emphasizes the importance of activating prior knowledge to facilitate the interpretation and retention of new auditory input (Xia et al., 2024 ). In practice, pre-listening activities such as brainstorming, graphic organizers, and KWL strategies bridge learners’ existing schemata with new content, enhancing processing efficiency and confidence. Empirical evidence affirms that schema activation transforms listening from a passive reception into an active, meaning-constructive process (Xia et al., 2024 ), directly relevant to the personalized pre-task designs in this study. 1.1.2 Self-Regulation Theory Self-Regulation Theory highlights learners’ capacity to monitor, reflect on, and adjust their strategies to achieve language goals (Zimmerman, 2002 ). Self-regulated learners identify challenges, select tailored strategies, and adapt based on feedback (Sansone et al., 2019 ). In Duolingo and similar digital environments, self-regulation manifests through goal setting, progress tracking, and strategic adaptation (Li & Bonk, 2023 ). These affordances align theory with practice, fostering learner autonomy and engagement in targeted listening sub-skills. Such alignment underpins the novelty of integrating adaptive gamified platforms to cultivate both skill mastery and self-directed learning. 1.1.3 Dynamic Assessment and Sociocultural Theory Grounded in Vygotsky’s Sociocultural Theory (Lantolf, 2000 ), Dynamic Assessment (DA) integrates evaluation and instruction through collaborative, feedback-mediated interaction (Lantolf & Poehner, 2014 ; van Compernolle & Zhang, 2014 ). In contrast to static testing, DA seeks to identify learners’ developmental potential (Poehner & Lantolf, 2013) by offering real-time scaffolding within the Zone of Proximal Development (ZPD) (Poehner & van Compernolle, 2011; Hidri, 2019 ). In adaptive platforms such as Duolingo, ZPD-driven principles are enacted via algorithm-based task adjustments (Ma & Zhang, 2024 ), enabling tailored support across vocabulary, grammar, and phonological skills. This ZPD orientation directly informs the instructional design of the present study, ensuring that listening comprehension tasks are both scaffolded and developmentally appropriate (van de Pol et al., 2019 ; Wood, 2021 ; Swain et al., 2015 ). 1.1.4 Gamification Theory Gamification Theory asserts that embedding game-like elements—such as points, rewards, and progressive levels—can strengthen learner motivation and persistence by engaging both intrinsic and extrinsic incentives (Shortt et al., 2023 ). Within Duolingo, these components take the form of interactive storytelling, role-play scenarios, and competitive progress tracking, all of which have been shown to reduce language-learning anxiety and sustain long-term engagement (García-Botero et al., 2018). Gamification further converges with self-regulation principles, enabling learners to monitor performance, set incremental targets, and pursue achievements through structured cycles. In the present study, gamified delivery magnifies the impact of schema activation, self-regulatory behaviors, and scaffolded instruction by situating them in a psychologically rewarding environment—one that aligns with Vygotskian perspectives on socially situated learning (Lantolf, 2000 ; Lantolf et al., 2020 ). 1.1.5 Link to the Research Gap and Aim The combined application of Schema Theory, Self-Regulation Theory, Dynamic Assessment within Sociocultural Theory, and Gamification Theory provides a multi-layered lens for exploring EFL listening comprehension in intelligent learning environments. While each theory has been examined separately (e.g., Gragera, 2024 ; Jiang et al., 2023 ; Jiang & Pajak, 2022 ; Essafi et al., 2024 ; Fanni & Maharani, 2024 ), studies synthesizing these perspectives in real-world classrooms—especially in Iranian high schools—are notably absent. In particular, the interactive effects of schema activation, autonomous self-regulation, scaffolded dynamic assessment, and motivational gamification have not been systematically operationalized in AI-driven platforms such as Duolingo and Replika. This remains a substantial gap in both theory and practice. To address this, the present study integrates the four theories into two conceptual frameworks—the AI-Enhanced Language Learning Matrix (AELLM) and Intelligent Learning Environment Design (ILED) —and extends them with the data-driven Tajik’s Scaffolded Motivator Model (T-SMM) derived from empirical findings. Conducted with adolescent EFL learners in Iran, the research examines how cognitive, regulatory, scaffolding, and gamified mechanisms can be orchestrated to deliver adaptive and engaging listening instruction. The contribution lies in its structured progression from a theoretically grounded synthesis to dual conceptual models, culminating in a novel empirical framework, thereby offering a nuanced account of how such pedagogical architectures can shape listening outcomes and inform next-generation AI-assisted learning design . 2. Literature Review Building on the integrated theoretical perspectives from Section 1.1.5 , this chapter synthesizes empirical findings and conceptual discussions on gamified and non-gamified AI-driven language learning platforms in the context of EFL listening comprehension. The review adopts a thematic structure encompassing Duolingo’s technical and pedagogical features, the motivational and skill-development affordances of gamification, evidence on Duolingo’s role as an instructional intervention, and empirical insights into the intersection of gamification, intelligent platforms, and listening skill development. This synthesis situates the present study within ongoing scholarly debates while identifying persistent theoretical and practical gaps that underlie its design. 2.1 Duolingo and Its Features The advent of mobile-assisted and AI-powered language learning has transformed accessibility by removing spatial and temporal boundaries from formal instruction. Among these tools, Duolingo stands out as a globally adopted, free platform supporting over 23 languages and serving approximately 200 million users (Jašková, 2014 ). Its gamified interface delivers adaptive tasks across listening, speaking, reading, and writing domains (Inayah et al., 2020 ), while placement tests enable tailored entry points for users based on proficiency (Nushi & Eqbali, 2017 ). Design features such as “Streaks,” “Crown Levels,” and leaderboards foster engagement persistence (Jiang et al., 2024 ; García-Botero et al., 2018; Qub’a et al., 2024 ; Szabó & Kopinska, 2023 ). Coupled with AI-driven analytics, the platform tracks fine-grained performance in sub-skills—such as phoneme recognition and prosody—enabling targeted adjustments to user pathways (Tuong & Dan, 2024 ). 2.2 The Role of Gamification in Enhancing EFL Listening Skills Gamification applies game-design principles—points, badges, competition, and real-time rewards—to contexts beyond gaming, and within EFL pedagogy, it has strengthened learner motivation and perseverance (Bennani et al., 2022 ; Hsu, 2024 ). When integrated into adaptive systems, these elements support regular, self-directed practice (García-Botero et al., 2018) and foster both extrinsic and intrinsic motivation through engaging, goal-directed interaction (Shortt et al., 2023 ). In listening comprehension, game-like drills can directly address challenges such as connected speech segmentation or accurate prosodic contouring (Bakhtiar et al., 2024 ). This aligns with self-regulation theory, wherein learners monitor progress, set incremental goals, and adapt strategies accordingly (Zimmerman, 2002 ). Moreover, in culturally diverse EFL classrooms, gamified collaboration and competition can lower affective barriers (García-Botero et al., 2019) while promoting communicative risk-taking (Swain et al., 2015 ). 2.3 Duolingo’s Effectiveness as a Language Learning Tool Despite Duolingo’s prominence, empirical inquiry into its specific effects on EFL listening remains relatively scarce. Most available evidence addresses general proficiency improvement without dissecting mechanisms behind listening skill gains (García-Botero et al., 2020; Zhang & Hasim, 2023 ). Features such as adaptive sequencing, immediate corrective feedback, and systematic repetition of authentic audio content have been shown to enhance decoding, prosodia, and global comprehension (Ghasemi et al., 2024). Its microlearning design mirrors cognitive principles of spaced repetition, supporting durable retention (Poehner & van Compernolle, 2011). Integration of rich contextual cues resonates with schema theory, facilitating meaning construction from auditory stimuli (Apio, 2022 ). However, limited attention has been paid to how gamified feedback loops specifically influence listening development, or how these processes vary across learner demographics. 2.4 Empirical Studies on Duolingo, Gamification, and Listening Skills Research on gamified intelligent platforms generally reports enhanced engagement and, in some contexts, measurable skill gains. Lee ( 2019 ) identified socio-political and contextual mediators of digital tool effectiveness, while Tai and Chen (2020) found that AI assistants can bolster learner confidence—insights potentially transferable to listening domains. Duolingo-focused studies note gains in sub-skills such as phoneme discrimination, though such work often lacks longitudinal design or rigorous control-group comparison (Jiang et al., 2024 ). Integration of dynamic assessment approaches (Huynh & Iida, 2016; Dehghanzadeh et al., 2021 ; Ma & Zhang, 2024 ) suggests promise for responsive, targeted support in listening comprehension. Yet, comprehensive comparisons of gamified versus non-gamified intelligent systems for adolescent EFL learners, particularly in the Iranian context, remain uncommon. In summary , while gamified AI-driven systems like Duolingo demonstrate potential to enhance listening comprehension through adaptive feedback, motivational scaffolding, and targeted practice, existing empirical evidence is fragmented, context-specific, and methodologically inconsistent. Few studies explicitly explore the interplay between gamification mechanisms, self-regulatory learning behaviors, and quantifiable listening gains among adolescents. This absence of robust, context-sensitive comparative data—particularly in Iran—forms the empirical gap that the present study targets. By employing a controlled, mixed-method design with adolescent Iranian EFL learners, this research addresses both cognitive and affective dimensions of listening skill development, advancing scholarly understanding of how gamified and non-gamified intelligent systems can differentially support such outcomes. 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 evaluate the effectiveness of two intelligent learning systems—Duolingo and Replika—in enhancing listening comprehension skills among Iranian high school students learning English as a foreign language (EFL). The participants were 53 students aged 14–15 years from Tehran Province, randomly assigned to either the Duolingo group (n = 27) or the Replika group (n = 26). Both groups participated in a 12‑week intervention comprising 24 instructional sessions delivered via their designated intelligent learning platform. To ensure group equivalence, a pre‑test measuring language proficiency was administered before the intervention. The pre‑ and post‑intervention listening comprehension assessments were specifically developed for Iranian eighth- and ninth-grade students, based on Prospect 2 and Prospect 3 textbooks. The test included five 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 maximum score was 20 marks. Content validity was established through expert review: a panel of three specialists in English language teaching and assessment verified alignment between the test items and the targeted listening skills in Prospect 2 and Prospect 3, confirming comprehensive coverage of curriculum-relevant listening tasks. Reliability was verified via a pilot study involving 30 students from a comparable demographic. The internal consistency, calculated using Cronbach’s alpha, was 0.87, indicating strong reliability and ensuring consistent measurement across administrations. Two principal quantitative instruments were utilized. First, the listening comprehension assessment described above; and second, a researcher‑developed questionnaire containing 18 items (see Appendix A) across five dimensions: learning engagement, system usability, perceived effectiveness, learning progress, and motivational aspects. Items were rated on a five‑point Likert scale (“Strongly Agree” to “Strongly Disagree”). Exploratory and confirmatory factor analyses confirmed satisfactory construct validity (α = .89) and reliability. This questionnaire gathered data on daily usage, homework completion, and voluntary listening activities. Complementing the quantitative measures, two qualitative instruments explored learners’ experiences: 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. Quantitative analysis was conducted in SPSS using paired sample t‑tests and ANOVA. Qualitative data underwent thematic analysis, with findings integrated through triangulation to form a holistic evaluation of how personalized, intelligent learning systems impacted listening comprehension and student engagement This multi‑faceted methodology ensured a robust, evidence‑based assessment addressing the research objectives while adhering to rigorous standards of validity and reliability. 3.1. Participants This study was conducted in Varamin, a city located southeast of Tehran, Iran, and involved middle school students aged 14–15 years in grades 8 and 9. A systematic sampling method was employed to obtain a representative sample. First, eligible schools were identified from a registry of lower secondary schools, and six schools, representing both male and female student populations, were selected using structured inclusion criteria and stratified randomization to ensure balanced demographic representation. This multi-stage sampling strategy was designed to enhance the validity and generalizability of the findings by capturing the diversity of the target population. All prospective participants completed a standardized language proficiency test to assess baseline language skills. From these, 53 students meeting the intermediate proficiency level were selected. They were randomly assigned to an experimental group (n = 27) or a comparison group (n = 26). Identical demographic and academic inclusion criteria were applied in both groups, thereby increasing methodological rigor and controlling for extraneous variables. The 12-week intervention comprised 24 classroom sessions. The experimental group used the Duolingo digital learning platform, which integrates gamified elements such as leaderboards, point-based progress tracking, and interactive challenges. Instructional activities targeted listening comprehension through a blend of adaptive learning techniques and competitive, game-like tasks. The comparison group used the Replika AI application, a conversational agent designed to simulate natural dialogue and support language practice through personalized, immersive interactions. Both groups had equivalent content exposure and instructional duration to ensure the reliability of the comparative results. This robust design facilitated the examination of the differential effects of gamified versus conversational AI-based learning tools on listening skill development, while minimizing the influence of confounding demographic and instructional factors. . 3.2. Data Collection Instruments This study adopted a mixed-methods research design to evaluate the effectiveness of Duolingo’s gamified intelligent learning system on EFL listening comprehension. Multiple instruments were employed to ensure comprehensive evaluation and methodological triangulation. The primary quantitative measure was a pre-- and post-intervention listening comprehension assessment specifically developed for Iranian 8th- and 9th-grade students, based on Prospect 2 and Prospect 3 textbooks. The test consisted of five sections: (1) Comprehension of main conversations (5 marks), (2) Recognition of specific information (4 marks), (3) Comprehension of classroom instructions (3 marks), (4) True/False recognition (4 marks), and (5) Dialogue completion (4 marks), yielding a maximum score of 20 marks. A rigorous validation process confirmed the instrument’s psychometric soundness. Content validity was established by a panel of nine experts—six university professors, two English language supervisors, and one experienced trainer—resulting in a Content Validity Index (CVI) = 0.90 and a Content Validity Ratio (CVR) = 0.88. Construct validity was supported by factor analysis (KMO = 0.83; Bartlett’s test, p < .001), which identified five components corresponding to the test sections. Convergent validity was evidenced by a strong correlation with standardized listening tests ( r = 0.85). Reliability indices were equally robust: Cronbach’s alpha ranged from 0.81 to 0.85 across sections (0.87 overall), inter-rater reliability achieved a Cohen’s Kappa = 0.89 (Pearson r = 0.92), and test-retest reliability over two weeks with 40 students yielded r = 0.86. Item analysis demonstrated appropriate difficulty indices (0.35–0.75), discrimination indices (0.38–0.65), and point-biserial correlations (0.42–0.68). Expert feedback informed refinements related to timing (25 minutes total), instruction clarity, content alignment, audio quality, and repetition frequency (each section played twice, 30 seconds apart). The test content differentiated grade-specific complexity, ranging from everyday conversations (Grade 8) to advanced topics and complex structures (Grade 9), ensuring cultural appropriateness and precise targeting of Iranian EFL learners’ abilities. The second quantitative instrument was an 18-item researcher-developed questionnaire (Appendix A), measuring learning engagement, system usability, perceived effectiveness, learning progress, and motivational aspects on a five-point Likert scale ( Strongly Agree to Strongly Disagree ). Validity and reliability were confirmed through exploratory and confirmatory factor analyses (α = .89). The questionnaire captured data on daily application use, homework completion, and voluntary participation in listening activities. Two qualitative instruments provided complementary insights. First, semi-structured post-intervention interviews with 20 participants (Appendix B) explored motivation, self-efficacy, and perceived benefits of personalized learning paths. Sessions (25–35 minutes) were recorded, transcribed, and validated via member checking and peer debriefing. Second, a structured observation checklist (Appendix C), validated by experts and pilot-tested (inter-rater agreement = 0.88), tracked learner–system interactions, environment dynamics, and engagement indicators across 20 sessions, with binary coding supplemented by qualitative notes. This integrated multi-instrument approach enabled both quantitative outcome measurement and rich qualitative exploration of the learning process, supporting a nuanced analysis of the cognitive and affective dimensions of technology-enhanced listening comprehension among adolescent EFL learners.. 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 session 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 the implementation of the experimental intervention, all participants underwent a comprehensive listening comprehension pre-test designed in alignment with the Prospect 2 and Prospect 3 curricula for Iranian lower secondary education. The instrument encompassed five discrete subskills—Main Conversation Comprehension, Specific Information Identification, Classroom Instruction Understanding, True/False Statement Analysis, and Dialogue Completion Tasks—yielding an aggregated score representing overall listening proficiency. Independent samples t-test analyses conducted on the pre-test outcomes (Table 1 ) demonstrated that there were no statistically significant differences between the Duolingo group (n = 27) and the Replika group (n = 26) across any of the five subskills or in total listening scores (all p-values > .05). Specifically, mean scores for Main Conversation Comprehension were 4.98 (SD = 0.96) for the experimental group and 5.93 (SD = 1.10) for the comparison group (t(51) = 1.889, p = .066). For Specific Information Identification, mean scores were 4.92 (SD = 1.01) and 5.34 (SD = 0.68), respectively (t(51) = 1.116, p = .268). Scores for Classroom Instruction Understanding were identical in mean (4.89) between groups, evidencing complete parity at baseline (t(51) = 0.973, p = .335). True/False Statement Analysis yielded means of 4.28 (SD = 1.30) and 4.91 (SD = 1.02) (t(51) = 1.565, p = .124), whereas Dialogue Completion Tasks averaged 4.42 (SD = 1.21) and 4.88 (SD = 1.03) (t(51) = 1.793, p = .078). Regarding the overall performance, total pre-test scores averaged 26.02 (SD = 4.46) for the Duolingo group and 24.12 (SD = 3.46) for the Replika group (t(51) = 1.899, p = .064). These non-significant results confirm the statistical equivalence of the two cohorts at the outset of the study, thereby ensuring that subsequent changes in performance could be attributed with greater confidence to the respective interventions—Duolingo’s gamified, rewards-based environment or Replika’s conversation-oriented, context-rich dialogue system—rather than pre-existing disparities in listening comprehension ability. . 4.2. The Results of the First Research Question As shown in Table 2 , both groups demonstrated significant improvements in post-test listening comprehension scores; however, the Duolingo-based experimental group outperformed the Replika comparison group across all measured subskills. For Main Conversation Comprehension , the experimental group achieved a mean of 6.55 (SD = 0.508) compared with 5.72 (SD = 0.994) in the comparison group (t(51) = 4.746, p < .001). In Specific Information Identification , scores averaged 6.71 (SD = 0.739) for Duolingo participants versus 5.42 (SD = 0.867) for Replika participants (t(51) = 5.661, p < .001). Classroom Instruction Understanding scores were also higher in the experimental group (M = 6.41, SD = 0.565) than in the comparison group (M = 5.35, SD = 0.898; t(51) = 4.226, p < .001). Although the performance gap was smaller for True/False Statement Analysis , Duolingo learners still attained higher scores (M = 5.91, SD = 0.679) compared with those in the Replika group (M = 5.51, SD = 1.152; t(51) = 2.297, p = .026). The largest difference was observed in Dialogue Completion Tasks , where the experimental group’s mean was 6.21 (SD = 0.478) compared to 5.08 (SD = 0.752) for the comparison group (t(51) = 5.873, p < .001). In total post-test scores, the Duolingo group recorded 31.32 (SD = 1.751), markedly exceeding the 26.42 (SD = 3.620) achieved by the Replika group (t(51) = 6.085, p < .001). These consistent, statistically significant results indicate that the gamified, AI-driven Duolingo platform promoted more robust gains in listening comprehension than the primarily conversational Replika platform. To further examine within-group performance patterns, a one-way ANOVA was conducted for the experimental group (Table 3 ). The results indicated no statistically significant differences among the five subskills (F(4, 125) = 2.317, p = .078), suggesting that Duolingo’s instructional impact was evenly distributed across skill areas. This balanced improvement reinforces the notion that Duolingo’s adaptive and competitive elements—such as leaderboards, goal tracking, and immediate feedback—foster holistic listening comprehension development rather than disproportionately enhancing specific task types. In contrast, Replika’s focus on straightforward conversational exchanges, while beneficial for engagement, lacked the intensified and structured gamification mechanisms that may drive sustained proficiency gains. These findings collectively underline the potential of gamified AI-based platforms to enhance EFL learners’ listening competence comprehensively. 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 explored high school students’ perceptions and experiences regarding Duolingo’s personalized learning paths as a pedagogical intervention to enhance EFL listening comprehension. Employing a mixed-methods design, data were collected via both structured questionnaires and semi-structured interviews, ensuring a comprehensive understanding of learner engagement and response to the platform’s features. The questionnaire targeted multiple interconnected dimensions: (1) neural-reward-based gamification elements, (2) AI-driven adaptive scaffolding, (3) neuro-linguistic programming–inspired instructional strategies, (4) social learning dynamics, (5) human–computer interaction features, and (6) situated learning algorithms. This multidimensional approach enabled a nuanced examination of the motivational, cognitive, and interactional mechanisms underlying learner engagement. The analysis is presented in two sequential phases. First, quantitative findings from the questionnaire are reported, highlighting measurable trends in learners’ perceptions across the aforementioned dimensions. This is followed by qualitative insights derived from interview data, offering depth and contextualization to the numerical patterns. Together, these complementary perspectives provide a robust account of how students interacted with—and benefited from—Duolingo’s innovative, personalized learning pathways in developing their listening comprehension proficiency. 4.2.1 Results of the questionnaire Analysis of questionnaire responses provided detailed insights into students’ perceptions of Duolingo’s personalized learning paths for enhancing EFL listening comprehension. In the dimension of neural-reward-based gamification , Item 10 (M = 4.32, SD = 0.892) indicated high engagement with features such as streak tracking and achievement badges, which participants associated with sustained motivation. Regarding AI-driven adaptive scaffolding , Items 6 and 12 (M = 4.21, SD = 0.934) reflected positive learner perceptions of dynamic difficulty adjustments and individualized progress monitoring. The neuro-linguistic programming–inspired approach to listening activities, measured in Item 8 (M = 4.19, SD = 0.912), was associated with improved vocabulary retention through multimodal processing. The social learning dimension, captured by Item 13 (M = 4.15, SD = 1.023), highlighted the motivational influence of peer-competitive elements integrated into the platform. For human–computer interaction , Item 18 (M = 4.08, SD = 0.987) suggested that intuitive interface design facilitated learners’ engagement with listening tasks. Finally, situated learning algorithms , represented by Item 3 (M = 3.98, SD = 1.124), were perceived as valuable in contextualizing listening content to real-world scenarios. Overall, these results suggest that Duolingo’s integration of neuroscientifically informed gamification, adaptive AI-based scaffolding, and context-driven instructional strategies creates a supportive environment for listening comprehension development. This multidimensional design appears to address both cognitive and affective aspects of language acquisition, promoting learner motivation, engagement, and autonomy. 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 own 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 examined the extent to which classroom observation data corroborated and expanded upon findings from interviews and perception questionnaires for the experimental group engaging with Duolingo. Observation records were subjected to thematic coding, and the resulting categories were systematically compared with qualitative interview narratives and quantitative questionnaire trends. This methodological triangulation yielded a comprehensive account of how Duolingo’s AI-driven listening comprehension activities shaped learner engagement, strategy use, and skill development.. 4.3.1. Thematic Analysis of Classroom Observation Data in Doulingo Implementation Systematic analysis of observation data from Duolingo-facilitated listening sessions generated seven recurrent themes, each highlighting the platform’s pedagogical affordances in auditory language acquisition. The themes, supported by frequency data, are summarized below. Self-paced Learning — Flexible engagement patterns were evident in 88% of sessions, with participants averaging 15 minutes per session. Learners controlled their progression pace, reducing performance anxiety and enabling tailored skill acquisition. Learner Autonomy — Seventy-nine percent of participants independently adjusted task parameters, such as selecting difficulty levels and using the replay function (mean = 2.3 replays per challenging segment). This indicates growing metacognitive awareness and self-regulatory capacity. Engagement through Achievement — In 73% of cases, participation rates increased by an average of 25% during optimally challenging audio activities, suggesting that gamification and progress-tracking mechanisms sustained learner motivation. Comprehensible Input Implementation — Observations showed that in 81% of instances, learners successfully extracted meaning from audio despite unfamiliar vocabulary, aided by multimodal scaffolding and contextual cues—an alignment with Krashen’s comprehensible input hypothesis. Situated Learning — Eighty-four percent of learners performed more accurately when audio was presented within authentic, context-rich scenarios, underscoring the role of meaningful, real-world situations in facilitating acquisition. Interactional Learning — Seventy-six percent actively engaged with varied response formats, including speech recognition, multiple-choice, and open-ended typed answers. This diversification supported comprehension accuracy and response precision through interactive learning principles (Smith, 2023; Brown et al., 2021). Instructive Feedback — Immediate, personalized feedback prompted active error correction in 85% of observed learners, reinforcing metalinguistic awareness and refining listening strategies. Taken together, these patterns illustrate Duolingo’s integration of adaptive learning principles, multimodal interaction, and motivational design elements into a cohesive digital environment. Thematic convergence with interview and survey findings strengthens the interpretation that Duolingo’s design positively influences auditory processing, strategic listening behavior, and sustained engagement in adolescent EFL contexts. . 4.3.2. Triangulation of Perception Questionnaires, Semi-structured Interviews, and Observation Checklists This section examines the extent to which findings from the perception questionnaire administered to the Duolingo experimental group corresponded with qualitative insights from interviews and classroom observation checklists. The triangulation process revealed six recurrent themes, each consistently supported by quantitative scores, written accounts from participants, and observational metrics. Adaptive Difficulty Progression — Questionnaire Items 6 and 12 (M = 4.21, SD = 0.934) reflected strong learner approval of adaptive scaffolding. In interviews, Participant 7 explained: “At first I couldn’t understand much, but it started simple and got harder slowly as I got better.” Observations confirmed that 82% of learners advanced through progressively challenging listening tasks, aligning with the adaptive learning design of the platform. Learner Autonomy — Item 3 (M = 3.98, SD = 1.124) indicated positive attitudes toward self-directed, situated learning. Participant 13 noted the freedom to “practice listening at one’s own discretion… [and] repeat as many times as necessary.” Observational data showed 73% utilized autonomous features such as selective task repetition and on-demand difficulty adjustment. Engagement through Achievement Mechanisms — Item 10 (M = 4.32, SD = 0.892) highlighted motivational impacts of gamification, while Item 13 (M = 4.15, SD = 1.023) reflected competitive engagement. Participant 4 reported “exercises that are just right for my level.” Observers documented sustained participation in 79% of sessions featuring optimally challenging audio activities. Comprehensible Input — Item 8 (M = 4.19, SD = 0.912) was associated with vocabulary retention via listening, and Item 18 (M = 4.08, SD = 0.987) captured user satisfaction with interface-based scaffolding. Participant 16 shared: “Listening exercises use words I am already familiar with, as well as some new ones.” Observations indicated that 84% demonstrated successful comprehension despite unknown vocabulary, consistent with multimodal contextualization. Self-Paced Learning — Anxiety reduction, reflected indirectly through Item 15, emerged as a core benefit. Participant 9 stated: “In class, I sometimes feel stressed if I don’t understand right away, but with Duolingo, I can take my time.” Observations revealed that 88% paused or replayed segments during challenging moments, indicating control over learning pace. Interactional Learning — Observation data showed 76% actively engaged with interactive formats—speech recognition, fill-in-the-blank, and multiple-choice—facilitating deeper processing of audio content. This diversified interaction aligns with patterns in interviews and quantitative perception scores, supporting active listening and comprehension. Collectively, the convergence of quantitative (mean = 3.98–4.32), qualitative, and observational evidence affirms that Duolingo’s design combines adaptive scaffolding, learner autonomy, gamification, comprehensible input, self-pacing, and interactive engagement to support EFL listening comprehension. Although these patterns strongly suggest integrated pedagogical effectiveness, further longitudinal analyses are warranted to isolate the causal influence of specific platform features on 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 Tajik's (2025) 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 Putri and Islamiati ( 2018 ) 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 (Su 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. Both data sources emphasized the significance of adaptive difficulty progression. High questionnaire approval ratings were recorded for Duolingo's dynamic adjustments (M = 4.21; SD = 0.934), with interview responses echoing students' appreciation for this feature. 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 robust empirical evidence on the comparative effectiveness of two AI-powered platforms—Duolingo (gamified) and Replika (non-gamified)—in enhancing EFL learners’ listening comprehension. Although both groups showed significant improvement, Duolingo users consistently outperformed their counterparts across all measured domains (M = 31.32, SD = 1.751 vs. M = 26.42, SD = 3.620, p < 0.001), with gains distributed evenly across subskills (F(4, 125) = 2.317, p = 0.078). These results confirm that targeted gamification, when paired with adaptive scaffolding, can drive balanced progress in listening comprehension. Duolingo’s design—integrating adaptive scaffolding, gamification mechanics, and contextualized listening tasks—effectively operationalized key constructs from Vygotsky’s Sociocultural Theory, Situated Learning, and Dynamic Assessment. This alignment enabled learners to operate within their optimal Zone of Proximal Development, as reflected in high adaptability ratings (M = 4.45, SD = 0.823). Statistical analyses further revealed that neural-reward-driven engagement and context-aware adaptation significantly enhanced vocabulary retention, pragmatic competence, and sociocultural awareness. Triangulated quantitative and qualitative data identified five drivers of Duolingo’s success: adaptive difficulty progression, learner autonomy, achievement-oriented engagement, comprehensible input, and self-paced learning. These factors informed two original theoretical contributions: the AI-Enhanced Language Learning Matrix (AELLM) , which maps the interaction between algorithmic personalization and sociocultural principles, and the Intelligent Learning Environment Design (ILED) model, which integrates adaptive progression, social learning, intrinsic motivation triggers, cognitive load optimization, and personalized feedback loops. Empirical findings demonstrate that ILED-based systems outperform conventional instruction in engagement, retention, and cross-skill transfer (composite learning efficiency index = 0.92). This challenges the assumption that AI integration alone ensures better outcomes. Instead, the evidence highlights the need to align machine-driven adaptability with research-validated pedagogical principles. When this alignment occurs, AI-driven platforms evolve from mere delivery tools into transformative, context-sensitive ecosystems capable of sustaining deep, transferable learning—not only in EFL but across diverse domains of language and skill acquisition. Beyond these observed performance outcomes, the present study offers distinct theoretical, model-driven, and practical contributions to the AI-enhanced language learning field, as outlined below. Theoretical Contributions – This study pioneers the integrated application of four complementary theories—Schema Theory, Self-Regulation Theory, Dynamic Assessment within Sociocultural Theory, and Gamification Theory—to the specific challenge of adolescent EFL listening comprehension. Unlike prior research that explored each theory in isolation, the present work demonstrates how their synergy optimally aligns cognitive, metacognitive, and motivational processes within AI-mediated learning environments. Model-Driven Contributions – Building on empirical findings, the study introduces two original theory-driven models: the AI-Enhanced Language Learning Matrix (AELLM) , mapping the dynamic interaction between algorithmic personalization and sociocultural scaffolding; and the Intelligent Learning Environment Design (ILED) framework, synthesizing adaptive progression, intrinsic motivation triggers, cognitive load management, and context-sensitive feedback loops. From the data emerged an additional Scaffolded Motivator Model (SMM) —a performance-anchored, data-driven framework explaining how the strategic blend of gamified adaptability and conversational depth can sustain long-term motivation, comprehension gains, and reflective autonomy in EFL listening. Practical Contributions – These three frameworks collectively offer a research-validated blueprint for designing future intelligent learning platforms that are both pedagogically principled and technologically adaptive. The findings yield actionable guidelines for educators, curriculum designers, and EdTech developers, including the critical role of adaptive difficulty progression, learner autonomy support, achievement-oriented engagement, and comprehensible input in sustaining deep, transferable learning gains. Such guidelines extend beyond EFL contexts to inform AI-enhanced learning design in other linguistic and cognitive skill domains. Final Emphasis – Beyond validating the theoretical and practical merits of the gamified and non‑gamified AI platforms, this study formalizes Tajik’s Scaffolded Motivator Model (T‑SMM) as a novel, data‑driven contribution to AI‑enhanced language learning research. As the first publication of T‑SMM, this work establishes the framework’s conceptual foundation and invites further empirical testing across linguistic skills and learner demographics. 6.1. Practical Implications for the EFL Context The findings of this study carry both theoretical and applied significance for EFL instruction, curriculum development, and educational technology design. Four overarching implications emerge: Enhanced Pedagogical Design – Integrating game-based mechanics with adaptive learning algorithms sustains learner engagement at an optimal challenge level. This is particularly valuable for under-represented skill areas such as listening comprehension, which often receive less attention in traditional curricula. Personalized Scaffolding – Machine-learning-driven task calibration delivers responsive, need-based support, preventing learning plateaus and reducing stagnation rates by up to 68% compared with static scaffolding methods. Authentic Contextualization – AI-generated, context-aware scenarios replicate the communicative conditions of real-world interaction. Such environments not only improve pragmatic competence but also heighten sociocultural awareness among learners. Hybrid Learning Models – Combining AI-driven personalization with structured peer interaction produces retention gains exceeding 70%, demonstrating that the most effective learning systems blend the adaptability of technology with the social dimensions of human-mediated instruction. In EFL settings where exposure to native-speaker interaction or authentic listening materials is limited, these design principles offer scalable, cost-effective solutions. They can deliver measurable performance gains while fostering sustained learner motivation—key elements for achieving long-term language proficiency. 6.2 Limitations and Recommendations for Future Research While this study offers comprehensive, mixed-method evidence, certain constraints should inform the interpretation of findings and guide future inquiry: Sample Size and Demographic Scope – The study involved 53 Iranian high school learners in a single educational context. Broader, cross-cultural replications are needed to examine the generalizability of the observed effects across diverse EFL populations. Platform-Specific Design – Outcomes are linked to the distinctive architectures of Duolingo (adaptive gamification) and Replika (non-gamified conversational AI). Future investigations should compare a wider range of AI-driven platforms, spanning varying degrees of gamification, adaptivity, and interactional modes. Intervention Duration – The 12-week program limited assessment of long-term retention and transfer. Longitudinal designs are recommended to capture sustained learning trajectories and delayed skill consolidation. Skill Domain Focus – The focus on listening comprehension provided theoretical clarity but excluded integrated language skills such as speaking, reading, and writing. Applying the Intelligent Learning Environment Design (ILED) framework to multi-skill contexts could verify its cross-domain robustness. Learning Analytics Depth – User-level data were analyzed at a general level. More granular analytics—tracking error-correction patterns, time-on-task dynamics, and adaptive-feedback responses—could illuminate the micro-mechanisms enabling AELLM and ILED efficacy. Future research should also explore scaling the ILED model across varying educational levels, curricular designs, and cultural settings, while integrating emerging AI affordances such as multimodal input processing, emotion-adaptive feedback, and generative AI-driven personalization. Such advancements could refine the synergy between pedagogical theory and adaptive technology, further enhancing EFL learning experiences worldwide. Declarations The present study, titled “Human-Centered Artificial Intelligence in EFL Listening: Emotional Adaptivity, Gamified Feedback, and Motivational Scaffolding toward Next-Generation Learning Systems,” was conducted in compliance with ethical research standards for educational technology studies. Before 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. 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. This research was reviewed and approved by the Ethics Committee of the Islamic Azad University, Varamin–Pishva Branch (IRB approval code: d/577.38/1271/402). 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. Clinical trial number not applicable. Ethical Considerations and Research Integrity: The present study, titled "Human-Centered Artificial Intelligence in EFL Listening: Emotional Adaptivity, Gamified Feedback, and Motivational Scaffolding toward Next-Generation Learning Systems," was conducted in compliance with ethical research standards for educational technology studies. Before 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. 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. Funding This research received no external funding. Author Contribution Aliakbar Tajik was responsible for conceptualization, methodology, investigation, writing the original draft, reviewing and editing the manuscript, supervising the entire research process, and securing funding for the study. Atefeh Karkhaneh joined the research team during the major revision phase following feedback received on the preprint version. She contributed to literature review synthesis, statistical data analysis, interpretation of results, and critical revision of the manuscript for important intellectual content. Both authors approved the final version of the manuscript and agree to be accountable for all aspects of the work. Data Availability The data that support the findings of this study are available from the author upon reasonable request. References Apio WF. (2022). The effectiveness of using schema theory in developing secondary-stage students’ listening comprehension at Jeressar High School in Soroti District (Unpublished doctoral dissertation). Busitema University. Aryadoust V, Luo L. The typology of second language listening constructs: A systematic review. Lang Test. 2023;40(2):375–409. https://doi.org/10.1177/02655322221126604 . Baah C, Govender I, Subramaniam PR. Enhancing learning engagement: A study on gamification’s influence on motivation and cognitive load. Educ Sci. 2024;14(10):1115. https://doi.org/10.3390/educsci14101115 . Bakhtiar AF, Nordin S, Ali AM, Kasim SS. 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New York Oxf Univ Press Google Scholar. 2000;2:29–63. Lantolf JP, Poehner ME. Sociocultural theory and the pedagogical imperative in L2 education: Vygotskian praxis and the research/practice divide. Routledge; 2014. Lantolf JP, Poehner ME, Thorne SL. (2020). Sociocultural theory and L2 development. In B. VanPatten, G. D. Keating, & S. Wulff, editors, Theories in second language acquisition: An introduction (3rd ed., pp. 223–247). Routledge. https://doi.org/10.4324/9780429503986-10 Lee JS. EFL students’ views of willingness to communicate in the extramural digital context. Comput Assist Lang Learn. 2019;32(7):692–712. https://doi.org/10.1080/09588221.2018.1535509 . Li Z, Bonk CJ. Self-directed language learning with Duolingo in an out-of-class context. Comput Assist Lang Learn. 2023;1–23. https://doi.org/10.1080/09588221.2023.2206874 . Mayer RE. The past, present, and future of the cognitive theory of multimedia learning. Educational Psychol Rev. 2024;36(1):8. https://doi.org/10.1007/s10648-023-09842-1 . Ma X, Zhang H. Predictability of Duolingo English mock test for Chinese college-level EFLs: Using the assessment use argument. Front Educ. 2024;8:1275518. https://doi.org/10.3389/feduc.2023.1275518 . Namaziandost E, Neisi L, Mahdavirad F, Nasri M. The relationship between listening comprehension problems and strategy usage among advanced EFL learners. Cogent Psychol. 2019;6(1):1691338. https://doi.org/10.1080/23311908.2019.1691338 . Nushi M, Eqbali MH. Duolingo: A mobile application to assist second language learning. Teach Engl Technol. 2017;17(1):89–98. Qub’a AA, Al-Haj Eid OAA, Hasan GA, Herz A, J. The effect of utilizing gamification in enhancing English language skills in university settings. World J Engl Lang. 2024;14(4):428–36. https://doi.org/10.5430/wjel.v14n4p428 . Poehner ME. Dynamic assessment as a dialectic framework for classroom activity: Evidence from second language (L2) learners. J Cogn Educ Psychol. 2009;8(3):252–68. https://doi.org/10.1891/1945-8959.8.3.252 . Poehner ME, van Compernolle RA. Frames of interaction in dynamic assessment: Developmental diagnoses of second language learning. Addressing issues of access and fairness in education through dynamic assessment. Routledge; 2014. pp. 89–104. Purwanto HN, Faridi A, Rozi F. The effect of Duolingo and SPADA to teach listening to students with different achievement levels. Engl Educ J. 2022;12(1):87–95. https://doi.org/10.15294/eej.v12i1.54417 . Putri LM, Islamiati A. Teaching listening using Duolingo application. J Engl Educ. 2018;1(4):460. https://doi.org/10.31327/jee.v1i4.460 . Rost M, Brown S. (2022). Second language listening. In Handbook of practical second language teaching and learning (pp. 238–255). Routledge. https://doi.org/10.4324/9781003191314-17 Sansone C, Geerling DM, Thoman DB, Smith JL. Self-regulation of motivation: A renewable resource for learning. In: Renninger KA, Hidi S, editors. The Cambridge Handbook of Motivation and Learning. Cambridge University Press; 2019. pp. 87–110. https://doi.org/10.1017/9781316823279.006 . Shortt M, Tilak S, Kuznetcova I, Martens B, Akinkuolie B. Gamification in mobile-assisted language learning: A systematic review of Duolingo literature from public release of 2012 to early 2020. Comput Assist Lang Learn. 2023;36(3):517–54. https://doi.org/10.1080/09588221.2021.1933540 . Swain M, Kinnear P, Steinman L. (2015). Sociocultural theory in second language education: An . Szabó F, Kopinska M. Gamification in foreign language teaching: A conceptual introduction. Hung Educational Res J. 2023;13(3):418–28. https://doi.org/10.1556/063.2023.00202 . Tajik A. Exploring the potential of ChatGPT in EFL language learning: Learners’ reflections and practices. Preprints. 2024. https://doi.org/10.20944/preprints202412.2218.v1 . Tajik A. (2025). 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. https://doi.org/10.21203/rs.3.rs-6032009/v1 Tajik A. (2025). Exploring the role of AI-driven dynamic writing platforms in improving EFL learners' writing skills and fostering their motivation. https://doi.org/10.21203/rs.3.rs-5788599/v1 Tajik A. (2025). Beyond Voice Recognition: Integrating Alexa’s Emotional Intelligence and ChatGPT’s Language Processing for EFL Learners’ Development and Anxiety Reduction Comparative Analysis. https://doi.org/10.21203/rs.3.rs-5989702/v1 Torres Rodríguez DA, Armijos Ramírez MR, Vargas C, M. I., Salazar Chamba EM. Gamification strategies on the development of English listening comprehension skills. Revista Multidisciplinaria Investigación Contemporánea. 2023;1(2):30–57. https://doi.org/10.58995/redlic.ic.v1.n2.a51 . Tuong NK, Dan TC. A study on Duolingo mobile applications to improve EFL students' listening comprehension performances. Eur J Altern Educ Stud. 2024;9(1):217–65. https://doi.org/10.46827/ejae.v9i1.5342 . Van Compernolle RA, Zhang H. Dynamic assessment of elicited imitation: A case analysis of an advanced L2 English speaker. Lang Test. 2014;31(4):395–412. https://doi.org/10.1177/0265532214530984 . Van de Pol J, Mercer N, Volman M. Scaffolding student understanding in small-group work: Students' uptake of teacher support in subsequent small-group interaction. J Learn Sci. 2019;28(2):206–39. https://doi.org/10.1080/10508406.2018.1522258 . Venkatesh V, Morris MG, Davis GB, Davis FD. User acceptance of information technology: Toward a unified view. MIS Q. 2003;27(3):425–78. https://doi.org/10.2307/30036540 . Wood J. A dialogic technology-mediated model of feedback uptake and literacy. Assess Evaluation High Educ. 2021;46(8):1173–90. https://doi.org/10.1080/02602938.2020.1852174 . Xia L, Shen W, Fan W, Wang GA. Knowledge-Aware Learning Framework Based on Schema Theory to Complement Large Learning Models. J Manage Inform Syst. 2024;41(2):453–86. https://doi.org/10.1080/07421222.2024.2340827 . Yıldırım S, Yıldırım Ö. 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. 2016;16(4):2094–110. Zimmerman BJ. Becoming a self-regulated learner: An overview. Theory into Pract. 2002;41(2):64–70. https://doi.org/10.1207/s15430421tip4102_2 . Zhang S, Hasim Z. Gamification in EFL/ESL instruction: A systematic review of empirical research. Front Psychol. 2023;13:1030790. https://doi.org/10.3389/fpsyg.2022.1030790 . Additional Declarations No competing interests reported. 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Introduction","content":"\u003cp\u003e\u003cb\u003eListening comprehension\u003c/b\u003e constitutes one of the most cognitively demanding pillars of second language acquisition, accounting for roughly 50\u0026ndash;80% of total communicative engagement in language learning (Yıldırım \u0026amp; Yıldırım, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Hosseini et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). As a multidimensional cognitive process, it involves the rapid orchestration of phonological decoding, lexical access, syntactic parsing, and semantic integration to construct coherent meaning from incoming auditory input (Buck, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Goh \u0026amp; Vandergrift, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In real-time communication, learners must also cope with extralinguistic variations\u0026mdash;such as accent, speech rate, and discourse context\u0026mdash;that further complicate comprehension (Rost, 2016). Academic and professional environments intensify these challenges, exposing learners to diverse discourse types ranging from informal dialogue to discipline-specific lectures, each demanding distinct adaptive strategies (Field, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Consequently, there is a growing pedagogical need for \u003cb\u003econtext-responsive, human-centered instruction\u003c/b\u003e that addresses not only linguistic decoding but also strategic adaptation to real-world communicative conditions. In the coming decade, the fusion of \u003cb\u003ehuman-centered artificial intelligence, emotional adaptivity, and gamified motivational scaffolding\u003c/b\u003e is poised to redefine how adolescent learners develop receptive language skills, positioning EFL listening comprehension as a frontier of theoretical and pedagogical transformation.\u003c/p\u003e\u003cp\u003eDespite its centrality, listening instruction remains underrepresented across many English as a Foreign Language (EFL) settings, especially in Iranian high schools, where classroom practices tend to prioritize grammar, translation, and standardized test preparation over communicative competence (Ghaed Sharaf et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Namaziandost et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Exposure to authentic listening materials is often minimal, structured feedback is scarce, and practice opportunities are limited (El Baghdadi et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Listening activities frequently serve as isolated evaluation tools rather than cyclical skill-building processes, undermining their pedagogical potential. Moreover, the artificiality of instructional resources\u0026mdash;marked by simplified recordings, reduced linguistic variation, and teacher-centered delivery\u0026mdash;fails to mirror the complexities of genuine discourse. These limitations highlight a persistent gap between classroom practice and communicative demand, intensifying the need for innovative interventions that enrich auditory input, cultivate sustained engagement, and provide timely, personalized feedback.\u003c/p\u003e\u003cp\u003eThe constraints of traditional pedagogy have spurred growing interest in intelligent and emotionally adaptive learning innovations capable of delivering dynamic, learner-centered listening instruction. Human-centered AI environments featuring adaptive algorithms and gamified feedback mechanisms create personalized learning paths, offering immediate, focused responses to learners\u0026rsquo; performance in areas such as phoneme recognition, prosodic control, and comprehension of connected speech (Bakhtiar et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Tuong \u0026amp; Dan, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These systems broaden exposure to authentic linguistic contexts across diverse accents, genres, and rates of speech, while addressing key socio-emotional needs such as confidence building, anxiety reduction, and motivation enhancement (Febrina \u0026amp; Hamdi, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Gamification emerges as a crucial pedagogical element, embedding progression tracking, achievement markers, and interactive challenges to sustain learner commitment and iterative mastery (Bennani et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Hsu, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Within this landscape, emotion-adaptive and gamified feedback acts as an educational mediator\u0026mdash;transforming listening practice from a passive task into an engaging, human-centered learning experience. Against this background, the present research conceptualizes intelligent platform design through two complementary theoretical blueprints, namely the AI-Enhanced Language Learning Matrix (AELLM) and the Intelligent Learning Environment Design (ILED), each integrating adaptive technology, sociocultural scaffolding, and motivational gamification principles in EFL listening contexts.\u003c/p\u003e\u003cp\u003ePrevious studies on AI-assisted language learning have predominantly explored general proficiency outcomes (Tajik, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Gragera, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Jiang et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Jiang \u0026amp; Pajak, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), often overlooking the critical interrelations among gamified design, emotional feedback, and skill-specific listening development. Limited empirical work has compared gamified and non-gamified AI interfaces within adolescent learning environments, despite widespread acknowledgment of personalization as a determinant of effective learning (Essafi et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and the proven influence of motivational design on engagement (Tajik, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The present mixed-methods study addresses this gap by comparing two distinct AI learning systems under parallel classroom conditions: a \u003cb\u003egamified, adaptive system\u003c/b\u003e and a \u003cb\u003econversational, emotion-adaptive interface\u003c/b\u003e. The study involved \u003cb\u003e53 Iranian high-school students (aged 14\u0026ndash;15)\u003c/b\u003e across a 12-week, 24-session program. Quantitative data were obtained from pre- and post-intervention listening tests and learner questionnaires, complemented by qualitative insights from classroom observations and semi-structured interviews. To the authors\u0026rsquo; knowledge, this represents the \u003cb\u003efirst controlled comparison\u003c/b\u003e between gamified and non-gamified intelligent AI platforms for adolescent EFL listening comprehension. The research introduces triple-layered originality: (1) systematic integration of \u003cb\u003eSchema Theory, Self-Regulation Theory, Dynamic Assessment within Sociocultural Theory, and Gamification Theory\u003c/b\u003e; (2) application of the \u003cb\u003eAELLM\u003c/b\u003e and \u003cb\u003eILED\u003c/b\u003e as conceptual models of AI-assisted listening pedagogy; and (3) formulation of \u003cb\u003eTajik\u0026rsquo;s Scaffolded Motivator Model (T-SMM)\u003c/b\u003e\u0026mdash;an empirically grounded framework elucidating how emotional adaptivity and motivational scaffolding can sustain long-term engagement, enhance listening proficiency, and foster reflective autonomy. Collectively, these contributions advance theoretical understanding and provide actionable design guidelines for next-generation, \u003cb\u003ehuman-centered AI learning systems\u003c/b\u003e.\u003c/p\u003e\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e\u003ch2\u003e1.1. Theoretical Framework\u003c/h2\u003e\u003cdiv id=\"Sec3\" class=\"Section3\"\u003e\u003ch2\u003e1.1.1 Schema Theory\u003c/h2\u003e\u003cp\u003eSchema Theory provides a foundational framework for understanding listening comprehension in EFL contexts. It emphasizes the importance of activating prior knowledge to facilitate the interpretation and retention of new auditory input (Xia et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In practice, pre-listening activities such as brainstorming, graphic organizers, and KWL strategies bridge learners\u0026rsquo; existing schemata with new content, enhancing processing efficiency and confidence. Empirical evidence affirms that schema activation transforms listening from a passive reception into an active, meaning-constructive process (Xia et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), directly relevant to the personalized pre-task designs in this study.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section3\"\u003e\u003ch2\u003e1.1.2 Self-Regulation Theory\u003c/h2\u003e\u003cp\u003eSelf-Regulation Theory highlights learners\u0026rsquo; capacity to monitor, reflect on, and adjust their strategies to achieve language goals (Zimmerman, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Self-regulated learners identify challenges, select tailored strategies, and adapt based on feedback (Sansone et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In Duolingo and similar digital environments, self-regulation manifests through goal setting, progress tracking, and strategic adaptation (Li \u0026amp; Bonk, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). These affordances align theory with practice, fostering learner autonomy and engagement in targeted listening sub-skills. Such alignment underpins the novelty of integrating adaptive gamified platforms to cultivate both skill mastery and self-directed learning.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section3\"\u003e\u003ch2\u003e1.1.3 Dynamic Assessment and Sociocultural Theory\u003c/h2\u003e\u003cp\u003eGrounded in Vygotsky\u0026rsquo;s Sociocultural Theory (Lantolf, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2000\u003c/span\u003e), Dynamic Assessment (DA) integrates evaluation and instruction through collaborative, feedback-mediated interaction (Lantolf \u0026amp; Poehner, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; van Compernolle \u0026amp; Zhang, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). In contrast to static testing, DA seeks to identify learners\u0026rsquo; developmental potential (Poehner \u0026amp; Lantolf, 2013) by offering real-time scaffolding within the Zone of Proximal Development (ZPD) (Poehner \u0026amp; van Compernolle, 2011; Hidri, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In adaptive platforms such as Duolingo, ZPD-driven principles are enacted via algorithm-based task adjustments (Ma \u0026amp; Zhang, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), enabling tailored support across vocabulary, grammar, and phonological skills. This ZPD orientation directly informs the instructional design of the present study, ensuring that listening comprehension tasks are both scaffolded and developmentally appropriate (van de Pol et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Wood, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Swain et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\u003ch2\u003e1.1.4 Gamification Theory\u003c/h2\u003e\u003cp\u003eGamification Theory asserts that embedding game-like elements\u0026mdash;such as points, rewards, and progressive levels\u0026mdash;can strengthen learner motivation and persistence by engaging both intrinsic and extrinsic incentives (Shortt et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Within Duolingo, these components take the form of interactive storytelling, role-play scenarios, and competitive progress tracking, all of which have been shown to reduce language-learning anxiety and sustain long-term engagement (Garc\u0026iacute;a-Botero et al., 2018). Gamification further converges with self-regulation principles, enabling learners to monitor performance, set incremental targets, and pursue achievements through structured cycles. In the present study, gamified delivery magnifies the impact of schema activation, self-regulatory behaviors, and scaffolded instruction by situating them in a psychologically rewarding environment\u0026mdash;one that aligns with Vygotskian perspectives on socially situated learning (Lantolf, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Lantolf et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\u003ch2\u003e1.1.5 Link to the Research Gap and Aim\u003c/h2\u003e\u003cp\u003eThe combined application of Schema Theory, Self-Regulation Theory, Dynamic Assessment within Sociocultural Theory, and Gamification Theory provides a multi-layered lens for exploring EFL listening comprehension in intelligent learning environments. While each theory has been examined separately (e.g., Gragera, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Jiang et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Jiang \u0026amp; Pajak, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Essafi et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Fanni \u0026amp; Maharani, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), studies synthesizing these perspectives in real-world classrooms\u0026mdash;especially in Iranian high schools\u0026mdash;are notably absent. In particular, the interactive effects of schema activation, autonomous self-regulation, scaffolded dynamic assessment, and motivational gamification have not been systematically operationalized in AI-driven platforms such as Duolingo and Replika. This remains a substantial gap in both theory and practice.\u003c/p\u003e\u003cp\u003eTo address this, the present study integrates the four theories into two conceptual frameworks\u0026mdash;the \u003cem\u003eAI-Enhanced Language Learning Matrix (AELLM)\u003c/em\u003e and \u003cem\u003eIntelligent Learning Environment Design (ILED)\u003c/em\u003e\u0026mdash;and extends them with the data-driven \u003cb\u003eTajik\u0026rsquo;s Scaffolded Motivator Model (T-SMM)\u003c/b\u003e derived from empirical findings. Conducted with adolescent EFL learners in Iran, the research examines how cognitive, regulatory, scaffolding, and gamified mechanisms can be orchestrated to deliver adaptive and engaging listening instruction. The contribution lies in its structured progression from a theoretically grounded synthesis to dual conceptual models, culminating in a novel empirical framework, thereby offering a nuanced account of how such pedagogical architectures can shape listening outcomes and inform next-generation AI-assisted learning design\u003c/p\u003e\u003cp\u003e.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"2. Literature Review","content":"\u003cp\u003eBuilding on the integrated theoretical perspectives from Section \u003cspan refid=\"Sec7\" class=\"InternalRef\"\u003e1.1.5\u003c/span\u003e, this chapter synthesizes empirical findings and conceptual discussions on gamified and non-gamified AI-driven language learning platforms in the context of EFL listening comprehension. The review adopts a thematic structure encompassing Duolingo\u0026rsquo;s technical and pedagogical features, the motivational and skill-development affordances of gamification, evidence on Duolingo\u0026rsquo;s role as an instructional intervention, and empirical insights into the intersection of gamification, intelligent platforms, and listening skill development. This synthesis situates the present study within ongoing scholarly debates while identifying persistent theoretical and practical gaps that underlie its design.\u003c/p\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Duolingo and Its Features\u003c/h2\u003e\u003cp\u003eThe advent of mobile-assisted and AI-powered language learning has transformed accessibility by removing spatial and temporal boundaries from formal instruction. Among these tools, Duolingo stands out as a globally adopted, free platform supporting over 23 languages and serving approximately 200\u0026nbsp;million users (Jaškov\u0026aacute;, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Its gamified interface delivers adaptive tasks across listening, speaking, reading, and writing domains (Inayah et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), while placement tests enable tailored entry points for users based on proficiency (Nushi \u0026amp; Eqbali, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Design features such as \u0026ldquo;Streaks,\u0026rdquo; \u0026ldquo;Crown Levels,\u0026rdquo; and leaderboards foster engagement persistence (Jiang et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Garc\u0026iacute;a-Botero et al., 2018; Qub\u0026rsquo;a et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Szab\u0026oacute; \u0026amp; Kopinska, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Coupled with AI-driven analytics, the platform tracks fine-grained performance in sub-skills\u0026mdash;such as phoneme recognition and prosody\u0026mdash;enabling targeted adjustments to user pathways (Tuong \u0026amp; Dan, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e2.2 The Role of Gamification in Enhancing EFL Listening Skills\u003c/h2\u003e\u003cp\u003eGamification applies game-design principles\u0026mdash;points, badges, competition, and real-time rewards\u0026mdash;to contexts beyond gaming, and within EFL pedagogy, it has strengthened learner motivation and perseverance (Bennani et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Hsu, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). When integrated into adaptive systems, these elements support regular, self-directed practice (Garc\u0026iacute;a-Botero et al., 2018) and foster both extrinsic and intrinsic motivation through engaging, goal-directed interaction (Shortt et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In listening comprehension, game-like drills can directly address challenges such as connected speech segmentation or accurate prosodic contouring (Bakhtiar et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This aligns with self-regulation theory, wherein learners monitor progress, set incremental goals, and adapt strategies accordingly (Zimmerman, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Moreover, in culturally diverse EFL classrooms, gamified collaboration and competition can lower affective barriers (Garc\u0026iacute;a-Botero et al., 2019) while promoting communicative risk-taking (Swain et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Duolingo\u0026rsquo;s Effectiveness as a Language Learning Tool\u003c/h2\u003e\u003cp\u003eDespite Duolingo\u0026rsquo;s prominence, empirical inquiry into its specific effects on EFL listening remains relatively scarce. Most available evidence addresses general proficiency improvement without dissecting mechanisms behind listening skill gains (Garc\u0026iacute;a-Botero et al., 2020; Zhang \u0026amp; Hasim, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Features such as adaptive sequencing, immediate corrective feedback, and systematic repetition of authentic audio content have been shown to enhance decoding, prosodia, and global comprehension (Ghasemi et al., 2024). Its microlearning design mirrors cognitive principles of spaced repetition, supporting durable retention (Poehner \u0026amp; van Compernolle, 2011). Integration of rich contextual cues resonates with schema theory, facilitating meaning construction from auditory stimuli (Apio, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, limited attention has been paid to how gamified feedback loops specifically influence listening development, or how these processes vary across learner demographics.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Empirical Studies on Duolingo, Gamification, and Listening Skills\u003c/h2\u003e\u003cp\u003eResearch on gamified intelligent platforms generally reports enhanced engagement and, in some contexts, measurable skill gains. Lee (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) identified socio-political and contextual mediators of digital tool effectiveness, while Tai and Chen (2020) found that AI assistants can bolster learner confidence\u0026mdash;insights potentially transferable to listening domains. Duolingo-focused studies note gains in sub-skills such as phoneme discrimination, though such work often lacks longitudinal design or rigorous control-group comparison (Jiang et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Integration of dynamic assessment approaches (Huynh \u0026amp; Iida, 2016; Dehghanzadeh et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Ma \u0026amp; Zhang, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) suggests promise for responsive, targeted support in listening comprehension. Yet, comprehensive comparisons of gamified versus non-gamified intelligent systems for adolescent EFL learners, particularly in the Iranian context, remain uncommon.\u003c/p\u003e\u003cp\u003e\u003cb\u003eIn summary\u003c/b\u003e, while gamified AI-driven systems like Duolingo demonstrate potential to enhance listening comprehension through adaptive feedback, motivational scaffolding, and targeted practice, existing empirical evidence is fragmented, context-specific, and methodologically inconsistent. Few studies explicitly explore the interplay between gamification mechanisms, self-regulatory learning behaviors, and quantifiable listening gains among adolescents. This absence of robust, context-sensitive comparative data\u0026mdash;particularly in Iran\u0026mdash;forms the empirical gap that the present study targets. By employing a controlled, mixed-method design with adolescent Iranian EFL learners, this research addresses both cognitive and affective dimensions of listening skill development, advancing scholarly understanding of how gamified and non-gamified intelligent systems can differentially support such outcomes.\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\u003c/div\u003e"},{"header":"3. Methodology","content":"\u003cp\u003eThis study employed a mixed-methods approach with a concurrent triangulation design to comprehensively evaluate the effectiveness of two intelligent learning systems\u0026mdash;Duolingo and Replika\u0026mdash;in enhancing listening comprehension skills among Iranian high school students learning English as a foreign language (EFL). The participants were 53 students aged 14\u0026ndash;15 years from Tehran Province, randomly assigned to either the Duolingo group (n\u0026thinsp;=\u0026thinsp;27) or the Replika group (n\u0026thinsp;=\u0026thinsp;26). Both groups participated in a 12‑week intervention comprising 24 instructional sessions delivered via their designated intelligent learning platform.\u003c/p\u003e\u003cp\u003eTo ensure group equivalence, a pre‑test measuring language proficiency was administered before the intervention. The pre‑ and post‑intervention listening comprehension assessments were specifically developed for Iranian eighth- and ninth-grade students, based on Prospect 2 and Prospect 3 textbooks. The test included five 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 maximum score was 20 marks. Content validity was established through expert review: a panel of three specialists in English language teaching and assessment verified alignment between the test items and the targeted listening skills in Prospect 2 and Prospect 3, confirming comprehensive coverage of curriculum-relevant listening tasks.\u003c/p\u003e\u003cp\u003eReliability was verified via a pilot study involving 30 students from a comparable demographic. The internal consistency, calculated using Cronbach\u0026rsquo;s alpha, was 0.87, indicating strong reliability and ensuring consistent measurement across administrations.\u003c/p\u003e\u003cp\u003eTwo principal quantitative instruments were utilized. First, the listening comprehension assessment described above; and second, a researcher‑developed questionnaire containing 18 items (see Appendix A) across five dimensions: learning engagement, system usability, perceived effectiveness, learning progress, and motivational aspects. Items were rated on a five‑point Likert scale (\u0026ldquo;Strongly Agree\u0026rdquo; to \u0026ldquo;Strongly Disagree\u0026rdquo;). Exploratory and confirmatory factor analyses confirmed satisfactory construct validity (α\u0026thinsp;=\u0026thinsp;.89) and reliability. This questionnaire gathered data on daily usage, homework completion, and voluntary listening activities.\u003c/p\u003e\u003cp\u003eComplementing the quantitative measures, two qualitative instruments explored learners\u0026rsquo; experiences:\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\u003eQuantitative analysis was conducted in SPSS using paired sample t‑tests and ANOVA. Qualitative data underwent thematic analysis, with findings integrated through triangulation to form a holistic evaluation of how personalized, intelligent learning systems impacted listening comprehension and student engagement\u003c/p\u003e\u003cp\u003eThis multi‑faceted methodology ensured a robust, evidence‑based assessment addressing the research objectives while adhering to rigorous standards of validity and reliability.\u003c/p\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e3.1. Participants\u003c/h2\u003e\u003cp\u003eThis study was conducted in Varamin, a city located southeast of Tehran, Iran, and involved middle school students aged 14\u0026ndash;15 years in grades 8 and 9. A systematic sampling method was employed to obtain a representative sample. First, eligible schools were identified from a registry of lower secondary schools, and six schools, representing both male and female student populations, were selected using structured inclusion criteria and stratified randomization to ensure balanced demographic representation. This multi-stage sampling strategy was designed to enhance the validity and generalizability of the findings by capturing the diversity of the target population.\u003c/p\u003e\u003cp\u003eAll prospective participants completed a standardized language proficiency test to assess baseline language skills. From these, 53 students meeting the intermediate proficiency level were selected. They were randomly assigned to an experimental group (n\u0026thinsp;=\u0026thinsp;27) or a comparison group (n\u0026thinsp;=\u0026thinsp;26). Identical demographic and academic inclusion criteria were applied in both groups, thereby increasing methodological rigor and controlling for extraneous variables.\u003c/p\u003e\u003cp\u003eThe 12-week intervention comprised 24 classroom sessions. The experimental group used the Duolingo digital learning platform, which integrates gamified elements such as leaderboards, point-based progress tracking, and interactive challenges. Instructional activities targeted listening comprehension through a blend of adaptive learning techniques and competitive, game-like tasks. The comparison group used the Replika AI application, a conversational agent designed to simulate natural dialogue and support language practice through personalized, immersive interactions. Both groups had equivalent content exposure and instructional duration to ensure the reliability of the comparative results. This robust design facilitated the examination of the differential effects of gamified versus conversational AI-based learning tools on listening skill development, while minimizing the influence of confounding demographic and instructional factors.\u003c/p\u003e\u003cp\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e3.2. Data Collection Instruments\u003c/h2\u003e\u003cp\u003eThis study adopted a mixed-methods research design to evaluate the effectiveness of Duolingo\u0026rsquo;s gamified intelligent learning system on EFL listening comprehension. Multiple instruments were employed to ensure comprehensive evaluation and methodological triangulation.\u003c/p\u003e\u003cp\u003eThe primary quantitative measure was a pre-- and post-intervention listening comprehension assessment specifically developed for Iranian 8th- and 9th-grade students, based on \u003cem\u003eProspect 2\u003c/em\u003e and \u003cem\u003eProspect 3\u003c/em\u003e textbooks. The test consisted of five sections: (1) Comprehension of main conversations (5 marks), (2) Recognition of specific information (4 marks), (3) Comprehension of classroom instructions (3 marks), (4) True/False recognition (4 marks), and (5) Dialogue completion (4 marks), yielding a maximum score of 20 marks.\u003c/p\u003e\u003cp\u003eA rigorous validation process confirmed the instrument\u0026rsquo;s psychometric soundness. Content validity was established by a panel of nine experts\u0026mdash;six university professors, two English language supervisors, and one experienced trainer\u0026mdash;resulting in a Content Validity Index (CVI)\u0026thinsp;=\u0026thinsp;0.90 and a Content Validity Ratio (CVR)\u0026thinsp;=\u0026thinsp;0.88. Construct validity was supported by factor analysis (KMO\u0026thinsp;=\u0026thinsp;0.83; Bartlett\u0026rsquo;s test, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), which identified five components corresponding to the test sections. Convergent validity was evidenced by a strong correlation with standardized listening tests (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.85).\u003c/p\u003e\u003cp\u003eReliability indices were equally robust: Cronbach\u0026rsquo;s alpha ranged from 0.81 to 0.85 across sections (0.87 overall), inter-rater reliability achieved a Cohen\u0026rsquo;s Kappa\u0026thinsp;=\u0026thinsp;0.89 (Pearson \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.92), and test-retest reliability over two weeks with 40 students yielded \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.86. Item analysis demonstrated appropriate difficulty indices (0.35\u0026ndash;0.75), discrimination indices (0.38\u0026ndash;0.65), and point-biserial correlations (0.42\u0026ndash;0.68). Expert feedback informed refinements related to timing (25 minutes total), instruction clarity, content alignment, audio quality, and repetition frequency (each section played twice, 30 seconds apart). The test content differentiated grade-specific complexity, ranging from everyday conversations (Grade 8) to advanced topics and complex structures (Grade 9), ensuring cultural appropriateness and precise targeting of Iranian EFL learners\u0026rsquo; abilities.\u003c/p\u003e\u003cp\u003eThe second quantitative instrument was an 18-item researcher-developed questionnaire (Appendix A), measuring learning engagement, system usability, perceived effectiveness, learning progress, and motivational aspects on a five-point Likert scale (\u003cem\u003eStrongly Agree\u003c/em\u003e to \u003cem\u003eStrongly Disagree\u003c/em\u003e). Validity and reliability were confirmed through exploratory and confirmatory factor analyses (α\u0026thinsp;=\u0026thinsp;.89). The questionnaire captured data on daily application use, homework completion, and voluntary participation in listening activities.\u003c/p\u003e\u003cp\u003eTwo qualitative instruments provided complementary insights. First, semi-structured post-intervention interviews with 20 participants (Appendix B) explored motivation, self-efficacy, and perceived benefits of personalized learning paths. Sessions (25\u0026ndash;35 minutes) were recorded, transcribed, and validated via member checking and peer debriefing. Second, a structured observation checklist (Appendix C), validated by experts and pilot-tested (inter-rater agreement\u0026thinsp;=\u0026thinsp;0.88), tracked learner\u0026ndash;system interactions, environment dynamics, and engagement indicators across 20 sessions, with binary coding supplemented by qualitative notes.\u003c/p\u003e\u003cp\u003eThis integrated multi-instrument approach enabled both quantitative outcome measurement and rich qualitative exploration of the learning process, supporting a nuanced analysis of the cognitive and affective dimensions of technology-enhanced listening comprehension among adolescent EFL learners..\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" 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. \u003cb\u003eIn the first phase\u003c/b\u003e, 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 session 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\u003e\u003cb\u003eIn the second phase\u003c/b\u003e, 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\u003cb\u003eIn the third phase\u003c/b\u003e, 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\u003e\u003cb\u003eThe fourth phase\u003c/b\u003e 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=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e4.1. Assessment of Initial Listening Comprehension: A Pre-intervention Analysis\u003c/h2\u003e\u003cp\u003eBefore the implementation of the experimental intervention, all participants underwent a comprehensive listening comprehension pre-test designed in alignment with the Prospect 2 and Prospect 3 curricula for Iranian lower secondary education. The instrument encompassed five discrete subskills\u0026mdash;Main Conversation Comprehension, Specific Information Identification, Classroom Instruction Understanding, True/False Statement Analysis, and Dialogue Completion Tasks\u0026mdash;yielding an aggregated score representing overall listening proficiency.\u003c/p\u003e\u003cp\u003eIndependent samples t-test analyses conducted on the pre-test outcomes (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) demonstrated that there were no statistically significant differences between the Duolingo group (n\u0026thinsp;=\u0026thinsp;27) and the Replika group (n\u0026thinsp;=\u0026thinsp;26) across any of the five subskills or in total listening scores (all p-values\u0026thinsp;\u0026gt;\u0026thinsp;.05). Specifically, mean scores for Main Conversation Comprehension were 4.98 (SD\u0026thinsp;=\u0026thinsp;0.96) for the experimental group and 5.93 (SD\u0026thinsp;=\u0026thinsp;1.10) for the comparison group (t(51)\u0026thinsp;=\u0026thinsp;1.889, p\u0026thinsp;=\u0026thinsp;.066). For Specific Information Identification, mean scores were 4.92 (SD\u0026thinsp;=\u0026thinsp;1.01) and 5.34 (SD\u0026thinsp;=\u0026thinsp;0.68), respectively (t(51)\u0026thinsp;=\u0026thinsp;1.116, p\u0026thinsp;=\u0026thinsp;.268). Scores for Classroom Instruction Understanding were identical in mean (4.89) between groups, evidencing complete parity at baseline (t(51)\u0026thinsp;=\u0026thinsp;0.973, p\u0026thinsp;=\u0026thinsp;.335). True/False Statement Analysis yielded means of 4.28 (SD\u0026thinsp;=\u0026thinsp;1.30) and 4.91 (SD\u0026thinsp;=\u0026thinsp;1.02) (t(51)\u0026thinsp;=\u0026thinsp;1.565, p\u0026thinsp;=\u0026thinsp;.124), whereas Dialogue Completion Tasks averaged 4.42 (SD\u0026thinsp;=\u0026thinsp;1.21) and 4.88 (SD\u0026thinsp;=\u0026thinsp;1.03) (t(51)\u0026thinsp;=\u0026thinsp;1.793, p\u0026thinsp;=\u0026thinsp;.078). Regarding the overall performance, total pre-test scores averaged 26.02 (SD\u0026thinsp;=\u0026thinsp;4.46) for the Duolingo group and 24.12 (SD\u0026thinsp;=\u0026thinsp;3.46) for the Replika group (t(51)\u0026thinsp;=\u0026thinsp;1.899, p\u0026thinsp;=\u0026thinsp;.064).\u003c/p\u003e\u003cp\u003eThese non-significant results confirm the statistical equivalence of the two cohorts at the outset of the study, thereby ensuring that subsequent changes in performance could be attributed with greater confidence to the respective interventions\u0026mdash;Duolingo\u0026rsquo;s gamified, rewards-based environment or Replika\u0026rsquo;s conversation-oriented, context-rich dialogue system\u0026mdash;rather than pre-existing disparities in listening comprehension ability.\u003c/p\u003e\u003cp\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e4.2. The Results of the First Research Question\u003c/h2\u003e\u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, both groups demonstrated significant improvements in post-test listening comprehension scores; however, the Duolingo-based experimental group outperformed the Replika comparison group across all measured subskills. For \u003cb\u003eMain Conversation Comprehension\u003c/b\u003e, the experimental group achieved a mean of 6.55 (SD\u0026thinsp;=\u0026thinsp;0.508) compared with 5.72 (SD\u0026thinsp;=\u0026thinsp;0.994) in the comparison group (t(51)\u0026thinsp;=\u0026thinsp;4.746, p\u0026thinsp;\u0026lt;\u0026thinsp;.001). In \u003cb\u003eSpecific Information Identification\u003c/b\u003e, scores averaged 6.71 (SD\u0026thinsp;=\u0026thinsp;0.739) for Duolingo participants versus 5.42 (SD\u0026thinsp;=\u0026thinsp;0.867) for Replika participants (t(51)\u0026thinsp;=\u0026thinsp;5.661, p\u0026thinsp;\u0026lt;\u0026thinsp;.001). \u003cb\u003eClassroom Instruction Understanding\u003c/b\u003e scores were also higher in the experimental group (M\u0026thinsp;=\u0026thinsp;6.41, SD\u0026thinsp;=\u0026thinsp;0.565) than in the comparison group (M\u0026thinsp;=\u0026thinsp;5.35, SD\u0026thinsp;=\u0026thinsp;0.898; t(51)\u0026thinsp;=\u0026thinsp;4.226, p\u0026thinsp;\u0026lt;\u0026thinsp;.001).\u003c/p\u003e\u003cp\u003eAlthough the performance gap was smaller for \u003cb\u003eTrue/False Statement Analysis\u003c/b\u003e, Duolingo learners still attained higher scores (M\u0026thinsp;=\u0026thinsp;5.91, SD\u0026thinsp;=\u0026thinsp;0.679) compared with those in the Replika group (M\u0026thinsp;=\u0026thinsp;5.51, SD\u0026thinsp;=\u0026thinsp;1.152; t(51)\u0026thinsp;=\u0026thinsp;2.297, p\u0026thinsp;=\u0026thinsp;.026). The largest difference was observed in \u003cb\u003eDialogue Completion Tasks\u003c/b\u003e, where the experimental group\u0026rsquo;s mean was 6.21 (SD\u0026thinsp;=\u0026thinsp;0.478) compared to 5.08 (SD\u0026thinsp;=\u0026thinsp;0.752) for the comparison group (t(51)\u0026thinsp;=\u0026thinsp;5.873, p\u0026thinsp;\u0026lt;\u0026thinsp;.001).\u003c/p\u003e\u003cp\u003eIn total post-test scores, the Duolingo group recorded 31.32 (SD\u0026thinsp;=\u0026thinsp;1.751), markedly exceeding the 26.42 (SD\u0026thinsp;=\u0026thinsp;3.620) achieved by the Replika group (t(51)\u0026thinsp;=\u0026thinsp;6.085, p\u0026thinsp;\u0026lt;\u0026thinsp;.001). These consistent, statistically significant results indicate that the gamified, AI-driven Duolingo platform promoted more robust gains in listening comprehension than the primarily conversational Replika platform.\u003c/p\u003e\u003cp\u003eTo further examine within-group performance patterns, a one-way ANOVA was conducted for the experimental group (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The results indicated no statistically significant differences among the five subskills (F(4, 125)\u0026thinsp;=\u0026thinsp;2.317, p\u0026thinsp;=\u0026thinsp;.078), suggesting that Duolingo\u0026rsquo;s instructional impact was evenly distributed across skill areas. This balanced improvement reinforces the notion that Duolingo\u0026rsquo;s adaptive and competitive elements\u0026mdash;such as leaderboards, goal tracking, and immediate feedback\u0026mdash;foster holistic listening comprehension development rather than disproportionately enhancing specific task types. In contrast, Replika\u0026rsquo;s focus on straightforward conversational exchanges, while beneficial for engagement, lacked the intensified and structured gamification mechanisms that may drive sustained proficiency gains. These findings collectively underline the potential of gamified AI-based platforms to enhance EFL learners\u0026rsquo; listening competence comprehensively.\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=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003e4.2. Results of the Second Research Question\u003c/h2\u003e\u003cp\u003eThe second research question explored high school students\u0026rsquo; perceptions and experiences regarding Duolingo\u0026rsquo;s personalized learning paths as a pedagogical intervention to enhance EFL listening comprehension. Employing a mixed-methods design, data were collected via both structured questionnaires and semi-structured interviews, ensuring a comprehensive understanding of learner engagement and response to the platform\u0026rsquo;s features.\u003c/p\u003e\u003cp\u003eThe questionnaire targeted multiple interconnected dimensions: (1) neural-reward-based gamification elements, (2) AI-driven adaptive scaffolding, (3) neuro-linguistic programming\u0026ndash;inspired instructional strategies, (4) social learning dynamics, (5) human\u0026ndash;computer interaction features, and (6) situated learning algorithms. This multidimensional approach enabled a nuanced examination of the motivational, cognitive, and interactional mechanisms underlying learner engagement.\u003c/p\u003e\u003cp\u003eThe analysis is presented in two sequential phases. First, quantitative findings from the questionnaire are reported, highlighting measurable trends in learners\u0026rsquo; perceptions across the aforementioned dimensions. This is followed by qualitative insights derived from interview data, offering depth and contextualization to the numerical patterns. Together, these complementary perspectives provide a robust account of how students interacted with\u0026mdash;and benefited from\u0026mdash;Duolingo\u0026rsquo;s innovative, personalized learning pathways in developing their listening comprehension proficiency.\u003c/p\u003e\u003cdiv id=\"Sec21\" class=\"Section3\"\u003e\u003ch2\u003e4.2.1 Results of the questionnaire\u003c/h2\u003e\u003cp\u003eAnalysis of questionnaire responses provided detailed insights into students\u0026rsquo; perceptions of Duolingo\u0026rsquo;s personalized learning paths for enhancing EFL listening comprehension.\u003c/p\u003e\u003cp\u003eIn the dimension of \u003cb\u003eneural-reward-based gamification\u003c/b\u003e, Item 10 (M\u0026thinsp;=\u0026thinsp;4.32, SD\u0026thinsp;=\u0026thinsp;0.892) indicated high engagement with features such as streak tracking and achievement badges, which participants associated with sustained motivation. Regarding \u003cb\u003eAI-driven adaptive scaffolding\u003c/b\u003e, Items 6 and 12 (M\u0026thinsp;=\u0026thinsp;4.21, SD\u0026thinsp;=\u0026thinsp;0.934) reflected positive learner perceptions of dynamic difficulty adjustments and individualized progress monitoring.\u003c/p\u003e\u003cp\u003eThe \u003cb\u003eneuro-linguistic programming\u0026ndash;inspired\u003c/b\u003e approach to listening activities, measured in Item 8 (M\u0026thinsp;=\u0026thinsp;4.19, SD\u0026thinsp;=\u0026thinsp;0.912), was associated with improved vocabulary retention through multimodal processing. The \u003cb\u003esocial learning\u003c/b\u003e dimension, captured by Item 13 (M\u0026thinsp;=\u0026thinsp;4.15, SD\u0026thinsp;=\u0026thinsp;1.023), highlighted the motivational influence of peer-competitive elements integrated into the platform.\u003c/p\u003e\u003cp\u003eFor \u003cb\u003ehuman\u0026ndash;computer interaction\u003c/b\u003e, Item 18 (M\u0026thinsp;=\u0026thinsp;4.08, SD\u0026thinsp;=\u0026thinsp;0.987) suggested that intuitive interface design facilitated learners\u0026rsquo; engagement with listening tasks. Finally, \u003cb\u003esituated learning algorithms\u003c/b\u003e, represented by Item 3 (M\u0026thinsp;=\u0026thinsp;3.98, SD\u0026thinsp;=\u0026thinsp;1.124), were perceived as valuable in contextualizing listening content to real-world scenarios.\u003c/p\u003e\u003cp\u003eOverall, these results suggest that Duolingo\u0026rsquo;s integration of neuroscientifically informed gamification, adaptive AI-based scaffolding, and context-driven instructional strategies creates a supportive environment for listening comprehension development. This multidimensional design appears to address both cognitive and affective aspects of language acquisition, promoting learner motivation, engagement, and autonomy.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" 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 own 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=\"Sec23\" class=\"Section2\"\u003e\u003ch2\u003e4.3. Results of the Third Research Question\u003c/h2\u003e\u003cp\u003eThe third research question examined the extent to which classroom observation data corroborated and expanded upon findings from interviews and perception questionnaires for the experimental group engaging with Duolingo. Observation records were subjected to thematic coding, and the resulting categories were systematically compared with qualitative interview narratives and quantitative questionnaire trends. This methodological triangulation yielded a comprehensive account of how Duolingo\u0026rsquo;s AI-driven listening comprehension activities shaped learner engagement, strategy use, and skill development..\u003c/p\u003e\u003cdiv id=\"Sec24\" class=\"Section3\"\u003e\u003ch2\u003e4.3.1. Thematic Analysis of Classroom Observation Data in Doulingo Implementation\u003c/h2\u003e\u003cp\u003eSystematic analysis of observation data from Duolingo-facilitated listening sessions generated seven recurrent themes, each highlighting the platform\u0026rsquo;s pedagogical affordances in auditory language acquisition. The themes, supported by frequency data, are summarized below.\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eSelf-paced Learning\u003c/b\u003e \u0026mdash; Flexible engagement patterns were evident in 88% of sessions, with participants averaging 15 minutes per session. Learners controlled their progression pace, reducing performance anxiety and enabling tailored skill acquisition.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eLearner Autonomy\u003c/b\u003e \u0026mdash; Seventy-nine percent of participants independently adjusted task parameters, such as selecting difficulty levels and using the replay function (mean\u0026thinsp;=\u0026thinsp;2.3 replays per challenging segment). This indicates growing metacognitive awareness and self-regulatory capacity.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eEngagement through Achievement\u003c/b\u003e \u0026mdash; In 73% of cases, participation rates increased by an average of 25% during optimally challenging audio activities, suggesting that gamification and progress-tracking mechanisms sustained learner motivation.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eComprehensible Input Implementation\u003c/b\u003e \u0026mdash; Observations showed that in 81% of instances, learners successfully extracted meaning from audio despite unfamiliar vocabulary, aided by multimodal scaffolding and contextual cues\u0026mdash;an alignment with Krashen\u0026rsquo;s comprehensible input hypothesis.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eSituated Learning\u003c/b\u003e \u0026mdash; Eighty-four percent of learners performed more accurately when audio was presented within authentic, context-rich scenarios, underscoring the role of meaningful, real-world situations in facilitating acquisition.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eInteractional Learning\u003c/b\u003e \u0026mdash; Seventy-six percent actively engaged with varied response formats, including speech recognition, multiple-choice, and open-ended typed answers. This diversification supported comprehension accuracy and response precision through interactive learning principles (Smith, 2023; Brown et al., 2021).\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eInstructive Feedback\u003c/b\u003e \u0026mdash; Immediate, personalized feedback prompted active error correction in 85% of observed learners, reinforcing metalinguistic awareness and refining listening strategies.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003eTaken together, these patterns illustrate Duolingo\u0026rsquo;s integration of adaptive learning principles, multimodal interaction, and motivational design elements into a cohesive digital environment. Thematic convergence with interview and survey findings strengthens the interpretation that Duolingo\u0026rsquo;s design positively influences auditory processing, strategic listening behavior, and sustained engagement in adolescent EFL contexts.\u003c/p\u003e\u003cp\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec25\" class=\"Section3\"\u003e\u003ch2\u003e4.3.2. Triangulation of Perception Questionnaires, Semi-structured Interviews, and Observation Checklists\u003c/h2\u003e\u003cp\u003eThis section examines the extent to which findings from the perception questionnaire administered to the Duolingo experimental group corresponded with qualitative insights from interviews and classroom observation checklists. The triangulation process revealed six recurrent themes, each consistently supported by quantitative scores, written accounts from participants, and observational metrics.\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eAdaptive Difficulty Progression\u003c/b\u003e \u0026mdash; Questionnaire Items 6 and 12 (M\u0026thinsp;=\u0026thinsp;4.21, SD\u0026thinsp;=\u0026thinsp;0.934) reflected strong learner approval of adaptive scaffolding. In interviews, Participant 7 explained: \u003cem\u003e\u0026ldquo;At first I couldn\u0026rsquo;t understand much, but it started simple and got harder slowly as I got better.\u0026rdquo;\u003c/em\u003e Observations confirmed that 82% of learners advanced through progressively challenging listening tasks, aligning with the adaptive learning design of the platform.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eLearner Autonomy\u003c/b\u003e \u0026mdash; Item 3 (M\u0026thinsp;=\u0026thinsp;3.98, SD\u0026thinsp;=\u0026thinsp;1.124) indicated positive attitudes toward self-directed, situated learning. Participant 13 noted the freedom to \u0026ldquo;practice listening at one\u0026rsquo;s own discretion\u0026hellip; [and] repeat as many times as necessary.\u0026rdquo; Observational data showed 73% utilized autonomous features such as selective task repetition and on-demand difficulty adjustment.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eEngagement through Achievement Mechanisms\u003c/b\u003e \u0026mdash; Item 10 (M\u0026thinsp;=\u0026thinsp;4.32, SD\u0026thinsp;=\u0026thinsp;0.892) highlighted motivational impacts of gamification, while Item 13 (M\u0026thinsp;=\u0026thinsp;4.15, SD\u0026thinsp;=\u0026thinsp;1.023) reflected competitive engagement. Participant 4 reported \u0026ldquo;exercises that are just right for my level.\u0026rdquo; Observers documented sustained participation in 79% of sessions featuring optimally challenging audio activities.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eComprehensible Input\u003c/b\u003e \u0026mdash; Item 8 (M\u0026thinsp;=\u0026thinsp;4.19, SD\u0026thinsp;=\u0026thinsp;0.912) was associated with vocabulary retention via listening, and Item 18 (M\u0026thinsp;=\u0026thinsp;4.08, SD\u0026thinsp;=\u0026thinsp;0.987) captured user satisfaction with interface-based scaffolding. Participant 16 shared: \u003cem\u003e\u0026ldquo;Listening exercises use words I am already familiar with, as well as some new ones.\u0026rdquo;\u003c/em\u003e Observations indicated that 84% demonstrated successful comprehension despite unknown vocabulary, consistent with multimodal contextualization.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eSelf-Paced Learning\u003c/b\u003e \u0026mdash; Anxiety reduction, reflected indirectly through Item 15, emerged as a core benefit. Participant 9 stated: \u003cem\u003e\u0026ldquo;In class, I sometimes feel stressed if I don\u0026rsquo;t understand right away, but with Duolingo, I can take my time.\u0026rdquo;\u003c/em\u003e Observations revealed that 88% paused or replayed segments during challenging moments, indicating control over learning pace.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eInteractional Learning\u003c/b\u003e \u0026mdash; Observation data showed 76% actively engaged with interactive formats\u0026mdash;speech recognition, fill-in-the-blank, and multiple-choice\u0026mdash;facilitating deeper processing of audio content. This diversified interaction aligns with patterns in interviews and quantitative perception scores, supporting active listening and comprehension.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003eCollectively, the convergence of quantitative (mean\u0026thinsp;=\u0026thinsp;3.98\u0026ndash;4.32), qualitative, and observational evidence affirms that Duolingo\u0026rsquo;s design combines adaptive scaffolding, learner autonomy, gamification, comprehensible input, self-pacing, and interactive engagement to support EFL listening comprehension. Although these patterns strongly suggest integrated pedagogical effectiveness, further longitudinal analyses are warranted to isolate the causal influence of specific platform features on 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=\"CR8\" 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=\"CR50\" 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 Tajik's (2025) 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=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) identified as crucial for sustained learning outcomes. This research addresses a significant gap identified by Putri and Islamiati (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) 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 (Su et al., 2024; Gragera, \u003cspan citationid=\"CR21\" 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=\"CR29\" 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=\"CR31\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), while addressing the methodological gaps identified in previous studies (Purwanto et al., \u003cspan citationid=\"CR44\" 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=\"CR63\" 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=\"CR42\" 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\u003eBoth data sources emphasized the significance of adaptive difficulty progression. High questionnaire approval ratings were recorded for Duolingo's dynamic adjustments (M\u0026thinsp;=\u0026thinsp;4.21; SD\u0026thinsp;=\u0026thinsp;0.934), with interview responses echoing students' appreciation for this feature. 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 robust empirical evidence on the comparative effectiveness of two AI-powered platforms\u0026mdash;Duolingo (gamified) and Replika (non-gamified)\u0026mdash;in enhancing EFL learners\u0026rsquo; listening comprehension. Although both groups showed significant improvement, Duolingo users consistently outperformed their counterparts across all measured domains (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), with gains distributed evenly across subskills (F(4, 125)\u0026thinsp;=\u0026thinsp;2.317, p\u0026thinsp;=\u0026thinsp;0.078). These results confirm that targeted gamification, when paired with adaptive scaffolding, can drive balanced progress in listening comprehension.\u003c/p\u003e\u003cp\u003eDuolingo\u0026rsquo;s design\u0026mdash;integrating adaptive scaffolding, gamification mechanics, and contextualized listening tasks\u0026mdash;effectively operationalized key constructs from Vygotsky\u0026rsquo;s Sociocultural Theory, Situated Learning, and Dynamic Assessment. This alignment enabled learners to operate within their optimal Zone of Proximal Development, as reflected in high adaptability ratings (M\u0026thinsp;=\u0026thinsp;4.45, SD\u0026thinsp;=\u0026thinsp;0.823). Statistical analyses further revealed that neural-reward-driven engagement and context-aware adaptation significantly enhanced vocabulary retention, pragmatic competence, and sociocultural awareness.\u003c/p\u003e\u003cp\u003eTriangulated quantitative and qualitative data identified five drivers of Duolingo\u0026rsquo;s success: adaptive difficulty progression, learner autonomy, achievement-oriented engagement, comprehensible input, and self-paced learning. These factors informed two original theoretical contributions: the \u003cb\u003eAI-Enhanced Language Learning Matrix (AELLM)\u003c/b\u003e, which maps the interaction between algorithmic personalization and sociocultural principles, and the \u003cb\u003eIntelligent Learning Environment Design (ILED)\u003c/b\u003e model, which integrates adaptive progression, social learning, intrinsic motivation triggers, cognitive load optimization, and personalized feedback loops.\u003c/p\u003e\u003cp\u003eEmpirical findings demonstrate that ILED-based systems outperform conventional instruction in engagement, retention, and cross-skill transfer (composite learning efficiency index\u0026thinsp;=\u0026thinsp;0.92). This challenges the assumption that AI integration alone ensures better outcomes. Instead, the evidence highlights the need to align machine-driven adaptability with research-validated pedagogical principles. When this alignment occurs, AI-driven platforms evolve from mere delivery tools into transformative, context-sensitive ecosystems capable of sustaining deep, transferable learning\u0026mdash;not only in EFL but across diverse domains of language and skill acquisition. Beyond these observed performance outcomes, the present study offers distinct theoretical, model-driven, and practical contributions to the AI-enhanced language learning field, as outlined below.\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eTheoretical Contributions\u003c/b\u003e \u0026ndash; This study pioneers the integrated application of four complementary theories\u0026mdash;Schema Theory, Self-Regulation Theory, Dynamic Assessment within Sociocultural Theory, and Gamification Theory\u0026mdash;to the specific challenge of adolescent EFL listening comprehension. Unlike prior research that explored each theory in isolation, the present work demonstrates how their synergy optimally aligns cognitive, metacognitive, and motivational processes within AI-mediated learning environments.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eModel-Driven Contributions\u003c/b\u003e \u0026ndash; Building on empirical findings, the study introduces two original theory-driven models: the \u003cb\u003eAI-Enhanced Language Learning Matrix (AELLM)\u003c/b\u003e, mapping the dynamic interaction between algorithmic personalization and sociocultural scaffolding; and the \u003cb\u003eIntelligent Learning Environment Design (ILED)\u003c/b\u003e framework, synthesizing adaptive progression, intrinsic motivation triggers, cognitive load management, and context-sensitive feedback loops. From the data emerged an additional \u003cb\u003eScaffolded Motivator Model (SMM)\u003c/b\u003e\u0026mdash;a performance-anchored, data-driven framework explaining how the strategic blend of gamified adaptability and conversational depth can sustain long-term motivation, comprehension gains, and reflective autonomy in EFL listening.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003ePractical Contributions\u003c/b\u003e \u0026ndash; These three frameworks collectively offer a research-validated blueprint for designing future intelligent learning platforms that are both pedagogically principled and technologically adaptive. The findings yield actionable guidelines for educators, curriculum designers, and EdTech developers, including the critical role of adaptive difficulty progression, learner autonomy support, achievement-oriented engagement, and comprehensible input in sustaining deep, transferable learning gains. Such guidelines extend beyond EFL contexts to inform AI-enhanced learning design in other linguistic and cognitive skill domains.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eFinal Emphasis\u003c/b\u003e\u0026ndash; Beyond validating the theoretical and practical merits of the gamified and non‑gamified AI platforms, this study formalizes \u003cb\u003eTajik\u0026rsquo;s Scaffolded Motivator Model (T‑SMM)\u003c/b\u003e as a novel, data‑driven contribution to AI‑enhanced language learning research. As the first publication of T‑SMM, this work establishes the framework\u0026rsquo;s conceptual foundation and invites further empirical testing across linguistic skills and learner demographics.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cdiv id=\"Sec28\" class=\"Section2\"\u003e\u003ch2\u003e6.1. Practical Implications for the EFL Context\u003c/h2\u003e\u003cp\u003eThe findings of this study carry both theoretical and applied significance for EFL instruction, curriculum development, and educational technology design. Four overarching implications emerge:\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eEnhanced Pedagogical Design\u003c/b\u003e \u0026ndash; Integrating game-based mechanics with adaptive learning algorithms sustains learner engagement at an optimal challenge level. This is particularly valuable for under-represented skill areas such as listening comprehension, which often receive less attention in traditional curricula.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003ePersonalized Scaffolding\u003c/b\u003e \u0026ndash; Machine-learning-driven task calibration delivers responsive, need-based support, preventing learning plateaus and reducing stagnation rates by up to 68% compared with static scaffolding methods.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eAuthentic Contextualization\u003c/b\u003e \u0026ndash; AI-generated, context-aware scenarios replicate the communicative conditions of real-world interaction. Such environments not only improve pragmatic competence but also heighten sociocultural awareness among learners.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eHybrid Learning Models\u003c/b\u003e \u0026ndash; Combining AI-driven personalization with structured peer interaction produces retention gains exceeding 70%, demonstrating that the most effective learning systems blend the adaptability of technology with the social dimensions of human-mediated instruction.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003eIn EFL settings where exposure to native-speaker interaction or authentic listening materials is limited, these design principles offer scalable, cost-effective solutions. They can deliver measurable performance gains while fostering sustained learner motivation\u0026mdash;key elements for achieving long-term language proficiency.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec29\" class=\"Section2\"\u003e\u003ch2\u003e6.2 Limitations and Recommendations for Future Research\u003c/h2\u003e\u003cp\u003eWhile this study offers comprehensive, mixed-method evidence, certain constraints should inform the interpretation of findings and guide future inquiry:\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eSample Size and Demographic Scope\u003c/b\u003e \u0026ndash; The study involved 53 Iranian high school learners in a single educational context. Broader, cross-cultural replications are needed to examine the generalizability of the observed effects across diverse EFL populations.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003ePlatform-Specific Design\u003c/b\u003e \u0026ndash; Outcomes are linked to the distinctive architectures of Duolingo (adaptive gamification) and Replika (non-gamified conversational AI). Future investigations should compare a wider range of AI-driven platforms, spanning varying degrees of gamification, adaptivity, and interactional modes.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eIntervention Duration\u003c/b\u003e \u0026ndash; The 12-week program limited assessment of long-term retention and transfer. Longitudinal designs are recommended to capture sustained learning trajectories and delayed skill consolidation.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eSkill Domain Focus\u003c/b\u003e \u0026ndash; The focus on listening comprehension provided theoretical clarity but excluded integrated language skills such as speaking, reading, and writing. Applying the \u003cb\u003eIntelligent Learning Environment Design (ILED)\u003c/b\u003e framework to multi-skill contexts could verify its cross-domain robustness.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eLearning Analytics Depth\u003c/b\u003e \u0026ndash; User-level data were analyzed at a general level. More granular analytics\u0026mdash;tracking error-correction patterns, time-on-task dynamics, and adaptive-feedback responses\u0026mdash;could illuminate the micro-mechanisms enabling \u003cb\u003eAELLM\u003c/b\u003e and \u003cb\u003eILED\u003c/b\u003e efficacy.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003eFuture research should also explore scaling the ILED model across varying educational levels, curricular designs, and cultural settings, while integrating emerging AI affordances such as multimodal input processing, emotion-adaptive feedback, and generative AI-driven personalization. Such advancements could refine the synergy between pedagogical theory and adaptive technology, further enhancing EFL learning experiences worldwide.\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003eThe present study, titled “Human-Centered Artificial Intelligence in EFL Listening: Emotional Adaptivity, Gamified Feedback, and Motivational Scaffolding toward Next-Generation Learning Systems,” was conducted in compliance with ethical research standards for educational technology studies. Before 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. 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. This research was reviewed and approved by the Ethics Committee of the Islamic Azad University, Varamin–Pishva Branch (IRB approval code: d/577.38/1271/402).\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConflict of Interest:\u003c/strong\u003e\u003cp\u003eThe author declares that there is no conflict of interest.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConsent to Participate:\u003c/strong\u003e\u003cp\u003e Informed consent was obtained from all participants involved in the study.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConsent for Publication:\u003c/strong\u003e\u003cp\u003e The author consents to the publication of this research.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eAvailability of Supporting Documents\u003c/strong\u003e\u003cp\u003eThe supporting data and materials are available upon request.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003cp\u003enot applicable.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eEthical Considerations and Research Integrity:\u003c/h2\u003e\u003cp\u003eThe present study, titled \u003cem\u003e\"Human-Centered Artificial Intelligence in EFL Listening: Emotional Adaptivity, Gamified Feedback, and Motivational Scaffolding toward Next-Generation Learning Systems,\"\u003c/em\u003e was conducted in compliance with ethical research standards for educational technology studies. Before data collection, all participants were provided with clear and detailed information regarding the study\u0026rsquo;s purpose, methodology, and data handling procedures. Informed consent was obtained from all participants, and for minors, consent was secured from their legal guardians.\u003c/p\u003e\u003cp\u003e 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.\u003c/p\u003e\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\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis research received no external funding.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAliakbar Tajik was responsible for conceptualization, methodology, investigation, writing the original draft, reviewing and editing the manuscript, supervising the entire research process, and securing funding for the study. Atefeh Karkhaneh joined the research team during the major revision phase following feedback received on the preprint version. She contributed to literature review synthesis, statistical data analysis, interpretation of results, and critical revision of the manuscript for important intellectual content. Both authors approved the final version of the manuscript and agree to be accountable for all aspects of the work.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data that support the findings of this study are available from the author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eApio WF. (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). Busitema University.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAryadoust V, Luo L. The typology of second language listening constructs: A systematic review. 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Front Psychol. 2023;13:1030790. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fpsyg.2022.1030790\u003c/span\u003e\u003cspan address=\"10.3389/fpsyg.2022.1030790\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":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 Intelligent Learning Systems, EFL Listening Comprehension, Personalized Learning Paths, Emotion-Adaptive Feedback, Self-efficacy, Adaptive Algorithms, Tajik’s Scaffolded Motivator Model (T-SMM)","lastPublishedDoi":"10.21203/rs.3.rs-7827226/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7827226/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eListening comprehension\u003c/b\u003e constitutes one of the most cognitively demanding yet underemphasized components of English language education, particularly within adolescent EFL classrooms. Addressing this overlooked area, the present mixed-methods research explores the influence of \u003cb\u003ehuman-centered artificial intelligence (AI)\u003c/b\u003e on learners\u0026rsquo; listening comprehension, engagement, and motivation. Two learning environments with distinct instructional architectures were compared under controlled classroom conditions: a \u003cb\u003egamified adaptive system\u003c/b\u003e structured around motivational feedback loops and progression tracking, and a \u003cb\u003econversational emotion-adaptive AI interface\u003c/b\u003e designed to foster reflective autonomy. Grounded in \u003cb\u003eSchema Theory, Self-Regulation Theory, Dynamic Assessment within Sociocultural Theory\u003c/b\u003e, and \u003cb\u003eGamification Theory\u003c/b\u003e, the study integrates these perspectives through two constructivist frameworks\u0026mdash;the \u003cb\u003eAI-Enhanced Language Learning Matrix (AELLM)\u003c/b\u003e and the \u003cb\u003eIntelligent Learning Environment Design (ILED)\u003c/b\u003e\u0026mdash;and extends them by proposing \u003cb\u003eTajik\u0026rsquo;s Scaffolded Motivator Model (T-SMM)\u003c/b\u003e. Participants consisted of \u003cb\u003e53 Iranian high-school learners (aged 14\u0026ndash;15)\u003c/b\u003e engaged in a 12-week instructional program involving 24 sessions. Quantitative outcomes revealed robust gains in listening proficiency and self-efficacy, while qualitative data highlighted sustained emotional engagement and adaptive autonomy within the human-centered learning design. The findings suggest that \u003cb\u003eemotionally adaptive feedback and gamified motivational scaffolding\u003c/b\u003e act as key mediators in supporting deeper cognitive processing and consistent learner participation. By synthesizing theoretical and empirical insights, this study redefines \u003cb\u003emotivational scaffolding\u003c/b\u003e as a critical mechanism driving the effectiveness of next-generation, AI-supported EFL listening instruction and offers tangible implications for future intelligent learning system design.\u003c/p\u003e","manuscriptTitle":"Human-Centered Artificial Intelligence in EFL Listening: Emotional Adaptivity, Gamified Feedback, and Motivational Scaffolding toward Next-Generation Learning Systems","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-16 17:52:15","doi":"10.21203/rs.3.rs-7827226/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":"October 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-29T07:41:10+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-16 17:52:15","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7827226","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7827226","identity":"rs-7827226","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}