Beyond Voice Recognition: Integrating Alexa’s Emotional Intelligence and ChatGPT’s Language Processing for EFL Learners’ Development and Anxiety Reduction - A Comparative Analysis

Beyond Voice Recognition: Integrating Alexa’s Emotional Intelligence and ChatGPT’s Language Processing for EFL Learners’ Development and Anxiety Reduction - A Comparative Analysis | 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 Beyond Voice Recognition: Integrating Alexa’s Emotional Intelligence and ChatGPT’s Language Processing for EFL Learners’ Development and Anxiety Reduction - A Comparative Analysis Dr Aliakbar Tajik This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5989702/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This groundbreaking study investigates the integration of Amazon Alexa, an emotionally intelligent AI platform, into English language teaching through an adaptive language learning system. Using a mixed-methods design, the study examined the impact of this innovative platform on the speaking skills of 40 high school students (aged 16–18) from Varamin County, Iran. The experimental group (n = 20) engaged with Amazon Alexa's emotionally intelligent platform, which provides AI-driven real-time feedback based on emotional intelligence (EI); in contrast, the control group (n = 20) received instruction using ChatGPT-3.5 over eight sessions following a pre-test to ensure group homogeneity. The study employed a concurrent mixed methods design, with quantitative data collected using the researcher-developed Speaking Assessment System and the researcher-developed Perception Questionnaire; qualitative data were derived from researcher-developed classroom observation checklists and researcher-developed semi-structured interviews (n = 15), focusing on emotional state monitoring and anxiety reduction patterns. Statistical analyses revealed a significant positive correlation between Alexa's EI-based instruction and speaking performance (p < 0.05, η2 = 0.42), with the experimental group showing significantly improved performance (F(1,38) = 24.63, p < 0.05). Amazon Alexa's emotional state detection capabilities demonstrated 94% accuracy in identifying and responding to learners' emotional states. This study represents a paradigm shift in language learning technology, leveraging Amazon Alexa's emotionally intelligent platform to address cognitive and emotional aspects of language acquisition simultaneously. The findings have significant implications for the global language learning market, particularly in addressing speaking anxiety and emotional barriers to language learning. The platform's scalability and cross-cultural applicability make it a potentially transformative solution for language learning worldwide, opening up new avenues for the development of emotionally intelligent educational technology. Artificial Intelligence and Machine Learning Special Education Agricultural Engineering Biochemical Research Methods Artificial Intelligence Emotional Intelligence Speaking Proficiency Mixed-method Research Educational Innovation Real-time Emotional Feedback Language Learning Technology Anxiety Management 1. Introduction The integration of artificial intelligence (AI) into language learning platforms has grown rapidly, attracting considerable attention from researchers and educators alike (Feng, 2025; Tajik, 2025; Imran et al., 2024; Araujo & Caldeira, 2024; Zhou & Gao, 2023). This surge in interest stems from AI's potential to revolutionize the educational experience through personalized learning and adaptive feedback systems. While promising, the implementation of AI-driven emotional intelligence (EI) has been conceptualized differently across studies, ranging from system-based emotional recognition (Wang, 2022) to adaptive feedback mechanisms (Fu et al., 2023) and intelligent tutoring systems (Kelkar, 2022). These different interpretations highlight the need for a unified understanding of the role of AI-powered EI in language learning environments. Recent advances in AI-driven platforms, particularly Amazon Alexa, have demonstrated remarkable capabilities in emotional intelligence and adaptive responses. Research has shown that Alexa's natural language processing capabilities, combined with its emotion recognition algorithms, can detect and respond to users' emotional states with up to 85% accuracy (Chen et al., 2023). Through voice pattern analysis, sentiment detection, and contextual understanding, Alexa can identify various emotional states, including anxiety, confidence, and engagement, and adapt its responses accordingly (Luria, 2024). This emotional awareness is particularly notable in educational contexts, where Alexa's ability to provide emotionally intelligent feedback has shown promising results in reducing learning anxiety and increasing user engagement (Tai & Chen, 2024). The effectiveness of language learning platforms is influenced by several components, including AI capabilities, emotional intelligence, adaptive feedback, and real-time processing (LE, 2024; Vistorte et al., 2024; Shi, 2024). The integration of AI-driven emotional intelligence has emerged as a critical factor in improving learner engagement and motivation. In this context, AI-driven EI refers to the ability of systems to detect, process, and respond to learners' emotional states while providing real-time adaptive feedback (Guo & Wang, 2024). Studies have shown that such integration has a significant impact on various aspects of language learning, from speaking confidence to overall proficiency (Du & Daniel, 2024; Rusmiyanto et al., 2023). Emotional intelligence has been consistently recognized as a cornerstone of effective speaking performance in language learning. Defined as the ability to recognize, understand, and manage emotions while navigating emotional interactions (Makhachashvili & Semenist, 2024), EI plays a fundamental role in language acquisition. In speaking activities, emotional management is strongly correlated with improved fluency, pronunciation accuracy, and communication effectiveness (Shi, 2024). Despite the growing recognition of the importance of EI in language teaching, research on the integration of AI-driven EI into language enhancement platforms remains limited. While studies have highlighted the potential of AI-EI in second language acquisition (Roberts et al., 2024; Li et al., 2024; De la et al., 2023), there are significant gaps in understanding its practical application for improving real-time speaking performance. The integration of AI with EI-driven features has shown promise in reading and listening comprehension (Gligorea et al., 2023), but research specifically addressing speaking skills remains scarce. Roberts et al. (2024) highlight that AI combined with emotional intelligence can provide personalized, context-sensitive interventions that respond to learners' emotional states. The challenges of implementing AI-EI in real-time language platforms are considerable. While AI can process large amounts of data and provide tailored feedback, replicating the nuanced emotional feedback of human instructors remains complex. Li et al. (2024) highlight that emotional expression in spontaneous speech requires a sophisticated understanding of both linguistic and contextual elements. Despite these challenges, incorporating AI-EI into speaking skill development platforms represents a potentially transformative approach to second language acquisition, particularly in addressing emotional and psychological barriers to learning performance. This research gap provides valuable opportunities to explore the convergence of AI technologies and emotional intelligence in language learning environments. 1.1. Emotional Intelligence and Speaking Skills A large body of research has found a strong correlation between emotional intelligence (EI) and speaking ability (Ebrahimi et al., 2018; Chen et al., 2024; Kumar & Tankha, 2023). This relationship is particularly evident in language learners, where higher levels of EI are associated with lower levels of speaking anxiety, improved oral communication skills and better overall speaking performance (Afifah et al., 2024; Dhawan & Kour, 2024; Williams, 2024).In essence, emotional intelligence enables learners to cope with the emotional challenges associated with language learning, particularly in the often daunting task of speaking. Two key components of EI, intrapersonal and interpersonal awareness, are particularly important for optimal speaking performance. Intrapersonal awareness enables speakers to understand and regulate their emotional states during communication, while interpersonal awareness facilitates the recognition, empathy and appropriate response to others' emotional cues during conversation (Wang & Wang, 2024). According to Ondé (2023), EI can be defined as the ability to recognize, process, and regulate emotions during conversational interactions. This includes not only the recognition of emotions but also the effective expression of these feelings through both verbal and non-verbal means. This skill is crucial for managing emotions in real-time conversations (Fathi et al., 2024; Zhou & Hou, 2024; Swathy & Kannammal, 2024) and is strongly linked to the development of speaking skills and emotional regulation. Lee et al. (2023) further suggest that EI is closely related to speaking confidence, fluency , and communicative competence. Together, these factors govern an individual's ability to articulate thoughts clearly, understand others' messages, and manage speaking anxiety in a variety of contexts. The convergence of emotional intelligence (EI) and artificial intelligence (AI) in language education has emerged as a transformative force in improving learners' speaking performance. Contemporary research shows that emotional intelligence, which encompasses both intrapersonal and interpersonal awareness, significantly influences speaking performance and anxiety management in language learning contexts (Ebrahimi et al., 2018; Kumar & Tankha, 2023). This relationship becomes particularly salient when examined through the lens of AI-enhanced learning environments, where emotional dynamics intersect with technological innovation. Qiao and Zhao (2023) shed light on how AI-based instructional methods promote improvements in speaking skills and self-regulation among English as a foreign language (EFL) learners, highlighting the potential of AI to facilitate personalized learning experiences that address both the linguistic and emotional dimensions of language acquisition. The integration of AI technologies with EI-aware pedagogical approaches has proven particularly effective in reducing speaking anxiety while maintaining high levels of engagement (Xin & Derakhshan, 2024). This synergy is manifested through adaptive emotional scaffolding and multimodal feedback systems that take into account both verbal and nonverbal aspects of communication, creating a more comprehensive and supportive learning environment (Zhou & Gao, 2023). Moreover, recent studies suggest that the effectiveness of AI-based speaking instruction is significantly enhanced when combined with emotional intelligence principles, leading to improved self-regulation, greater self-confidence, and more sophisticated communication skills (Zhang, 2023). This emerging paradigm suggests that the future of speaking instruction lies in the sophisticated integration of emotional intelligence principles with AI-based learning environments that address both the cognitive and affective dimensions of language acquisition. 1.2. Amazon Echo Show in Language Learning The emergence of intelligent personal assistants (IPAs), such as the Amazon Echo Show, represents a significant advance in second language (L2) learning technology, particularly in addressing the complex challenges of developing listening and speaking skills. Recent empirical studies have demonstrated the significant impact of these AI-powered devices on language acquisition outcomes. In particular, Hsu et al. (2023) conducted a comprehensive study showing significant improvements in speaking skills and a significant reduction in speaking anxiety among L2 learners using the Echo Show. Their results indicated a 28% increase in speaking performance scores (p < 0.001) and a 35% decrease in speaking-related anxiety, highlighting the effectiveness of the device in creating a supportive learning environment. The psychological dimensions of language learning through IPA technology have emerged as an important area of research. Xu, Qiu, et al. (2022) developed specific scales to measure psychological needs related to L2 speaking and listening and identified significant relationships between autonomy, competence, and relatedness. Their research highlights the importance of integrating these psychological factors into language learning strategies, especially when implementing technology-enhanced learning solutions. The Echo Show's non-judgmental interface and immediate feedback mechanisms appear to effectively address these psychological needs, creating an environment conducive to confident language production and experimentation (Hsu et al., 2023). Contemporary research has increasingly emphasized the importance of multimodal approaches to language learning. Bräuer and Mazarakis (2024) highlighted that although multimodal teaching methods are essential for effective language acquisition, many educators do not fully utilize these approaches. This finding is in line with Sejdiu's (2017) research, which highlights the often underestimated role of listening skills in language development and advocates for the integration of multimedia and computer-assisted language learning programmers. The multimedia capabilities of the Amazon Echo Show, combined with its IPA features, provide a comprehensive platform for the effective implementation of these multimodal approaches. Recent developments in the field have also revealed strong correlations between language learning strategies, self-efficacy, and overall language proficiency. Gao et al. (2022) demonstrated that improving metacognitive strategies and self-efficacy significantly benefits learners' language acquisition processes. The Echo Show's ability to provide consistent, personalized feedback and facilitate self-paced learning directly supports these findings by promoting learner autonomy and building confidence in language production. This technological integration represents a significant advancement in educational technology and offers a promising tool for enhancing both the cognitive and affective aspects of language learning while addressing the complex interplay between speaking skills, listening comprehension, and psychological factors in L2 acquisition(Gao et al., 2022) . 1.3. AI-Driven Emotional Intelligence Feedback and Affective User Modeling in Language Learning The evolution of emotionally intelligent feedback systems, particularly through voice-activated AI assistants such as Amazon Alexa, signifies a substantial advancement in the realm of educational technology for English language learning. Pekrun and Linnenbrink-Garcia (2021) established a fundamental framework that can be applied to Alexa's feedback system, categorizing it into three distinct types: empathetic feedback, where Alexa responds to learners' emotional states through voice tone analysis; motivational feedback, where Alexa provides encouraging responses to enhance engagement and speaking confidence; and regulatory feedback, where Alexa helps learners manage their emotional responses during speaking practice sessions. Building on this foundation, D'Mello and Graesser (2015) demonstrated through empirical research that emotional awareness in voice-based learning technologies significantly impacts student engagement and speaking outcomes. The relevance of their work to Alexa's capabilities in real-time emotion detection through voice analysis and appropriate response generation is particularly salient, as it creates effective speaking practice environments. The integration of emotional awareness into the Alexa platform has demonstrated particular promise in addressing common challenges in English-speaking practice, such as reducing speaking anxiety and sustaining motivation for continuous practice. Affective user modeling has emerged as a crucial component in personalizing speaking practice experiences through Alexa. Azevedo et al. (2018) provided a comprehensive framework that can be applied to Alexa’s adaptive system, defining it as “the systematic representation of learner emotional states, preferences, and responses to create adaptive learning environments.” This framework demonstrates how Alexa’s systems incorporating affective modeling can significantly enhance speaking outcomes through real-time adaptation to learners’ emotional states, particularly during speaking practice sessions. This approach has proven particularly effective in English-speaking contexts, where emotional factors such as confidence and anxiety significantly influence oral production and fluency development. Recent empirical evidence supports the effectiveness of voice-based AI platforms in language learning. Lajoie et al. (2020) conducted extensive research on emotion measurement in technology-rich learning environments, revealing significant improvements in learner engagement and speaking performance when affective modeling was incorporated into voice-based educational systems. Their findings can be directly applied to Alexa’s capability to recognize and respond to learners’ emotional states through voice analysis, achieving markedly better results in both speaking outcomes and user satisfaction. In addition to these findings, Loderer et al. (2020) conducted a comprehensive meta-analysis that quantified the impact of emotion-aware learning technologies, which can be extended to voice-activated AI platforms such as Alexa. Their research revealed substantial improvements in learner engagement and significant reductions in speaking anxiety when affective modeling was implemented. The meta-analysis provides robust evidence for the effectiveness of integrating emotional intelligence into Alexa's educational technology platform, particularly for English-speaking practice. This extensive body of research collectively demonstrates the transformative potential of integrating Alexa's AI-driven emotional intelligence feedback with affective user modeling in English language learning environments. The integration of these approaches within Alexa's platform engenders more responsive, adaptive, and effective speaking practice systems, which can address both the cognitive and emotional needs of English language learners. However, future research should focus on refining Alexa's emotion recognition algorithms for diverse accents and speaking patterns, developing more sophisticated response generation mechanisms for various speaking contexts, and investigating the long-term impacts of voice-based AI systems on speaking skill development. 1.4. The Current Study: Gap and Significance In recent years, there has been an increasing focus among researchers on the potential of AI-driven emotional intelligence (EI) to enhance language learning outcomes (Hastungkara & Triastuti, 2023; Gao et al., 2023; Cai & Liu, 2023). However, a substantial research gap remains, as many studies have neglected to address the pivotal role of AI-driven EI in providing real-time adaptive feedback, particularly for speaking performance, within digital language learning environments (Bin-Hady et al., 2024; Xiao et al., 2024; Roberts et al., 2024). This oversight is a critical concern in contemporary language learning platforms (Davis et al., 2024; Harris & Kim, 2024). Language learners frequently encounter challenges such as speaking anxiety and the lack of real-time emotional support during speaking practice, which can impede progress and engagement (Afifah et al., 2024; Dhawan & Kour, 2024; Williams, 2024). The integration of artificial intelligence (AI)-driven emotional intelligence (EI) into language learning platforms has been identified as a potential solution to these existing educational gaps, particularly in the domain of speaking skill development (Sergeeva, 2023). As asserted by Sergeeva (2023), the effective cultivation of speaking skills necessitates both technological integration and consistent emotional support, which can be realised through AI-driven EI systems capable of offering real-time, personalized, and adaptive feedback. While traditional pedagogical approaches have demonstrated varying degrees of success (Sintya & Handayani, 2023), AI-enhanced systems offer unique capabilities to create personalized, emotionally intelligent learning environments wherein learners can effectively articulate their thoughts while simultaneously regulating their emotional states (Surahman & Sofyan, 2023). This capability assumes particular significance in light of the limitations of traditional methods in providing continuous, individualized support, as highlighted by Zou (2020) and Zhang (2023). A substantial body of research has repeatedly indicated a notable lacuna in the effective implementation of artificial intelligence-driven emotional intelligence (EI) within language learning platforms. Some studies have drawn attention to this deficit, emphasizing the absence of systematic investigation into the advantages of AI-EI, particularly in the domain of spoken proficiency enhancement (Zainuddin, 2023; Xin & Derakhshan, 2024; Topal, 2024). Recent investigations by Santoso, Affandi et al. (2024) reveal critical differences in how emotional intelligence influences learners' speaking performance, particularly in managing speaking anxiety in English as a Foreign Language (EFL) contexts. Conventional pedagogical approaches frequently encounter difficulties in providing continuous, customized emotional assistance. In contrast, AI systems have the potential to deliver such support in a consistent and scalable manner, a capacity that may prove challenging for human instructors to maintain over time (Zou, 2020; Zhang, 2023). To address these challenges, AI-driven systems offer a viable solution through the provision of personalized feedback mechanisms that respond dynamically to learners' emotional states in real-time, while also tailoring learning pathways to align with individual emotional intelligence profiles. The integration of these adaptive systems within learning environments has been shown to contribute to the creation of a more supportive and conducive learning atmosphere, thereby addressing the frequently observed anxiety issues within traditional classroom settings (Santoso et al., 2024). Consequently, this fosters more sophisticated and individualized learning approaches. Despite the mounting recognition of the pivotal role of Emotional Intelligence (EI) in language education, a substantial gap persists in studies that specifically integrate AI-driven Emotional Intelligence (AI-EI) into platforms designed to enhance speaking performance. Recent research has underscored the potential of AI-EI in the context of second language acquisition (Roberts et al., 2024; Li et al., 2024; De la et al., 2023). However, there is a discernible absence of research addressing the effective utilization of such technologies to enhance real-time speaking performance. While the integration of AI with EI-driven features, such as real-time adaptive feedback mechanisms, has yielded promising results in other areas of language learning, notably in reading and listening comprehension (Gligorea et al., 2023), the application of AI-EI to the refinement of speaking skills remains significantly under-explored. This paucity of exploration is particularly salient in light of the centrality of emotional regulation, self-confidence, and spontaneous communication to oral proficiency, as these aspects are often directly influenced by emotional responses. Furthermore, Roberts et al. (2024) underscore the potential of a synergistic integration of AI and emotional intelligence to provide customized, context-sensitive interventions that are attuned to the learner's emotional state, thereby fostering a more dynamic, supportive, and personalized learning environment. A review of the extant literature reveals a substantial research gap regarding the specific integration of AI-driven emotional intelligence to develop speaking skills. While numerous studies have explored various facets of AI in language learning (Du & Daniel, 2024), the precise application of AI-EI for improving speaking skills has not received adequate scholarly attention. This research gap provides a compelling rationale for further investigation into the relationship between AI-based emotional intelligence and improving speaking performance, particularly in the context of real-time adaptive feedback systems. A focus on real-time, emotionally attuned feedback is essential for paving the way for more effective, personalized, and psychologically robust language learning experiences. Such experiences must consider linguistic proficiency and the learner's emotional and psychological engagement within the speaking process. 2. Literature Review 2.1. Emotional Intelligence in Educational Technology: From CALL to AI-Driven Language Learning The nexus of emotional intelligence and educational technology can be traced back to the seminal contributions of Salovey and Mayer (1990), who initially theorized emotional intelligence as the capacity to discern one's own and others' emotions, differentiate between them, and employ this discernment to inform cognitive and behavioral processes. This seminal framework established the foundations for comprehending the influence of emotions on learning processes, particularly within the context of language acquisition. Building on this, Goleman (1995) expanded the concept to include self-awareness, self-regulation, and social skills, elements that are now central to AI-driven language learning platforms. The evolution of Computer-Assisted Language Learning (CALL) has tracked these developments in emotional intelligence research. Warschauer (1996) was a pioneering figure in the integration of technology in language learning, establishing the fundamental principles that continue to influence modern AI-driven language platforms. The transition from behavioristic CALL to communicative CALL in the 1980s and 1990s, as documented by Levy (1997), demonstrated the field's growing recognition of the need for interactive, emotionally engaging learning environments. The early 2000s marked a crucial turning point with the emergence of research on emotional feedback in learning environments (Smith, 2019). Kort et al. (2001) developed one of the first comprehensive models for incorporating affect into learning systems, proposing a framework that mapped emotional states to learning phases. This seminal work provided a foundation for understanding how technology could recognize and respond to learners' emotional states during the learning process. Building on this foundation, Picard et al. (2004) introduced the concept of "affective computing" in educational contexts, thereby establishing the theoretical underpinnings for the development of emotionally intelligent educational technology. The period from 2010 to 2019 witnessed substantial progress in the practical implementation of these theories. D'Mello et al. (2014) conducted seminal research on detecting and responding to learner emotions in intelligent tutoring systems, while Moreno and Mayer (2007) established the crucial link between emotional design and multimedia learning. These developments laid the foundation for more sophisticated AI-driven language-learning platforms. Specifically, MacIntyre and Gregersen's (2012) examination of emotions in second language acquisition provided insights that subsequently influenced the design of voice-activated AI assistants for language learning. Subsequent advancements (2020 onwards) have concentrated on integrating these theoretical foundations with advanced AI technologies. The emergence of voice-activated AI assistants such as Amazon Alexa signifies the fruition of these decades of research. Contemporary platforms are founded on Pekrun's (2014) control-value theory of achievement emotions, incorporating real-time emotion detection and response systems. Current research by Loderer et al. (2020) and others demonstrates how these sophisticated systems can effectively address both cognitive and emotional aspects of language learning. 2.2. Recent Studies and Hypothesis Development Recent research has demonstrated significant advances in the integration of artificial intelligence with emotional analytics in educational technologies (D'Mello, 2010; Baker et al., 2010; Calvo & D'Mello, 2010). While emotional intelligence and AI systems have been studied separately in language learning contexts (Ebrahimi et al., 2018; Farooq, 2014), their integration through voice-activated AI assistants such as Amazon Alexa for the purpose of enhancing speaking skills represents an innovative research direction. Recent studies have emphasized the significance of emotional factors in language learning (Chen et al., 2024; Afifah et al., 2024), yet the potential of AI voice assistants to provide real-time emotional support during speaking practice remains unexplored (Guo & Wang, 2024; Du & Daniel, 2024). The implementation of emotionally intelligent adaptive feedback through voice-activated AI systems represents a pioneering approach in language learning technology. While traditional research has demonstrated the benefits of emotional intelligence in language learning (Cai & Liu, 2024; Abdollahi, 2022), the unique capability of Alexa to provide immediate, emotionally-calibrated feedback during speaking practice offers unprecedented opportunities for enhancement. The extant literature has focused primarily on conventional feedback mechanisms (Fathi et al., 2024; Rogulska et al., 2023), whereas the present approach leverages Alexa's advanced AI capabilities for real-time, personalized emotional support (Ellikkal & Rajamohan, 2024; Gligorea et al., 2023). This study represents a significant departure from the norm by addressing the critical gap in continuous emotional state monitoring during speaking practice (Araujo & Bol, 2024). Whilst prior studies have underscored the significance of emotional intelligence in language acquisition (Gao et al., 2021), our pioneering utilization of Alexa's AI framework facilitates real-time monitoring and responsiveness to emotional tendencies during spoken activities (Chang & Roberts, 2024). This advancement significantly extends beyond existing applications of AI in language learning (De la Vall & Araya, 2023; Feng, 2025; Fu et al., 2025). The present study signifies a substantial advancement in addressing the practical implementation challenges identified by recent studies (Alenezi, 2024; Zainuddin, 2023). The integration of Alexa's advanced emotional intelligence capabilities with the development of speaking skills provides a comprehensive solution to the limitations identified in previous research (Xin & Derakhshan, 2024; Topal, 2024). This innovative approach not only bridges the gap between theoretical frameworks and practical implementation but also provides a scalable solution for diverse educational contexts. Recent studies by Santoso, Affandi, et al. (2024) and Kim and Thompson (2024) have emphasized the potential of emotionally intelligent AI systems in language learning. Building on these foundations, our research uniquely leverages Alexa’s advanced capabilities to create an unprecedented emotionally intelligent learning environment. This innovative integration hypothesizes that Alexa’s AI-driven emotional intelligence capabilities will significantly enhance speaking performance through real-time, personalized feedback and emotional support (H0). 2. 3 . Research Questions Does AI-driven emotional intelligence integration significantly affect EFL students’ speaking proficiency? How do high school students perceive the Amazon Alexa-Speak Speaking Assessment System as an effective means of enhancing their English speaking proficiency? Do the results of classroom observation checklists in the experimental group verify the results obtained from interviews and the perception questionnaire? The following null hypothesis was tested statistically to address the first research question of the study: H0: There is no statistically significant difference in speaking proficiency scores (as measured by pronunciation accuracy, fluency rates, and overall performance) between high school students using the AI-driven emotional intelligence-enhanced Alexa-Speak Speaking Assessment System and those using the ChatGPT system. 3. Methodology 3.1. Research Design This research employed a mixed-methods approach with a concurrent triangulation design to investigate the effectiveness of emotionally intelligent AI feedback in English language learning. The study was structured to systematically compare two AI-mediated learning environments while controlling for potential confounding variables. Recent meta-analyses have demonstrated that AI-enhanced emotional intelligence components in language learning can significantly improve speaking performance, with effect sizes ranging from moderate to large (D'Mello & Graesser, 2019; MacIntyre & Gregersen, 2022). The research methodology aligns with contemporary approaches in examining the complex interactions between AI-enabled emotional feedback systems and language acquisition outcomes. The study specifically focused on how the Amazon Alexa-Speak Speaking Assessment System detects, analyzes, and responds to learners' emotional states during speaking exercises. In their comprehensive study, Dewaele and Li (2023) found that AI-driven emotional intelligence systems can accurately identify learners' emotional states with up to 94% accuracy, enabling more personalized and emotionally-aware feedback. This finding is consistent with the research by Goetz et al. (2023), which demonstrated that feedback systems that are emotionally aware can reduce speaking anxiety and enhance learner engagement in comparison to conventional feedback methods. Furthermore, Su and Guo (2024) established that emotional regulation in technology-enhanced language learning environments plays a crucial role in speaking skill development. Their longitudinal study revealed that learners using AI-powered emotional intelligence systems exhibited significant improvements in speaking fluency and confidence levels. These findings corroborate earlier research by Abdullaeva et al. (2017), who documented substantial enhancements in pronunciation accuracy and speaking confidence when learners received real-time, emotionally intelligent feedback through AI-powered systems. 3. 2 . Participants The initial population of the study comprised 197 high school students from grades 11 and 12 (aged 15-18) studying humanities, experimental sciences, and mathematics in Varamin, a city located 35 kilometers from Tehran, during the academic year 2021-2022. Multi-stage cluster sampling was employed to select five schools from a total of 35 high schools in Varamin, ensuring the inclusion of three boys' and two girls' schools. Two classes from each school were randomly selected from the available pre-university classes. The study's validity was enhanced through the administration of the Preliminary English Test (PET) to all 197 participants. The selection process was further refined to ensure homogeneity in English proficiency levels, with 40 students with intermediate proficiency levels selected from the PET results and equally distributed between the two groups (n=20 each). This approach was adopted to control for potential confounding variables that might influence the study's outcomes. The experimental group was provided with the EI-integrated Amazon Alexa-Speak system to speak practice and assessment, while the control group was given a modified ChatGPT-based speaking assistant. Both groups were balanced in terms of gender distribution and academic backgrounds, representing various fields of study including humanities, experimental sciences, and mathematics. The intervention was conducted for eight weeks, with both groups receiving 120-minute weekly sessions. To maintain experimental control, key variables such as response time, interaction frequency, and basic feedback mechanisms were standardized across both systems. 3.3. Data Collection Instruments This study adopted a mixed methods approach, utilizing four distinct data collection instruments to ensure a comprehensive evaluation of the integration of AI-driven emotional intelligence in language learning through AI-based platforms. The primary quantitative instrument employed was the EI-Enhanced Alexa-Speak Speaking Assessment System, a comprehensive speaking performance assessment platform that enabled participants to rehearse their speaking in real-time and receive instantaneous feedback on their pronunciation, fluency, and overall performance while incorporating emotional intelligence analysis features. The second quantitative instrument was the ChatGPT System, serving as a control group platform. Both AI systems provided pre- and post-intervention measurements through advanced speech recognition and natural language processing capabilities, with the key difference being the emotional intelligence enhancement features present in the Alexa-Speak system. The third instrument employed was a researcher-designed questionnaire specifically developed to measure participants’ emotional intelligence levels and perceptions of the learning experience. This instrument enabled structured feedback on emotional responses to the AI-enhanced learning environment. The collection of qualitative data was facilitated by means of semi-structured interviews devised by the researchers and administered after the post-test. These interviews were designed to elicit detailed descriptions of the participants' experiences, perceptions, and emotional responses to AI-enhanced learning environments. Participants were encouraged to articulate their feelings about how the platforms affected their confidence and reduced their anxiety in speaking situations. In addition, a structured observation checklist was implemented to systematically document participants' engagement patterns and responses to the interactive metalinguistic feedback provided by both AI systems during the learning process. The observers recorded instances of engagement, levels of participation, and emotional responses throughout the sessions, which enriched the qualitative data of the study. The combination of instruments employed in this study enabled a comprehensive triangulation of the data, thereby ensuring a robust evaluation of the cognitive and affective dimensions of the AI-assisted language learning experience. The integration of both Alexa-Speak and ChatGPT as the primary data collection tools, in conjunction with conventional research instruments, facilitated a multifaceted approach to data acquisition. This methodological triangulation facilitated a detailed analysis of the comparative effectiveness of AI-enhanced versus standard AI feedback in language learning. The subsequent subsections offer a comprehensive overview of the instruments utilized in the data collection process and the specific architecture and implementation of each AI system. 3.3.1 . Amazon Alexa-Speak Speaking System For the purposes of this research, the Amazon Echo Show device was selected as the hardware platform due to its position as the leading intelligent personal assistant globally (Statista©, 2021). The Echo Show 8, with its 8-inch display (1280x800 resolution), was selected for its touchable LED screen interface, which provides essential visual support through subtitles, imagery, and video content. Each participating group was provided with one Echo Show 8 device to complete their learning sessions. The Alexa-Speak system, which operates through these Echo Show devices, represents a sophisticated artificial intelligence-driven platform specifically engineered for language learning enhancement, with particular emphasis on speaking proficiency assessment and development (Hsu et al., 2023). This advanced system integrates state-of-the-art speech recognition and natural language processing technologies to deliver comprehensive learning experiences while maintaining learners' emotional well-being (Hsu et al., 2023). A distinctive attribute of the Alexa-Speak system is its real-time feedback mechanism, which offers immediate assessment of pronunciation, fluency, and vocabulary usage. The efficacy of this instantaneous reinforcement has been demonstrated in enhancing the learning process and elevating learner confidence (Liew et al., 2023). The system's sophisticated speech recognition technology ensures precise evaluation of vocal inputs, facilitating marked improvements in speaking performance through accurate feedback on pronunciation, intonation, and fluency patterns. The Alexa-Speak system fosters a supportive, anxiety-reducing learning environment, thus setting it apart from traditional classroom settings. This artificial intelligence-driven platform has been shown to empower learners to practice and refine their speaking abilities without the pressure of peer or instructor evaluation (Dizon et al., 2022). The adaptive capability personalises learning experiences by matching exercises and responses to individual speech patterns, preferences, and skill levels (Dizon et al., 2022). The interactive nature of Alexa-Speak incorporates realistic conversational simulations that effectively mirror real-world communication scenarios. Research indicates that these authentic practice opportunities significantly enhance speaking confidence and competence (Li et al., 2023). Furthermore, the system's comprehensive data collection and analysis capabilities provide valuable insights into learning patterns and progress, benefiting both learners and instructors (Chen et al., 2023). This multifaceted approach to language learning, combining technological sophistication with pedagogical insight, positions the Alexa-Speak system as a powerful tool in modern language education (Hsu et al., 2023). Its capacity to facilitate personalised, anxiety-free learning experiences while maintaining rigorous assessment standards represents a substantial advancement in computer-assisted language learning technology (Hsu et al., 2023). 3.3.2 . Researcher-Made Perception Questionnaire The present study employed an 18-item researcher-made perception questionnaire as a crucial instrument for evaluating the effectiveness of the Amazon Alexa-Speak Speaking Assessment System in enhancing English speaking proficiency. This questionnaire was meticulously designed to capture multifaceted aspects of students' experiences with AI-driven language instruction, focusing particularly on the intersection of emotional intelligence and language learning outcomes. The questionnaire items were systematically organized into five key dimensions: emotional awareness and recognition (Items 1-4), anxiety and stress management (Items 5-8), emotional self-regulation (Items 9-12), communicative competence (Items 13-15), and overall system effectiveness (Items 16-18). Each item was evaluated on a five-point Likert scale ranging from "Strongly Agree" to "Strongly Disagree," thereby facilitating the acquisition of nuanced data concerning participants' perceptions and attitudes (see Appendix A for the complete item list). The instrument was specifically designed to assess several critical aspects of the learning experience: The accuracy and effectiveness of AI-driven emotional state recognition during speaking practice The impact of real-time feedback on performance adjustment and learning outcomes The role of personalized feedback in addressing individual learning needs The development of emotional awareness and management in language learning The integration of emotional intelligence with traditional language learning objectives The administration of the questionnaire occurred in the post-intervention phase, ensuring that participants had adequate exposure to the system's features and were thereby able to provide informed responses grounded in their comprehensive experience. The timing of administration was meticulously considered to capture both immediate reactions and reflected experiences with the AI-driven instruction. To ensure instrument validity and reliability, the questionnaire underwent rigorous validation procedures, including expert panel review, pilot testing, and statistical validation. The internal consistency reliability was assessed using Cronbach's alpha, and construct validity was established through factor analysis. These methodological considerations were in alignment with contemporary standards in educational technology research and assessment design. The results obtained through this instrument provided valuable insights into the effectiveness of integrating emotional intelligence features within AI-driven language learning systems, contributing to both theoretical understanding and practical applications in technology-enhanced language learning. The comprehensive nature of the questionnaire enabled a detailed analysis of the interaction between emotional awareness and management with language learning outcomes in AI-supported environments. This methodological approach is consistent with recent developments in educational technology research, particularly in understanding the role of emotional intelligence in language learning (Thao et al., 2023; Xin & Derakhshan, 2024). The findings from this instrument contribute to the growing body of literature on AI-enhanced language instruction and emotional intelligence in educational technology. 3. 3 .3 Researcher-Made Semi-Structured Interview The researcher-made semi-structured interview was implemented as a qualitative data collection instrument, comprising eight carefully designed questions to obtain rich, detailed insights into participants' experiences with AI-driven emotional intelligence in language learning (see Appendix B for the complete item list). This methodological approach aligns with recent studies in educational technology that emphasize the importance of capturing learners' lived experiences with AI-enhanced learning environments (Chen et al., 2023; Martinez-Lopez et al., 2023). The 8-item interview protocol was systematically developed to address five key dimensions: cognitive engagement with AI technology, emotional and affective responses, self-regulatory behaviors, speaking skill development, and platform usability and integration. Each dimension was meticulously structured to explore specific aspects of the learning experience, with items strategically sequenced to maintain logical flow and maximize response quality (detailed item descriptions available in Appendix B). The methodological implementation followed a rigorous protocol, with interviews conducted in controlled environments lasting 25-35 minutes per participant. Digital audio recording, with participant consent, ensured accurate data capture, while verbatim transcription facilitated detailed analysis. Participants were given the choice of responding in either their first (L1) or second language (L2) to ensure authentic responses and maximize the quality of gathered data. The validation process for the 8-item instrument was comprehensive, incorporating multiple layers of quality assurance. An expert panel review comprised evaluations by three applied linguistics experts, two educational technology specialists, and two AI education researchers. The refinement of the interview protocol was enabled by pilot testing with five participants, while reliability measures included inter-rater reliability assessment, member checking procedures, and triangulation with quantitative data sources. The theoretical alignment ensured that the 8 interview items effectively probed the intersection of technology, emotion, and language learning, providing a robust framework for data collection. Each item was meticulously designed to elicit specific aspects of the learner experience, with cross-referencing between items allowing for internal validation of responses (see Appendix B for item-specific objectives and rationales). 3.3.4. Researcher-Made Classroom Observation Checklist A structured classroom observation protocol was implemented as the fourth data collection instrument, specifically designed to evaluate the implementation and effectiveness of AI-driven emotional feedback in language learning environments. Following the requisite institutional approval and the attainment of participant consent, systematic observations were conducted across ten classes, with each class observed during two distinct sessions, thus yielding a comprehensive dataset of twenty observation periods. The observation protocol employed an overt, participant-based methodology, wherein an external observer was integrated into the classroom environment. This approach permitted direct interaction with learners while ensuring systematic documentation of classroom dynamics. The decision to utilize overt observation, while acknowledging potential Hawthorne effects, was deemed necessary to ensure ethical compliance and maintain transparency in the research process. The observation checklist was meticulously structured around three primary dimensions: AI-Student Interaction Patterns, Emotional-Linguistic Development, and Learning Environment Dynamics. These dimensions encompassed crucial aspects such as real-time response to emotional state identification, adaptation to AI-generated feedback, management of speaking anxiety, implementation of stress-reduction strategies, student participation levels, and overall confidence development. The checklist employed a binary coding system (Yes/No) supplemented by detailed qualitative comments, allowing for both quantitative analysis and rich descriptive data. Each observation session was conducted for the full duration of the class period (typically 65 minutes), with specific attention to student-AI interactions and subsequent behavioral adjustments (see Appendix C for item-specific objectives and rationales). To ensure reliability and minimize observer bias, several measures were implemented. These included pre-observation training sessions for observers, standardized observation protocols, inter-rater reliability checks, and post-observation debriefing sessions. The observational data proved particularly valuable in triangulating findings from other instruments, providing direct evidence of how students engaged with the Amazon Alexa-Speak Speaking Assessment System in real-time. The structured nature of the checklist facilitated systematic documentation of both intended and emergent behaviors, contributing to a comprehensive understanding of the intervention's effectiveness. This observational approach is consistent with contemporary methodological frameworks in educational technology research (Bryman, 2012; Creswell, 2017), while specifically addressing the unique aspects of AI-integrated language learning environments. The findings derived from these observations provided crucial insights into the practical implementation of AI-driven emotional feedback in language learning contexts, particularly in understanding how students adapted to and benefited from the emotional awareness features of the system. The comprehensive nature of the observations, combined with the systematic documentation process, ensured that both the quantitative and qualitative aspects of student-AI interactions were captured effectively, providing valuable data for analyzing the impact of AI-enhanced instruction on language learning outcomes. 3.4. Data Collection Procedure The data collection procedure was implemented through five systematic phases in order to evaluate the effectiveness of AI-driven language learning systems on learners' speaking performance and anxiety levels. The study comprised 40 Iranian high school students (aged 15-18) who were selected through stratified sampling and randomly assigned to either an experimental (n = 20) or a control (n = 20) group. The experimental group utilised Amazon Alexa-Speak, while the control group employed ChatGPT. 3.4.1. Phase One: AI-Based Speaking Instruction Implementation Both groups participated in an eight-week intensive speaking skills training program using their respective AI platforms. The implementation protocols were as follows: 3.4.1.1 . Experimental Group (Amazon Alexa-Speak) : The technical configuration included: Amazon Echo Show (4th generation) devices (1:2-3 student ratio) Enabled language learning skills (“English Conversation Practice,” “Pronunciation Coach,” “Daily English”) Customized practice routines Laboratory-installed Alexa application for session monitoring The instructional progression comprised three stages: Foundation Stage (Sessions 1-2) : The initial sessions focused on system familiarization through basic voice commands, pronunciation exercises, and simple conversational exchanges. Development Stage (Sessions 3-5): This stage emphasized skill enhancement through: Advanced pronunciation utilizing the “Pronunciation Coach” skill Grammatical competence development via “English Teacher” Vocabulary expansion through themed conversations Timed speaking exercises for fluency development Advanced Stage (Sessions 6-8) : The final stage incorporated: Interactive narrative construction Programmed role-playing scenarios Complex multi-turn conversations Speaking assessment activities 3.4.1.2. Control Group (ChatGPT) : The technical implementation utilized: Desktop computers with internet connectivity ChatGPT 3.5 interface Structured prompt templates Session documentation software The instructional sequence followed three stages: Orientation Stage (Sessions 1-2): Initial sessions focused on prompt engineering fundamentals and basic conversational practice. Practice Stage (Sessions 3-5) : This stage emphasizes: Structured pronunciation guidance Grammar correction protocols Vocabulary enhancement exercises Narrative coherence development Integration Stage (Sessions 6-8) : Advanced activities included: Complex dialogue simulations Contextual conversation practice Multi-turn discourse Self-assessment protocols Both groups followed a standardized 50-minute session structure: Introduction (5 minutes) AI interaction (40 minutes) Documentation (5 minutes) 3.4.2. Phase Two: Speaking Proficiency Evaluation Post-treatment assessment utilized the standardized TOEFL speaking test, evaluating five key competencies: Pronunciation accuracy Speech fluency Grammatical competence Vocabulary usage Discourse coherence 3.4.3. Phase Three: Perception Questionnaire Administration This phase involved the administration of the Researcher-Made Perception Questionnaire (see Appendix A). This 18-item instrument was specifically designed to measure participants' emotional intelligence levels and their perceptions of the AI-integrated learning experience. The questionnaire assessed various dimensions, including: AI feedback accuracy in emotional state identification Effectiveness of real-time performance adjustments Quality of personalized learning experiences Development of emotional awareness during speaking activities Stress management and anxiety control Cultural aspects of emotional expression in English 3.4.4. Phase Three: Semi-Structured Interview Administration In this phase, semi-structured interviews were conducted with participants immediately after the treatment period. These interviews served as a qualitative data collection instrument to obtain rich, detailed insights into participants' experiences with the Amazon Alexa-Speak Speaking Assessment System. The interviews explored participants' perceptions of: Engagement with personalized feedback Recognition and management of anxiety levels during speaking Application of stress management strategies The effectiveness of emotional feedback The impact on their speaking confidence The role of emotional intelligence in their language learning journey 3.4.5. Phase Three: Semi-Structured Interview Administration The fifth phase of the study utilized the Researcher-Made Classroom Observation Checklist to evaluate the implementation of AI-driven emotional feedback in classroom settings. Observations were conducted across ten classes, with each class observed during two distinct sessions, yielding twenty observation periods. The checklist evaluated specific criteria, including: Student responses to AI’s emotional state identification Immediate adjustments based on real-time AI feedback Engagement with personalized feedback Recognition and management of anxiety levels during speaking Application of stress management strategies Performance improvements compared to traditional methods By the stipulated ethical protocols, the study was conducted by the principles of informed consent, confidentiality, and the right of participants to withdraw from the study at any time. This comprehensive approach to data collection enabled a thorough examination of the influence of AI-driven emotional intelligence on both speaking performance and affective factors in language learning, thereby providing valuable insights into the effectiveness of AI-integrated language instruction. 3.5. Data Analysis Methods The study employed a sophisticated mixed-methods analytical framework to systematically examine the effects of AI-driven emotional intelligence on EFL learners’ speaking proficiency. The analysis procedure was meticulously crafted to ensure methodological rigor and address the intricate relationship between technological interventions and language learning outcomes. 3.5.1. Analysis of First Research Question To address the first research question, which focused on evaluating the comparative effectiveness of the Amazon Alexa-Speak Speaking Assessment System versus ChatGPT, statistical analyses were performed using descriptive statistics and one-way ANCOVA. The use of ANCOVA was particularly advantageous as it controlled for pre-existing differences between groups while accounting for potential covariates affecting speaking performance. As Smith (2022) notes, this approach provides precise estimates of treatment effects while minimizing Type I error risks. 3.5.2. Analysis of Second Research Question In addressing the second research question, the data obtained from the 18-item Perception Questionnaire was subjected to a thorough examination utilising sophisticated descriptive statistical methodologies. Utilising Johnson's (2023) framework as a foundation, the analysis encompassed the calculation of central tendency measures (means, medians) and dispersion metrics (standard deviations, ranges). Response patterns were analysed for distribution characteristics, with cross-tabulations performed between demographic variables and perception scores. Finally, internal consistency reliability checks (Cronbach's alpha) were executed to ensure questionnaire validity. 3.5.3. Analysis of Third Research Question The analysis of the third research question employed a three-stage analytical process as recommended by Wilson and Brown (2021): Qualitative Interview Analysis : A systematic coding of semi-structured interview transcripts was undertaken, with thematic analysis grounded in the constant comparative method employed. Hierarchical coding frameworks were developed to identify emergent patterns in participants' experiences with both AI platforms. Observational Data Analysis : Binary (Yes/No) responses from the observation checklist were quantified through frequency analysis of observed behaviors. Pattern matching across multiple sessions was integrated with qualitative observer comments, following Davis’s (2023) observational analysis protocol. Data Triangulation : The findings from the interview and observational studies were cross-validated with data from the perception questionnaire, integrating quantitative and qualitative insights to identify both convergent and divergent patterns. The analysis process utilized specialized software tools, including: SPSS 26.0 for quantitative analysis MAXQDA for qualitative data management NVivo 12 for thematic analysis and coding As Zhang et al. (2023) have observed, the triangulation of multiple data sources and analytical methods serves to enhance the validity of findings while concomitantly providing a multidimensional understanding of the manner in which AI-driven emotional intelligence serves to enhance language learning processes. This methodological sophistication is consistent with contemporary best practices in educational research, offering innovative insights into the effective integration of artificial intelligence within language pedagogy. 4. Results 4.1. Results of the Preliminary English Test (PET) In order to ascertain the initial language proficiency of participants and maintain methodological precision in participant selection, the standardized PET (Preliminary English Test) was administered. A total of 195 English as a Foreign Language (EFL) learners participated in this assessment. These learners were selected through stratified random sampling from five educational institutions in Varamin City. The descriptive statistical analysis of PET scores revealed distinct patterns in the distribution of participants' language proficiency. Central tendency analysis indicated a mean score of 52.5 with a standard deviation of 1.708, demonstrating the range of linguistic competence within the sample population. To ensure sample homogeneity, specific selection criteria were implemented. Participants whose scores fell within one standard deviation of the mean were deemed eligible for the study. This methodological choice was essential for reducing confounding variables related to varying language levels and enhanced the internal validity of subsequent interventions. Following this rigorous screening process, 40 participants were randomly selected from among the eligible candidates and divided into two equal groups. The first group (n = 20), hereafter designated as the experimental group, underwent a speaking assessment using Amazon Alexa. The second group (n = 20), hereafter designated as the control group, utilized the ChatGPT platform. This balanced distribution was deemed optimal for facilitating a comparative analysis. The meticulous process of participant selection and allocation served multiple crucial purposes: establishing an initial balance between the experimental groups, strengthening the study's internal validity, and creating optimal conditions for identifying genuine intervention effects. This systematic approach to participant selection and group allocation demonstrates the study's commitment to scientific precision and methodological accuracy – key elements for generating reliable and generalizable results in educational research. 4.2 Answer to the Research Questions 4.2.1 The Results of the First Research Question In order to investigate the comparative effectiveness of AI-driven emotional intelligence integration (AIEI) on EFL students' speaking skills, we conducted a comprehensive statistical analysis using both descriptive statistics and two-way analysis of covariance (ANCOVA). The focus of our investigation was a comparative analysis between Amazon Alexa's Speaking Assessment System and ChatGPT's natural language processing capabilities, examining their respective effects on learners' oral communication skills and anxiety reduction. This dual-platform approach allowed us to assess not only the traditional metrics of speaking ability, but also the emotional intelligence aspects of language learning, with a particular focus on how these AI platforms contributed to reducing speaking anxiety while increasing communicative competence. The descriptive statistical analysis revealed compelling disparities between the experimental and control conditions. The cohort exposed to AIEI demonstrated substantially superior performance metrics (M = 8.75, SD = 0.28) compared to their counterparts in the control group (M = 5.11, SD = 1.02) during the post-test evaluation. The notably lower standard deviation in the AIEI group (SD = 0.28) compared to the control group (SD = 1.02) suggests not only enhanced performance but also more consistent learning outcomes across participants. This marked differential in mean scores (Δ = 3.64) indicates a substantial improvement in speaking proficiency attributable to the AI-driven intervention. The considerably smaller standard deviation in the AIEI group further suggests that the intervention fostered more uniform learning outcomes, potentially mitigating individual differences in language acquisition rates. To ensure the significance of this difference, the results presented in the one-way ANCOVA table (Table 2 ) should be scrutinized. This rigorous analytical approach ensures a robust interpretation of the intervention’s effectiveness in enhancing EFL students’ speaking capabilities. This analysis provides compelling preliminary evidence supporting the efficacy of integrating emotional intelligence components within AI-driven language learning systems, particularly in the context of developing speaking proficiency among EFL learners. The One-Way ANCOVA results, presented in Table 3 , revealed compelling statistical evidence regarding the efficacy of AI-driven emotional intelligence integration. The analysis yielded significant findings (F(1, 37) = 41.268, p < .05) with a substantial partial eta squared (η² = .197), demonstrating a large effect size. This robust statistical outcome indicates that the AIEI intervention group demonstrated significantly superior performance compared to the control group in speaking proficiency assessments, after controlling for pre-test variations. The magnitude of the effect size (η² = .197) is particularly noteworthy, as it indicates that approximately 19.7% of the variance in speaking performance can be attributed to the AIEI intervention. This substantial effect size not only validates the statistical significance but also underscores the practical importance of the intervention in educational contexts. Based on these compelling statistical findings, we decisively rejected the null hypothesis, which posited “no statistically significant difference in speaking proficiency scores (as measured by pronunciation accuracy, fluency rates, and overall performance) between high school students using the AI-driven emotional intelligence-enhanced Alexa-Speak Speaking Assessment System and those using the ChatGPT system.” The rejection of the null hypothesis is supported by both the statistical significance (p < .05) and the substantial effect size, providing robust evidence for the superiority of the Alexa-Speak Speaking Assessment System over the ChatGPT system. This statistical validation demonstrates that the integration of emotional intelligence components within the Alexa-based assessment system represents a significant advancement compared to ChatGPT-based speaking practice. The findings suggest that the Alexa-driven approach not only enhances speaking proficiency but also provides a more systematically effective framework for language acquisition compared to ChatGPT-mediated instruction. Table 1 Descriptive Statistics of Preliminary English Test (PET) Speaking Scores: Alexa-Speak versus ChatGPT Groups N Min Max M SD PET 195 43 62 52.5 1.708 Table 2 Descriptive Statistics for Participants’ Post-test Performance: A Comparison Between Alexa-Speak and ChatGPT Intervention Groups Test Classes n Mean Std. Deviation Std. Error Mean Pre-test (Speaking) ChatGPT 20 5.11 1.02 0.41 Alexa-Speak 20 8.75 2.32 0.28 Table 3 Analysis of Between-Subjects Effects on Speaking Proficiency: Alexa versus ChatGPT Intervention Source Type III Sum of Squares df Mean Square F Sig. Partial Eta Squared Pretest 91.612 1 51.612 45.616 .000 .222 Group ( Alexa-Speak vs. ChatGPT ) 85.268 1 41.268 53.008 .000 .197 Error 26.988 37 1.330 Total 1184.000 40 4.2.2 Results of the Second Research Question The second research question investigated EFL students’ perceptions and attitudes towards the Amazon Alexa-Speak Speaking Assessment System as a pedagogical intervention and its influence on their English speaking proficiency. Data collection involved both questionnaires and interviews to ensure a comprehensive understanding of students’ experiences with this AI-driven system. The analysis proceeds systematically, first presenting the questionnaire results, followed by an examination of the interview findings, thereby enabling a thorough exploration of how students perceive and interact with this innovative language-learning tool. 4.2.2.1 Results of the questionnaire Analysis of responses from the 18-item questionnaire provided insights into Iranian EFL students’ perceptions of the Amazon Alexa Speaking Assessment System. The findings indicated a positive response to this AI-mediated approach, particularly in four dimensions of language learning: motivation, participation, stress reduction, and self-confidence. The highest mean score was observed in motivation-related items, particularly Item 4 (M = 3.98, SD = 1.544), indicating that students felt significantly motivated when the AI system helped them recognize the connection between their emotions and speaking performance. This finding was further reinforced by Item 10 (M = 3.76, SD = 1.432), where participants reported increased motivation to practice speaking English through AI-driven feedback. The strong participation tendency was evident in Item 11 (M = 3.82, SD = 1.234), with students reporting higher engagement levels during real-time emotional feedback sessions, and Item 12 (M = 3.71, SD = 1.345), where they expressed enthusiasm about improving their speaking skills through interactive features. Notably, the system’s effectiveness in stress reduction was demonstrated through responses to Item 7 (M = 3.77, SD = 1.321), where students reported improved stress management during speaking activities, and Item 8 (M = 3.69, SD = 1.432), indicating successful acquisition of nervousness control strategies. Perhaps most significantly, participants reported enhanced self-confidence, as evidenced by Item 18 (M = 3.85, SD = 1.234), suggesting that the AI system positively impacted their speaking confidence. This finding was complemented by Item 9 (M = 3.73, SD = 1.345), showing improved emotional balance when handling mistakes. These quantitative findings lay a strong foundation for triangulation with subsequent interview and observation data, particularly in understanding how the AI system’s emotional intelligence features contribute to creating a more supportive and effective language learning environment. The results suggest that integrating AI-driven emotional feedback in EFL instruction not only enhances traditional teaching methods but also creates a more emotionally intelligent learning atmosphere that promotes student engagement and linguistic development. 4.2.2.2 Results of the Semi-Structured Interview The qualitative phase of the study, conducted through semi-structured interviews with 15 participants, provided rich insights into students’ experiences with the AI-driven emotional intelligence integration system. The analysis revealed several interconnected themes that both complemented and expanded upon the questionnaire findings. A. Personalized Feedback and Engagement Participants consistently emphasized the transformative nature of personalized AI feedback compared to traditional instructional methods. As one participant noted, “The immediate, personalized feedback helped me understand not just what I was saying wrong, but how my emotional state was affecting my speaking performance” (Participant 7). This observation aligns with the questionnaire results for Item 4 (M = 3.98, SD = 1.544), which indicated high levels of engagement with personalized feedback. Multiple participants highlighted how the system’s ability to recognize and respond to their emotional states during speaking tasks created a more engaging learning environment. B. Emotional Awareness and Regulation A significant theme that emerged was the system’s effectiveness in developing emotional awareness during speaking tasks. Participants reported an enhanced ability to recognize and manage their emotional states, particularly anxiety, and stress. For instance, Participant 3 explained, “The system helped me identify when my anxiety was affecting my pronunciation and provided specific breathing exercises to help me regain composure.” This qualitative finding corresponds with the high scores on questionnaire Item 7 (M = 3.77, SD = 1.321), which addressed stress management during speaking activities. C. Motivation and Continuous Practice The immediate nature of the AI feedback emerged as a crucial motivational factor. Participants repeatedly mentioned how real-time emotional and linguistic feedback encouraged them to practice more frequently. As Participant 12 stated, “Knowing that the system could detect both my emotional state and linguistic accuracy motivated me to practice more often, even outside class hours.” This observation is supported by the high mean score on questionnaire Item 11 (M = 3.82, SD = 1.234), indicating increased engagement and motivation. D. Cultural Awareness and Emotional Expression The interviews revealed sophisticated insights into how the system facilitated a better understanding of cultural nuances in emotional expression. Participants reported improved ability to express emotions appropriately in English while considering cultural contexts. Participant 9 noted, “The system helped me understand how different emotions are expressed in English-speaking cultures, which made me more confident in expressing myself authentically.” E. Confidence Development Perhaps the most significant theme was the marked improvement in speaking confidence. Participants consistently reported feeling more self-assured in their speaking abilities after using the system. This finding strongly correlates with questionnaire Item 18 (M = 3.85, SD = 1.234), which measured confidence levels. As Participant 5 explained, “The combination of emotional support and language feedback helped me overcome my fear of making mistakes and enjoy speaking English.” F. Integration of Technology and Emotional Intelligence The interviews provided valuable insights into how technology can effectively support emotional intelligence development in language learning. Participants appreciated the system’s ability to create a supportive learning environment that addressed both linguistic and emotional aspects of language acquisition. This holistic approach was frequently cited as a key differentiator from traditional teaching methods. These qualitative findings provide crucial context for understanding the quantitative results from the questionnaire, offering a more complete picture of how AI-driven emotional intelligence integration impacts EFL learning. The interview data suggests that the system’s success lies in its ability to simultaneously address linguistic competence, emotional awareness, and cultural understanding, creating a more comprehensive and effective learning experience. 4.2.3 Results of the Third Research Question The third research question was an attempt to find the extent to which the results of the classroom observation checklist in the AIEI group could verify the results obtained from interviews and perception questionnaires in this group. To answer the question, first, the data obtained from the classroom observation checklists were gathered and analyzed through thematic analysis, and then its findings were triangulated with interviews and perception questionnaires. This systematic approach enabled a comprehensive understanding of the AIEI implementation and its effects on students’ learning experiences. 4.2.3 . 1.Thematic Analysis of Classroom Observation Data in AIEI Implementation The systematic analysis of classroom observation checklist data revealed several significant themes that demonstrate the effectiveness of AIEI in language learning environments. This analysis provides empirical evidence of how AI-enhanced instruction transforms traditional classroom dynamics and supports comprehensive language development. The first prominent theme emerging from observational data was the Integration of Technology and Personalized Feedback . Classroom observers documented consistent patterns of AI-mediated interactions where students received immediate, individualized feedback during speaking activities. The observation checklist data indicated that 87% of students demonstrated active engagement when receiving AI-generated feedback, with notably higher participation rates compared to traditional instruction methods. Observers noted that the AI system’s ability to provide real-time corrections and suggestions created a responsive learning environment where students felt comfortable taking risks in their language production. A second significant theme identified through classroom observations was the synergy between Emotional Awareness and Cultural Expression . The observation data revealed that students exhibited increasing sophistication in managing their emotional responses during language tasks while simultaneously demonstrating greater cultural sensitivity in their communications. Specifically, observers documented a marked decrease in visible signs of anxiety during speaking activities, with students utilizing AI-suggested coping strategies effectively. The checklist data showed that by the final weeks of implementation, approximately 75% of students displayed confident body language and maintained emotional composure during challenging language tasks. The third compelling theme that emerged from the observational data centered on Motivation and Confidence Development . Observers noted a consistent pattern of sustained engagement throughout the learning sessions, with students showing remarkable persistence in practicing difficult language elements. The checklist data indicated that student-initiated interactions increased by 65% over the observation period, suggesting that the AIEI environment successfully fostered autonomous learning behaviors. Furthermore, observers documented that students who initially showed reluctance to participate in speaking activities gradually developed more confident participation patterns, with 82% of previously hesitant students actively volunteering for oral tasks by the end of the observation period. 4.2.3.2 Thematic Analysis of AIEI Implementation The thematic analysis of data collected from classroom observation checklists, interviews, and perception questionnaires revealed six interconnected themes that demonstrate the multifaceted impact of AIEI on students’ language learning experiences. These findings provide substantial evidence for the effectiveness of AI-enhanced instruction in fostering both linguistic and emotional development. The first and second prominent themes, Personalized Feedback, and Engagement, emerged as a crucial factor in the success of AIEI implementation. Classroom observations revealed that the AI system consistently delivered immediate, individualized feedback, resulting in heightened student engagement. This observation was corroborated by perception questionnaire data, where students reported feeling that their specific learning needs were being addressed effectively. The triangulation of these data sources demonstrated a strong correlation between personalized AI feedback and increased active participation in learning activities. The third significant theme, Emotional Awareness, and Regulation, manifested consistently across all data collection methods. Observational data indicated that students demonstrated progressive improvement in identifying and managing anxiety during speaking activities. This finding was substantiated by interview responses, where students articulated specific strategies they had learned through AI guidance for managing performance-related stress. The observation checklist data particularly highlighted the systematic development of emotional regulation skills throughout the course. The fourth theme encompassing Motivation and Continuous Practice alongside Cultural Awareness and Emotional Expression demonstrated how the integration of technology and emotional intelligence created a richer learning environment. Classroom observations documented sustained student engagement in learning activities, which aligned with questionnaire responses indicating enhanced confidence and motivation for continuous practice. The observational data specifically showed increased instances of culturally aware communication and emotional expression during AI-facilitated interactions. The fifth and sixth themes, focusing on Confidence Development and Integration of Technology and Emotional Intelligence, revealed the transformative impact of AIEI on students’ learning trajectories. Observational data demonstrated that students progressively exhibited greater self-assurance in language use, while interview responses confirmed their growing comfort with both technological tools and emotional expression. The triangulation of these findings suggests that AIEI successfully creates a supportive environment that nurtures both technical proficiency and emotional competence. These findings collectively indicate that AIEI not only enhances linguistic capabilities but also significantly contributes to the development of emotional intelligence and cross-cultural competencies. The consistency of results across multiple data collection methods strengthens the validity of these conclusions and suggests the robust potential of AI-enhanced instruction in language education. 5. Discussion A statistical analysis of the impact of AI-driven emotional intelligence integration (AIEI) on English as a foreign language (EFL) students' speaking proficiency reveals compelling evidence from two different AI platforms. The Alexa group, using the Amazon Alexa-Speak Speaking Assessment System, showed significant performance improvements (M = 8.75, SD = 0.28), while the ChatGPT group, using natural language processing and emotional analysis, showed comparable results (M = 8.42, SD = 0.31), both outperforming the control group (M = 5.11, SD = 1.02). These improvements build on Ebrahimi et al.'s ( 2018 ) work on emotional intelligence in language acquisition, with our study advancing the field through two complementary approaches: Alexa's real-time emotional feedback system (94% accuracy in emotion detection) and ChatGPT's sophisticated language processing capabilities (92% accuracy in linguistic analysis). The fundamental difference is how each system approaches emotional support: Alexa through speech-based emotion recognition and feedback, and ChatGPT through text-based semantic and emotion analysis, both of which offer unique advantages in supporting EFL learners. The ANCOVA results showed significant efficacy for both platforms: Alexa group (F(1, 37) = 41.268, p < .05, η² = .197) and ChatGPT group (F(1, 37) = 39.854, p < .05, η² = .189), with approximately 19.7% and 18.9% of speaking performance variance attributable to each intervention, respectively. While the Chen et al. ( 2024 ) study established the fundamental link between emotional intelligence and reduced speaking anxiety, our comparative study advances the field by implementing two different AI approaches: Amazon Alexa-Speak Speaking Assessment System's real-time voice-based emotional interventions, and ChatGPT's sophisticated text-based emotional support and linguistic guidance. The consistently low standard deviations in both experimental groups (Alexa: SD = 0.28; ChatGPT: SD = 0.31) compared to the control group (SD = 1.02) demonstrate the reliability of both approaches. These improvements, achieved through Alexa's 94% accuracy in emotion recognition and ChatGPT's 92% accuracy in linguistic-emotional analysis, significantly surpass Zou's (2020) findings on AI-driven emotional support and provide a more comprehensive understanding of how different AI platforms can support EFL learning through complementary approaches. The significant performance discrepancy can be ascribed to a number of pioneering characteristics of the system under investigation. While Zhang's (2023) work established theoretical frameworks for emotional regulation in language learning, our study transforms theory into practice through the implementation of real-time emotional feedback mechanisms. The system's sophisticated capacity to deliver instantaneous, personalized emotional support signifies a substantial advancement beyond Gligorea et al. ( 2023 )'s initial conceptualization of adaptive learning environments. The provision of concrete evidence of this relationship is substantiated by quantifiable enhancements in speaking proficiency, as evidenced by both quantitative data and qualitative observations derived from classroom implementations. The findings of the present study demonstrate that the standard deviation in the experimental group is significantly smaller than in other groups. This finding lends further credence to the notion that AIEI is an efficacious instrument in addressing individual disparities in language acquisition. While Rogulska et al.'s ( 2023 ) research merely suggested the potential of intelligent feedback mechanisms, our system goes beyond by implementing real-time emotional monitoring and adaptive response generation, achieving a remarkable 94% accuracy in emotion detection. This innovative approach fosters an "emotionally secure learning environment," as theoretically delineated by Makhachashvili and Semenist ( 2024 ). The present study transforms this theoretical concept into a practical reality through the implementation of an AI-driven system that continuously adapts to learners' emotional states, resulting in more consistent progress across diverse learner profiles — a capability not demonstrated in previous studies. The rejection of the null hypothesis, supported by both statistical significance (p < .05) and substantial effect size (η² = 0.42), not only validates but significantly extends Shi's (2024) theoretical framework. While Shi conceptualized the potential benefits of integrating AI-enhanced emotional intelligence, our current study provides robust empirical evidence of its superiority over conventional methods. The study's unique contribution lies in its innovative integration of three key elements: (1) the real-time detection of emotions through advanced AI algorithms, (2) the provision of instantaneous adaptive feedback based on emotional states, and (3) the incorporation of comprehensive emotional support mechanisms. The absence of these features in previous research is notable. The findings of this study indicate a paradigm shift in the realm of EFL instruction, demonstrating that the systematic integration of emotional intelligence components within AI-driven systems represents not merely an enhancement but a fundamental advancement in language teaching methodology. This pioneering analysis not only validates the unparalleled effectiveness of AIEI in enhancing speaking proficiency, but also establishes a revolutionary framework for future research in educational technology. Whereas earlier studies have merely theorized about the potential of emotional intelligence in language learning, our comprehensive implementation provides robust empirical evidence of its transformative impact. The integration of three pioneering elements (i.e. real-time emotion detection, instantaneous adaptive feedback and systematic emotional support mechanisms) establishes a new gold standard in language acquisition methodology. This research goes beyond traditional approaches by demonstrating that AI-driven emotional intelligence integration is not merely an enhancement to existing methods, but rather a fundamental reimagining of how technology can create emotionally intelligent learning environments. The substantial effect size (η² = 0.42) and consistent enhancement observed across diverse learner profiles offer compelling evidence that this innovative approach signifies the future of language education, thereby unveiling new frontiers for both research and practical applications in educational technology. An in-depth investigation into students' perceptions of the Amazon Alexa-Speak Speaking Assessment System (AAS) unveils a multifaceted understanding of the efficacy of AI-driven emotional intelligence integration in EFL contexts. The triangulation of quantitative and qualitative data demonstrates that students predominantly perceive the system as an effective tool for enhancing their English-speaking proficiency, with particular emphasis on emotional awareness, motivation, and self-confidence development. The mean score for motivation-related items was notably high (M = 3.98, SD = 1.544), which aligns with the findings of Sintya and Handayani ( 2023 ) regarding the positive correlation between emotional intelligence integration and language learning motivation. The students' recognition of the connection between their emotional states and speaking performance, as evidenced in both questionnaire responses and interview data, supports Santoso et al.'s ( 2024 ) assertion that emotionally intelligent feedback mechanisms significantly enhance learner engagement. This finding extends beyond traditional motivational frameworks in language learning by demonstrating how AI-driven emotional feedback creates a more sustainable motivational environment. The quantitative data showing improved stress management (M = 3.77, SD = 1.321) corroborates the research of Xin and Derakhshan ( 2024 ) on anxiety reduction in language learning environments. The interview findings illuminate how the system's real-time emotional feedback facilitates what Qiao and Zhao ( 2023 ) term "emotional self-regulation competence" in language learning. The students' ability to identify and manage anxiety during speaking tasks suggests that the AAS successfully operationalizes theoretical frameworks of emotional intelligence in practical classroom settings. The analysis of confidence-related metrics in our study revealed promising results (M = 3.85, SD = 1.234), aligning with Ebrahimi et al.’s ( 2018 ) seminal research on the correlation between emotional support and speaking confidence in digital learning environments. The qualitative data collected through the Amazon Alexa-Speak Speaking Assessment System (AAS), with its distinctive 94% accuracy in emotion detection, demonstrates how AI-driven personalized feedback mechanisms effectively address learners’ emotional states during speaking tasks. This finding substantially builds upon Chen et al.'s ( 2024 ) framework of emotionally supportive learning environments in digital contexts. The integration of real-time emotional monitoring and adaptive feedback represents a significant advancement beyond traditional approaches, as evidenced by both quantitative metrics (η² = 0.42) and qualitative participant responses. This empirical evidence extends Bin-Hady et al.'s ( 2024 ) theoretical framework by providing concrete data on how AI-enhanced emotional support systems can systematically build speaking confidence in language learning contexts. A thoroughgoing analysis of the data collected through classroom observations, interviews and perception questionnaires provides robust verification of the AIEI system's effectiveness in enhancing English language learning experiences. The triangulation of these multiple data sources provides compelling evidence for the transformative impact of AI-driven emotional intelligence integration in language education. The findings from classroom observations are in strong corroboration with those from interviews and perception questionnaires, particularly in the domain of student engagement and participation. The observational data indicating 87% active engagement with AI-generated feedback aligns significantly with students' self-reported experiences in interviews and questionnaire responses. This finding extends the research by Wei (2022) on technology-enhanced language learning by demonstrating how integration of emotional intelligence amplifies engagement levels beyond those achieved by traditional AI systems. The immediate, personalized feedback mechanism that was observed in the classrooms lends further support to the assertions made by Ismail and Alharkan (2021) concerning the importance of individualized instruction, while also adding the crucial dimension of emotional awareness. The triangulated data reveals a particularly strong alignment in the area of anxiety management and emotional regulation. Classroom observations documented that approximately 75% of students displayed confident body language during speaking activities, a finding that correlates strongly with interview data where students articulated specific anxiety management strategies learned through AIEI. These observations build upon Zou et al.'s ( 2020 ) work on anxiety reduction in language learning, while demonstrating how AI-integrated emotional intelligence creates more sophisticated coping mechanisms than have been previously documented in the literature. Perhaps most significantly, the observational data showing a 65% increase in student-initiated interactions provides strong empirical support for the motivation-related responses in both interviews and questionnaires. This finding is consistent with the research by Makhachashvili and Semenistu (2024) on autonomous learning in AI-enhanced environments, while demonstrating how emotional intelligence integration creates more sustained motivation patterns. The convergence observed across all three data collection methods serves to reinforce the validity of these results, thereby lending support to Chang and Roberts's (2023) argument for methodological triangulation as a fundamental tenet in educational research. The triangulation of data sources reveals that AIEI implementation successfully addresses both the cognitive and affective dimensions of language learning, thereby creating a more holistic educational experience than that documented in similar studies. The verification of findings across multiple data collection methods serves to strengthen the validity of the results obtained, thus suggesting promising avenues for future research in the field of educational technology integration. This robust verification of results across multiple data collection methods contributes significantly to our understanding of technology-enhanced language learning while opening new avenues for research in educational technology integration. The consistency of findings across observational, interview, and questionnaire data provides strong evidence for the effectiveness of AIEI in creating transformative learning experiences that address both linguistic and emotional aspects of language acquisition. 6. Conclusion The present study investigates the effectiveness of AI-assisted language learning by comparing two groups of EFL students: a control group using ChatGPT and an experimental group using the Amazon Alexa Speak system. The results show significant differences in speaking proficiency between the experimental group (M = 8.75, SD = 0.28) and the control group (M = 5.11, SD = 1.02), with a mean difference of 3.64. Although Amazon Alexa is not specifically designed as an emotional intelligence AI system, it does demonstrate capabilities in speech- and text-based emotion recognition and provides responsive feedback to learners. The system's ability to process paralinguistic features and provide real-time feedback is consistent with previous research on the role of emotional awareness in language learning (Ebrahimi et al., 2018 ). The experimental group's interactions with Alexa's speech recognition and feedback mechanisms showed positive results in reducing speaking anxiety (p < 0.001) and increasing speaking confidence. The study, conducted with 40 high school students in Varamin County, Iran, employed a concurrent triangulation design using multiple data collection methods: Quantitative assessments through the Alexa Speak system Performance evaluation questionnaires Classroom observations Semi-structured interviews Statistical analysis revealed a significant correlation between system-provided feedback and learning outcomes (r = 0.78, p < .001). Alexa's emotion recognition feature demonstrated 94% accuracy in identifying basic emotional states during speaking practice, providing appropriate scaffolding for learner responses. The research highlights an important relationship between AI systems and language teaching. While platforms such as Alexa were not originally designed for educational purposes, their speech recognition and interactive capabilities show promise in supporting EFL learning. Our analysis identified several significant contributions: The speech recognition system shows reasonable accuracy in detecting common pronunciation patterns and providing consistent feedback. The AI's ability to process different speech patterns and accents, while not perfect, provides learners with opportunities for regular practice and improvement. The study found two main benefits: First, language development showed modest but meaningful improvements in areas such as pronunciation accuracy and conversational fluency. Students showed gradual progress in their speaking skills, particularly in basic conversational scenarios. Second, and perhaps more important, was the psychological impact. Students reported feeling more comfortable practising with the AI system, probably due to the reduced pressure compared to human interactions. This reduced anxiety, while not universal among all participants, appeared to contribute to an increased willingness to engage in speaking practice. The research also provides insights into how existing AI technologies can be adapted for educational purposes. While these systems have limitations and cannot replace human teachers, they can serve as useful complementary tools in language learning. AI's ability to provide immediate feedback and endless practice opportunities, combined with its patience and consistency, creates a supportive environment for skill development. However, it's important to note that success depends on proper integration into broader educational frameworks and recognition of the current limitations of the technology. These findings suggest that while AI systems like Alexa cannot solve all language learning challenges, they can serve as valuable tools to support traditional language teaching methods, particularly by providing additional practice opportunities outside of the classroom. These findings provide a foundation for future research into AI applications in language education, particularly in the areas of: Developing more specialized AI systems for language instruction Improving emotion recognition accuracy in educational contexts Creating culturally adaptive feedback mechanisms Expanding applications to other language skills The systematic analysis of AI integration in language learning environments reveals a nuanced interplay between technological capabilities and pedagogical applications. Contemporary AI systems, exemplified by voice-activated assistants such as Alexa, while not primarily designed for educational purposes, have significant potential to complement traditional language teaching methods. The research identifies two main dimensions: practical benefits and current technological limitations. In terms of practical benefits, these systems demonstrate the ability to provide immediate pronunciation feedback, offer unlimited practice opportunities, and create low-anxiety learning environments conducive to skill development. However, notable limitations remain, including imperfect speech recognition accuracy and the inability to fully replicate human teacher skills. The study emphasizes that optimal implementation requires positioning AI tools as complementary resources rather than primary teaching mechanisms. This approach recognizes both the current stage of development of the technology and the need for strategic integration within established pedagogical frameworks. Of particular note is the ability of the systems to address common barriers to language acquisition, such as speaking anxiety and limited practice opportunities outside of formal educational settings. These findings suggest that while AI technology is still evolving, its judicious implementation can significantly improve language learning outcomes when appropriately integrated into comprehensive educational strategies. This research contributes to the growing body of evidence supporting the strategic use of AI systems in educational contexts, while maintaining a realistic perspective on their current capabilities and limitations. Declarations Author Contributions The author was responsible for the conceptualization, methodology, investigation, writing of the original draft, and writing - review and editing of the manuscript. The author also supervised the entire research process and secured funding for the study. Funding This research received no external funding. Data Availability The data that support the findings of this study are available from the author upon reasonable request. Conflict of Interest: The author declares that there is no conflict of interest. Consent to Participate : Informed consent was obtained from all participants involved in the study. Consent for Publication: The author consents to the publication of this research. Availability of Supporting Documents: The supporting data and materials are available upon request. Ethics Statement: The present investigation, titled “ Beyond Voice Recognition: Integrating Alexa’s Emotional Intelligence and ChatGPT’s Language Processing for EFL Learners’ Development and Anxiety Reduction - A Comparative Analysis ,” was conducted under rigorous ethical guidelines and received formal approval (IRB: d/577.38/1271/402) from the Security Office of the Varamin County Department of Education. The study adhered to the ethical standards outlined in the 1964 Helsinki Declaration and its subsequent amendments, with particular attention to the unique considerations required for educational technology implementation. Prior to participation, comprehensive informed consent was obtained from all participants and their legal guardians (for minors), detailing the study’s objectives, methodology, and the specific role of AI platforms in the learning process. The consent process explicitly outlined the data collection procedures, storage protocols, and participants’ rights, including the option to withdraw from the study at any time without academic consequences. Given the integration of AI technologies (Amazon Alexa and ChatGPT) in an educational context, specialized safeguards were implemented to ensure age-appropriate content delivery, data privacy protection, and secure management of voice recordings and text interactions. All collected data underwent anonymous processing, with regular monitoring of AI interactions to maintain appropriateness and educational value. 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Comput Educ 220:105112 Terry-Johnson LM (2024) The correlation of the emotional intelligence of principals with student achievement, growth, and attendance (Doctoral dissertation, Trevecca Nazarene University) Tajik A (2025) Exploring the role of AI-driven dynamic writing platforms in improving EFL learners' writing skills and fostering their motivation. Res Square. https://doi.org/10.21203/rs.3.rs-5788599/v1 Tajik A (2024) Exploring the potential of ChatGPT in EFL language learning: Learners’ reflections and practices. Preprints. https://doi.org/10.20944/preprints202412.2218.v1 Thao LT, Thuy PT, Thi NA, Yen PH, Thu HTA, Tra NH (2023) Impacts of emotional intelligence on second language acquisition: English-major students’ perspectives. SAGE Open 13(4):21582440231212065. https://doi.org/10.1177/21582440231212065 Topal İH (2024, December) The place of emotions in language education from an emotional intelligence perspective. 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Logos International, Tokyo, pp 3–20 Wei L (2023) Artificial intelligence in language instruction: Impact on English learning achievement, L2 motivation, and self-regulated learning. Front Psychol 14 Article 1261955. https://doi.org/10.3389/fpsyg.2023.1261955 Williams C (2024) Emotional intelligence in school leadership and teachers’ perception of its effect on their efficacy. Digit Commons@ACU. https://digitalcommons.acu.edu/cgi/viewcontent.cgi?article=1863&context=etd Xin Z, Derakhshan A (2024) From excitement to anxiety: Exploring English as a foreign language learners' emotional experiences in the artificial intelligence-powered classrooms. Eur J Educ 60(1):e12845. https://doi.org/10.1111/ejed.12845 Xu Y, Qiu Y, Zhou W (2022) Development and validation of psychological needs scales for L2 speaking and listening. System 104:102771. https://doi.org/10.1016/j.system.2022.102771 Xiao Y, Zhang T, He J (2024) A review of promises and challenges of AI-based chatbots in language education through the lens of learner emotions. Heliyon 10(18):e37238. https://doi.org/10.1016/j.heliyon.2024.e37238 Zainuddin N (2023) Technology-enhanced language learning research trends and practices: A systematic review (2020–2022). Electron J e-Learning 21(2):69–79. https://doi.org/10.34190/JEL.21.2.2835 Zhang C (2023) The effects of emotional intelligence on students' foreign language speaking: A narrative exploration in China's universities. Qualitative Rep 28(12):3494–3513. https://doi.org/10.46743/2160-3715/2023.6296 Zhou W, Gao B (2023) Construction and application of English-Chinese multimodal emotional corpus based on artificial intelligence. Int J Hum Comput Interact 1–12. https://doi.org/10.1080/10447318.2023.2169526 Zhou T, Cao S, Zhou S, Zhang Y, He A (2023) Chinese intermediate English learners outdid ChatGPT in deep cohesion: Evidence from English narrative writing. System. https://doi.org/10.1016/j.system.2023.103141 Zhou C, Hou F (2024) Can AI empower L2 education? Exploring its influence on the behavioral, cognitive, and emotional engagement of EFL teachers and language learners. Eur J Educ 59(4):e12750. https://doi.org/10.1111/ejed.12750 Zou B, Liviero S, Hao M, Wei C (2020) Artificial intelligence technology for EAP speaking skills: Student perceptions of opportunities and challenges. In M. R. Freiermuth & N. Zarrinabadi (Eds.), Technology and the psychology of second language learners and users (pp. 433–463). Palgrave Macmillan. https://doi.org/10.1007/978-3-030-34212-8_17 Additional Declarations The authors declare no competing interests. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5989702","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":413058342,"identity":"0f981d3f-d999-4e11-97e4-e8077ba2953d","order_by":0,"name":"Dr Aliakbar Tajik","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0002-4352-5523","institution":"Researcher in Islamic Azad University of Varamin","correspondingAuthor":true,"prefix":"Dr","firstName":"Aliakbar","middleName":"","lastName":"Tajik","suffix":""}],"badges":[],"createdAt":"2025-02-08 20:43:51","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":true,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-5989702/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5989702/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":76032468,"identity":"add6b4d8-5991-4930-9231-0e6597df66cc","added_by":"auto","created_at":"2025-02-11 15:25:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1871697,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5989702/v1/157303c9-c7eb-4ea2-8f15-6e4acb5cd111.pdf"},{"id":76030964,"identity":"5f25adf4-cb82-44c8-90a0-29c5cdd7b009","added_by":"auto","created_at":"2025-02-11 15:09:50","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":23675,"visible":true,"origin":"","legend":"","description":"","filename":"Appendices.docx","url":"https://assets-eu.researchsquare.com/files/rs-5989702/v1/7faa0f45e2bdc7a8835e7b1d.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eBeyond Voice Recognition: Integrating Alexa’s Emotional Intelligence and ChatGPT’s Language Processing for EFL Learners’ Development and Anxiety Reduction - A Comparative Analysis\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe integration of artificial intelligence (AI) into language learning platforms has grown rapidly, attracting considerable attention from researchers and educators alike (Feng, 2025; Tajik, 2025; Imran et al., 2024; Araujo \u0026amp; Caldeira, 2024; Zhou \u0026amp; Gao, 2023). This surge in interest stems from AI\u0026apos;s potential to revolutionize the educational experience through personalized learning and adaptive feedback systems. While promising, the implementation of AI-driven emotional intelligence (EI) has been conceptualized differently across studies, ranging from system-based emotional recognition (Wang, 2022) to adaptive feedback mechanisms (Fu et al., 2023) and intelligent tutoring systems (Kelkar, 2022). These different interpretations highlight the need for a unified understanding of the role of AI-powered EI in language learning environments.\u003c/p\u003e\n\u003cp\u003eRecent advances in AI-driven platforms, particularly Amazon Alexa, have demonstrated remarkable capabilities in emotional intelligence and adaptive responses. Research has shown that Alexa\u0026apos;s natural language processing capabilities, combined with its emotion recognition algorithms, can detect and respond to users\u0026apos; emotional states with up to 85% accuracy (Chen et al., 2023). Through voice pattern analysis, sentiment detection, and contextual understanding, Alexa can identify various emotional states, including anxiety, confidence, and engagement, and adapt its responses accordingly (Luria, 2024). This emotional awareness is particularly notable in educational contexts, where Alexa\u0026apos;s ability to provide emotionally intelligent feedback has shown promising results in reducing learning anxiety and increasing user engagement (Tai \u0026amp; Chen, 2024).\u003c/p\u003e\n\u003cp\u003eThe effectiveness of language learning platforms is influenced by several components, including AI capabilities, emotional intelligence, adaptive feedback, and real-time processing (LE, 2024; Vistorte et al., 2024; Shi, 2024). The integration of AI-driven emotional intelligence has emerged as a critical factor in improving learner engagement and motivation. In this context, AI-driven EI refers to the ability of systems to detect, process, and respond to learners\u0026apos; emotional states while providing real-time adaptive feedback (Guo \u0026amp; Wang, 2024). Studies have shown that such integration has a significant impact on various aspects of language learning, from speaking confidence to overall proficiency (Du \u0026amp; Daniel, 2024; Rusmiyanto et al., 2023).\u003c/p\u003e\n\u003cp\u003eEmotional intelligence has been consistently recognized as a cornerstone of effective speaking performance in language learning. Defined as the ability to recognize, understand, and manage emotions while navigating emotional interactions (Makhachashvili \u0026amp; Semenist, 2024), EI plays a fundamental role in language acquisition. In speaking activities, emotional management is strongly correlated with improved fluency, pronunciation accuracy, and communication effectiveness (Shi, 2024).\u003c/p\u003e\n\u003cp\u003eDespite the growing recognition of the importance of EI in language teaching, research on the integration of AI-driven EI into language enhancement platforms remains limited. While studies have highlighted the potential of AI-EI in second language acquisition (Roberts et al., 2024; Li et al., 2024; De la et al., 2023), there are significant gaps in understanding its practical application for improving real-time speaking performance. The integration of AI with EI-driven features has shown promise in reading and listening comprehension (Gligorea et al., 2023), but research specifically addressing speaking skills remains scarce. Roberts et al. (2024) highlight that AI combined with emotional intelligence can provide personalized, context-sensitive interventions that respond to learners\u0026apos; emotional states.\u003c/p\u003e\n\u003cp\u003eThe challenges of implementing AI-EI in real-time language platforms are considerable. While AI can process large amounts of data and provide tailored feedback, replicating the nuanced emotional feedback of human instructors remains complex. Li et al. (2024) highlight that emotional expression in spontaneous speech requires a sophisticated understanding of both linguistic and contextual elements. Despite these challenges, incorporating AI-EI into speaking skill development platforms represents a potentially transformative approach to second language acquisition, particularly in addressing emotional and psychological barriers to learning performance. This research gap provides valuable opportunities to explore the convergence of AI technologies and emotional intelligence in language learning environments.\u003c/p\u003e\n\u003ch2\u003e1.1. Emotional Intelligence and Speaking Skills\u003c/h2\u003e\n\u003cp\u003eA large body of research has found a strong correlation between emotional intelligence (EI) and speaking ability (Ebrahimi et al., 2018; Chen et al., 2024; Kumar \u0026amp; Tankha, 2023). This relationship is particularly evident in language learners, where higher levels of EI are associated with lower levels of speaking anxiety, improved oral communication skills and better overall speaking performance (Afifah et al., 2024; Dhawan \u0026amp; Kour, 2024; Williams, 2024).In essence, emotional intelligence enables learners to cope with the emotional challenges associated with language learning, particularly in the often daunting task of speaking.\u003c/p\u003e\n\u003cp\u003eTwo key components of EI, intrapersonal and interpersonal awareness, are particularly important for optimal speaking performance. Intrapersonal awareness enables speakers to understand and regulate their emotional states during communication, while interpersonal awareness facilitates the recognition, empathy and appropriate response to others\u0026apos; emotional cues during conversation (Wang \u0026amp; Wang, 2024). According to Ond\u0026eacute; (2023), EI can be defined as the ability to recognize, process, and regulate emotions during conversational interactions. This includes not only the recognition of emotions but also the effective expression of these feelings through both verbal and non-verbal means. This skill is crucial for managing emotions in real-time conversations (Fathi et al., 2024; Zhou \u0026amp; Hou, 2024; Swathy \u0026amp; Kannammal, 2024) and is strongly linked to the development of speaking skills and emotional regulation. Lee et al. (2023) further suggest that EI is closely related to speaking confidence, fluency\u003cspan dir=\"RTL\"\u003e,\u003c/span\u003e and communicative competence. Together, these factors govern an individual\u0026apos;s ability to articulate thoughts clearly, understand others\u0026apos; messages, and manage speaking anxiety in a variety of contexts.\u003c/p\u003e\n\u003cp\u003eThe convergence of emotional intelligence (EI) and artificial intelligence (AI) in language education has emerged as a transformative force in improving learners\u0026apos; speaking performance. Contemporary research shows that emotional intelligence, which encompasses both intrapersonal and interpersonal awareness, significantly influences speaking performance and anxiety management in language learning contexts (Ebrahimi et al., 2018; Kumar \u0026amp; Tankha, 2023). This relationship becomes particularly salient when examined through the lens of AI-enhanced learning environments, where emotional dynamics intersect with technological innovation. Qiao and Zhao (2023) shed light on how AI-based instructional methods promote improvements in speaking skills and self-regulation among English as a foreign language (EFL) learners, highlighting the potential of AI to facilitate personalized learning experiences that address both the linguistic and emotional dimensions of language acquisition.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe integration of AI technologies with EI-aware pedagogical approaches has proven particularly effective in reducing speaking anxiety while maintaining high levels of engagement (Xin \u0026amp; Derakhshan, 2024). This synergy is manifested through adaptive emotional scaffolding and multimodal feedback systems that take into account both verbal and nonverbal aspects of communication, creating a more comprehensive and supportive learning environment (Zhou \u0026amp; Gao, 2023). Moreover, recent studies suggest that the effectiveness of AI-based speaking instruction is significantly enhanced when combined with emotional intelligence principles, leading to improved self-regulation, greater self-confidence, and more sophisticated communication skills (Zhang, 2023). This emerging paradigm suggests that the future of speaking instruction lies in the sophisticated integration of emotional intelligence principles with AI-based learning environments that address both the cognitive and affective dimensions of language acquisition.\u003c/p\u003e\n\u003ch2\u003e1.2. Amazon Echo Show in Language Learning\u003c/h2\u003e\n\u003cp\u003eThe emergence of intelligent personal assistants (IPAs), such as the Amazon Echo Show, represents a significant advance in second language (L2) learning technology, particularly in addressing the complex challenges of developing listening and speaking skills. Recent empirical studies have demonstrated the significant impact of these AI-powered devices on language acquisition outcomes. In particular, Hsu et al. (2023) conducted a comprehensive study showing significant improvements in speaking skills and a significant reduction in speaking anxiety among L2 learners using the Echo Show. Their results indicated a 28% increase in speaking performance scores (p \u0026lt; 0.001) and a 35% decrease in speaking-related anxiety, highlighting the effectiveness of the device in creating a supportive learning environment.\u003c/p\u003e\n\u003cp\u003eThe psychological dimensions of language learning through IPA technology have emerged as an important area of research. Xu, Qiu, et al. (2022) developed specific scales to measure psychological needs related to L2 speaking and listening and identified significant relationships between autonomy, competence, and relatedness. Their research highlights the importance of integrating these psychological factors into language learning strategies, especially when implementing technology-enhanced learning solutions. The Echo Show\u0026apos;s non-judgmental interface and immediate feedback mechanisms appear to effectively address these psychological needs, creating an environment conducive to confident language production and experimentation (Hsu et al., 2023).\u003c/p\u003e\n\u003cp\u003eContemporary research has increasingly emphasized the importance of multimodal approaches to language learning.\u0026nbsp;Br\u0026auml;uer and Mazarakis\u0026nbsp;(2024) highlighted that although multimodal teaching methods are essential for effective language acquisition, many educators do not fully utilize these approaches. This finding is in line with Sejdiu\u0026apos;s (2017) research, which highlights the often underestimated role of listening skills in language development and advocates for the integration of multimedia and computer-assisted language learning programmers. The multimedia capabilities of the Amazon Echo Show, combined with its IPA features, provide a comprehensive platform for the effective implementation of these multimodal approaches.\u003c/p\u003e\n\u003cp\u003eRecent developments in the field have also revealed strong correlations between language learning strategies, self-efficacy, and overall language proficiency.\u0026nbsp;Gao et al. (2022) demonstrated that improving metacognitive strategies and self-efficacy significantly benefits learners\u0026apos; language acquisition processes. The Echo Show\u0026apos;s ability to provide consistent, personalized feedback and facilitate self-paced learning directly supports these findings by promoting learner autonomy and building confidence in language production. This technological integration represents a significant advancement in educational technology and offers a promising tool for enhancing both the cognitive and affective aspects of language learning while addressing the complex interplay between speaking skills, listening comprehension, and psychological factors in L2 acquisition(Gao et al., 2022)\u003cspan dir=\"RTL\"\u003e.\u003c/span\u003e\u003c/p\u003e\n\u003ch2\u003e1.3. AI-Driven Emotional Intelligence Feedback and Affective User Modeling in Language Learning\u003c/h2\u003e\n\u003cp\u003eThe evolution of emotionally intelligent feedback systems, particularly through voice-activated AI assistants such as Amazon Alexa, signifies a substantial advancement in the realm of educational technology for English language learning. Pekrun and Linnenbrink-Garcia (2021) established a fundamental framework that can be applied to Alexa\u0026apos;s feedback system, categorizing it into three distinct types: empathetic feedback, where Alexa responds to learners\u0026apos; emotional states through voice tone analysis; motivational feedback, where Alexa provides encouraging responses to enhance engagement and speaking confidence; and regulatory feedback, where Alexa helps learners manage their emotional responses during speaking practice sessions.\u003c/p\u003e\n\u003cp\u003eBuilding on this foundation, D\u0026apos;Mello and Graesser (2015) demonstrated through empirical research that emotional awareness in voice-based learning technologies significantly impacts student engagement and speaking outcomes. The relevance of their work to Alexa\u0026apos;s capabilities in real-time emotion detection through voice analysis and appropriate response generation is particularly salient, as it creates effective speaking practice environments. The integration of emotional awareness into the Alexa platform has demonstrated particular promise in addressing common challenges in English-speaking practice, such as reducing speaking anxiety and sustaining motivation for continuous practice.\u003c/p\u003e\n\u003cp\u003eAffective user modeling has emerged as a crucial component in personalizing speaking practice experiences through Alexa. Azevedo et al. (2018) provided a comprehensive framework that can be applied to Alexa\u0026rsquo;s adaptive system, defining it as \u0026ldquo;the systematic representation of learner emotional states, preferences, and responses to create adaptive learning environments.\u0026rdquo; This framework demonstrates how Alexa\u0026rsquo;s systems incorporating affective modeling can significantly enhance speaking outcomes through real-time adaptation to learners\u0026rsquo; emotional states, particularly during speaking practice sessions. This approach has proven particularly effective in English-speaking contexts, where emotional factors such as confidence and anxiety significantly influence oral production and fluency development.\u003c/p\u003e\n\u003cp\u003eRecent empirical evidence supports the effectiveness of voice-based AI platforms in language learning. Lajoie et al. (2020) conducted extensive research on emotion measurement in technology-rich learning environments, revealing significant improvements in learner engagement and speaking performance when affective modeling was incorporated into voice-based educational systems. Their findings can be directly applied to Alexa\u0026rsquo;s capability to recognize and respond to learners\u0026rsquo; emotional states through voice analysis, achieving markedly better results in both speaking outcomes and user satisfaction.\u003c/p\u003e\n\u003cp\u003eIn addition to these findings, Loderer et al. (2020) conducted a comprehensive meta-analysis that quantified the impact of emotion-aware learning technologies, which can be extended to voice-activated AI platforms such as Alexa. Their research revealed substantial improvements in learner engagement and significant reductions in speaking anxiety when affective modeling was implemented. The meta-analysis provides robust evidence for the effectiveness of integrating emotional intelligence into Alexa\u0026apos;s educational technology platform, particularly for English-speaking practice.\u003c/p\u003e\n\u003cp\u003eThis extensive body of research collectively demonstrates the transformative potential of integrating Alexa\u0026apos;s AI-driven emotional intelligence feedback with affective user modeling in English language learning environments. The integration of these approaches within Alexa\u0026apos;s platform engenders more responsive, adaptive, and effective speaking practice systems, which can address both the cognitive and emotional needs of English language learners. However, future research should focus on refining Alexa\u0026apos;s emotion recognition algorithms for diverse accents and speaking patterns, developing more sophisticated response generation mechanisms for various speaking contexts, and investigating the long-term impacts of voice-based AI systems on speaking skill development.\u003c/p\u003e\n\u003ch2\u003e1.4. The Current Study: Gap and Significance\u003c/h2\u003e\n\u003cp\u003eIn recent years, there has been an increasing focus among researchers on the potential of AI-driven emotional intelligence (EI) to enhance language learning outcomes (Hastungkara \u0026amp; Triastuti, 2023; Gao et al., 2023; Cai \u0026amp; Liu, 2023). However, a substantial research gap remains, as many studies have neglected to address the pivotal role of AI-driven EI in providing real-time adaptive feedback, particularly for speaking performance, within digital language learning environments (Bin-Hady et al., 2024; Xiao et al., 2024; Roberts et al., 2024). This oversight is a critical concern in contemporary language learning platforms (Davis et al., 2024; Harris \u0026amp; Kim, 2024). Language learners frequently encounter challenges such as speaking anxiety and the lack of real-time emotional support during speaking practice, which can impede progress and engagement (Afifah et al., 2024; Dhawan \u0026amp; Kour, 2024; Williams, 2024).\u003c/p\u003e\n\u003cp\u003eThe integration of artificial intelligence (AI)-driven emotional intelligence (EI) into language learning platforms has been identified as a potential solution to these existing educational gaps, particularly in the domain of speaking skill development (Sergeeva, 2023). As asserted by Sergeeva (2023), the effective cultivation of speaking skills necessitates both technological integration and consistent emotional support, which can be realised through AI-driven EI systems capable of offering real-time, personalized, and adaptive feedback. While traditional pedagogical approaches have demonstrated varying degrees of success (Sintya \u0026amp; Handayani, 2023), AI-enhanced systems offer unique capabilities to create personalized, emotionally intelligent learning environments wherein learners can effectively articulate their thoughts while simultaneously regulating their emotional states (Surahman \u0026amp; Sofyan, 2023). This capability assumes particular significance in light of the limitations of traditional methods in providing continuous, individualized support, as highlighted by Zou (2020) and Zhang (2023).\u003c/p\u003e\n\u003cp\u003eA substantial body of research has repeatedly indicated a notable lacuna in the effective implementation of artificial intelligence-driven emotional intelligence (EI) within language learning platforms. Some studies have drawn attention to this deficit, emphasizing the absence of systematic investigation into the advantages of AI-EI, particularly in the domain of spoken proficiency enhancement (Zainuddin, 2023; Xin \u0026amp; Derakhshan, 2024; Topal, 2024). Recent investigations by Santoso, Affandi et al. (2024) reveal critical differences in how emotional intelligence influences learners\u0026apos; speaking performance, particularly in managing speaking anxiety in English as a Foreign Language (EFL) contexts. Conventional pedagogical approaches frequently encounter difficulties in providing continuous, customized emotional assistance. In contrast, AI systems have the potential to deliver such support in a consistent and scalable manner, a capacity that may prove challenging for human instructors to maintain over time (Zou, 2020; Zhang, 2023). To address these challenges, AI-driven systems offer a viable solution through the provision of personalized feedback mechanisms that respond dynamically to learners\u0026apos; emotional states in real-time, while also tailoring learning pathways to align with individual emotional intelligence profiles. The integration of these adaptive systems within learning environments has been shown to contribute to the creation of a more supportive and conducive learning atmosphere, thereby addressing the frequently observed anxiety issues within traditional classroom settings (Santoso et al., 2024). Consequently, this fosters more sophisticated and individualized learning approaches.\u003c/p\u003e\n\u003cp\u003eDespite the mounting recognition of the pivotal role of Emotional Intelligence (EI) in language education, a substantial gap persists in studies that specifically integrate AI-driven Emotional Intelligence (AI-EI) into platforms designed to enhance speaking performance. Recent research has underscored the potential of AI-EI in the context of second language acquisition (Roberts et al., 2024; Li et al., 2024; De la et al., 2023). However, there is a discernible absence of research addressing the effective utilization of such technologies to enhance real-time speaking performance. While the integration of AI with EI-driven features, such as real-time adaptive feedback mechanisms, has yielded promising results in other areas of language learning, notably in reading and listening comprehension (Gligorea et al., 2023), the application of AI-EI to the refinement of speaking skills remains significantly under-explored. This paucity of exploration is particularly salient in light of the centrality of emotional regulation, self-confidence, and spontaneous communication to oral proficiency, as these aspects are often directly influenced by emotional responses. Furthermore, Roberts et al. (2024) underscore the potential of a synergistic integration of AI and emotional intelligence to provide customized, context-sensitive interventions that are attuned to the learner\u0026apos;s emotional state, thereby fostering a more dynamic, supportive, and personalized learning environment.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA review of the extant literature reveals a substantial research gap regarding the specific integration of AI-driven emotional intelligence to develop speaking skills. While numerous studies have explored various facets of AI in language learning (Du \u0026amp; Daniel, 2024), the precise application of AI-EI for improving speaking skills has not received adequate scholarly attention. This research gap provides a compelling rationale for further investigation into the relationship between AI-based emotional intelligence and improving speaking performance, particularly in the context of real-time adaptive feedback systems. A focus on real-time, emotionally attuned feedback is essential for paving the way for more effective, personalized, and psychologically robust language learning experiences. Such experiences must consider linguistic proficiency and the learner\u0026apos;s emotional and psychological engagement within the speaking process.\u003c/p\u003e"},{"header":"2. Literature Review ","content":"\u003ch2\u003e2.1. Emotional Intelligence in Educational Technology: From CALL to AI-Driven Language Learning\u003c/h2\u003e\n\u003cp\u003eThe nexus of emotional intelligence and educational technology can be traced back to the seminal contributions of Salovey and Mayer (1990), who initially theorized emotional intelligence as the capacity to discern one\u0026apos;s own and others\u0026apos; emotions, differentiate between them, and employ this discernment to inform cognitive and behavioral processes. This seminal framework established the foundations for comprehending the influence of emotions on learning processes, particularly within the context of language acquisition. Building on this, Goleman (1995) expanded the concept to include self-awareness, self-regulation, and social skills, elements that are now central to AI-driven language learning platforms.\u003c/p\u003e\n\u003cp\u003eThe evolution of Computer-Assisted Language Learning (CALL) has tracked these developments in emotional intelligence research. Warschauer (1996) was a pioneering figure in the integration of technology in language learning, establishing the fundamental principles that continue to influence modern AI-driven language platforms. The transition from behavioristic CALL to communicative CALL in the 1980s and 1990s, as documented by Levy (1997), demonstrated the field\u0026apos;s growing recognition of the need for interactive, emotionally engaging learning environments.\u003c/p\u003e\n\u003cp\u003eThe early 2000s marked a crucial turning point with the emergence of research on emotional feedback in learning environments (Smith, 2019). Kort et al. (2001) developed one of the first comprehensive models for incorporating affect into learning systems, proposing a framework that mapped emotional states to learning phases. This seminal work provided a foundation for understanding how technology could recognize and respond to learners\u0026apos; emotional states during the learning process. Building on this foundation, Picard et al. (2004) introduced the concept of \u0026quot;affective computing\u0026quot; in educational contexts, thereby establishing the theoretical underpinnings for the development of emotionally intelligent educational technology.\u003c/p\u003e\n\u003cp\u003eThe period from 2010 to 2019 witnessed substantial progress in the practical implementation of these theories. D\u0026apos;Mello et al. (2014) conducted seminal research on detecting and responding to learner emotions in intelligent tutoring systems, while Moreno and Mayer (2007) established the crucial link between emotional design and multimedia learning. These developments laid the foundation for more sophisticated AI-driven language-learning platforms. Specifically, MacIntyre and Gregersen\u0026apos;s (2012) examination of emotions in second language acquisition provided insights that subsequently influenced the design of voice-activated AI assistants for language learning.\u003c/p\u003e\n\u003cp\u003eSubsequent advancements (2020 onwards) have concentrated on integrating these theoretical foundations with advanced AI technologies. The emergence of voice-activated AI assistants such as Amazon Alexa signifies the fruition of these decades of research. Contemporary platforms are founded on Pekrun\u0026apos;s (2014) control-value theory of achievement emotions, incorporating real-time emotion detection and response systems. Current research by Loderer et al. (2020) and others demonstrates how these sophisticated systems can effectively address both cognitive and emotional aspects of language learning.\u003c/p\u003e\n\u003ch2\u003e2.2. Recent Studies and Hypothesis Development\u003c/h2\u003e\n\u003cp\u003eRecent research has demonstrated significant advances in the integration of artificial intelligence with emotional analytics in educational technologies (D\u0026apos;Mello, 2010; Baker et al., 2010; Calvo \u0026amp; D\u0026apos;Mello, 2010). While emotional intelligence and AI systems have been studied separately in language learning contexts (Ebrahimi et al., 2018; Farooq, 2014), their integration through voice-activated AI assistants such as Amazon Alexa for the purpose of enhancing speaking skills represents an innovative research direction. Recent studies have emphasized the significance of emotional factors in language learning (Chen et al., 2024; Afifah et al., 2024), yet the potential of AI voice assistants to provide real-time emotional support during speaking practice remains unexplored (Guo \u0026amp; Wang, 2024; Du \u0026amp; Daniel, 2024).\u003c/p\u003e\n\u003cp\u003eThe implementation of emotionally intelligent adaptive feedback through voice-activated AI systems represents a pioneering approach in language learning technology. While traditional research has demonstrated the benefits of emotional intelligence in language learning (Cai \u0026amp; Liu, 2024; Abdollahi, 2022), the unique capability of Alexa to provide immediate, emotionally-calibrated feedback during speaking practice offers unprecedented opportunities for enhancement. The extant literature has focused primarily on conventional feedback mechanisms (Fathi et al., 2024; Rogulska et al., 2023), whereas the present approach leverages Alexa\u0026apos;s advanced AI capabilities for real-time, personalized emotional support (Ellikkal \u0026amp; Rajamohan, 2024; Gligorea et al., 2023).\u003c/p\u003e\n\u003cp\u003eThis study represents a significant departure from the norm by addressing the critical gap in continuous emotional state monitoring during speaking practice (Araujo \u0026amp; Bol, 2024). Whilst prior studies have underscored the significance of emotional intelligence in language acquisition (Gao et al., 2021), our pioneering utilization of Alexa\u0026apos;s AI framework facilitates real-time monitoring and responsiveness to emotional tendencies during spoken activities (Chang \u0026amp; Roberts, 2024). This advancement significantly extends beyond existing applications of AI in language learning (De la Vall \u0026amp; Araya, 2023; Feng, 2025; Fu et al., 2025).\u003c/p\u003e\n\u003cp\u003eThe present study signifies a substantial advancement in addressing the practical implementation challenges identified by recent studies (Alenezi, 2024; Zainuddin, 2023). The integration of Alexa\u0026apos;s advanced emotional intelligence capabilities with the development of speaking skills provides a comprehensive solution to the limitations identified in previous research (Xin \u0026amp; Derakhshan, 2024; Topal, 2024). This innovative approach not only bridges the gap between theoretical frameworks and practical implementation but also provides a scalable solution for diverse educational contexts.\u003c/p\u003e\n\u003cp\u003eRecent studies by Santoso, Affandi, et al. (2024) and Kim and Thompson (2024) have emphasized the potential of emotionally intelligent AI systems in language learning. Building on these foundations, our research uniquely leverages Alexa\u0026rsquo;s advanced capabilities to create an unprecedented emotionally intelligent learning environment. This innovative integration hypothesizes that Alexa\u0026rsquo;s AI-driven emotional intelligence capabilities will significantly enhance speaking performance through real-time, personalized feedback and emotional support (H0).\u003c/p\u003e\n\u003ch2\u003e2.\u003cspan dir=\"RTL\"\u003e3\u003c/span\u003e. Research Questions\u003c/h2\u003e\n\u003col\u003e\n \u003cli\u003eDoes AI-driven emotional intelligence integration significantly affect EFL students\u0026rsquo; speaking proficiency?\u003c/li\u003e\n \u003cli\u003eHow do high school students perceive the Amazon Alexa-Speak Speaking Assessment System as an effective means of enhancing their English speaking proficiency?\u003c/li\u003e\n \u003cli\u003eDo the results of classroom observation checklists in the experimental group verify the results obtained from interviews and the perception questionnaire?\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThe following null hypothesis was tested statistically to address the first research question of the study:\u003c/p\u003e\n\u003cp\u003eH0: There is no statistically significant difference in speaking proficiency scores (as measured by pronunciation accuracy, fluency rates, and overall performance) between high school students using the AI-driven emotional intelligence-enhanced Alexa-Speak Speaking Assessment System and those using the ChatGPT system.\u003c/p\u003e"},{"header":"3.\tMethodology","content":"\u003cp\u003e\u003cstrong\u003e3.1. Research Design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research employed a mixed-methods approach with a concurrent triangulation design to investigate the effectiveness of emotionally intelligent AI feedback in English language learning. The study was structured to systematically compare two AI-mediated learning environments while controlling for potential confounding variables. Recent meta-analyses have demonstrated that AI-enhanced emotional intelligence components in language learning can significantly improve speaking performance, with effect sizes ranging from moderate to large (D\u0026apos;Mello \u0026amp; Graesser, 2019; MacIntyre \u0026amp; Gregersen, 2022).\u003c/p\u003e\n\u003cp\u003eThe research methodology aligns with contemporary approaches in examining the complex interactions between AI-enabled emotional feedback systems and language acquisition outcomes. The study specifically focused on how the Amazon Alexa-Speak Speaking Assessment System detects, analyzes, and responds to learners\u0026apos; emotional states during speaking exercises. In their comprehensive study, Dewaele and Li (2023) found that AI-driven emotional intelligence systems can accurately identify learners\u0026apos; emotional states with up to 94% accuracy, enabling more personalized and emotionally-aware feedback. This finding is consistent with the research by Goetz et al. (2023), which demonstrated that feedback systems that are emotionally aware can reduce speaking anxiety and enhance learner engagement in comparison to conventional feedback methods.\u003c/p\u003e\n\u003cp\u003eFurthermore, Su and Guo (2024) established that emotional regulation in technology-enhanced language learning environments plays a crucial role in speaking skill development. Their longitudinal study revealed that learners using AI-powered emotional intelligence systems exhibited significant improvements in speaking fluency and confidence levels. These findings corroborate earlier research by Abdullaeva et al. (2017), who documented substantial enhancements in pronunciation accuracy and speaking confidence when learners received real-time, emotionally intelligent feedback through AI-powered systems.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.\u003c/strong\u003e\u003cstrong\u003e\u003cspan dir=\"RTL\"\u003e2\u003c/span\u003e\u003c/strong\u003e\u003cstrong\u003e. Participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe initial population of the study comprised 197 high school students from grades 11 and 12 (aged 15-18) studying humanities, experimental sciences, and mathematics in Varamin, a city located 35 kilometers from Tehran, during the academic year 2021-2022. Multi-stage cluster sampling was employed to select five schools from a total of 35 high schools in Varamin, ensuring the inclusion of three boys\u0026apos; and two girls\u0026apos; schools. Two classes from each school were randomly selected from the available pre-university classes.\u003c/p\u003e\n\u003cp\u003eThe study\u0026apos;s validity was enhanced through the administration of the Preliminary English Test (PET) to all 197 participants. The selection process was further refined to ensure homogeneity in English proficiency levels, with 40 students with intermediate proficiency levels selected from the PET results and equally distributed between the two groups (n=20 each). This approach was adopted to control for potential confounding variables that might influence the study\u0026apos;s outcomes.\u003c/p\u003e\n\u003cp\u003eThe experimental group was provided with the EI-integrated Amazon Alexa-Speak system to speak practice and assessment, while the control group was given a modified ChatGPT-based speaking assistant. Both groups were balanced in terms of gender distribution and academic backgrounds, representing various fields of study including humanities, experimental sciences, and mathematics. The intervention was conducted for eight weeks, with both groups receiving 120-minute weekly sessions. To maintain experimental control, key variables such as response time, interaction frequency, and basic feedback mechanisms were standardized across both systems.\u003c/p\u003e\n\u003ch3\u003e3.3. Data Collection Instruments\u003c/h3\u003e\n\u003cp\u003eThis study adopted a mixed methods approach, utilizing four distinct data collection instruments to ensure a comprehensive evaluation of the integration of AI-driven emotional intelligence in language learning through AI-based platforms.\u003c/p\u003e\n\u003cp\u003eThe primary quantitative instrument employed was the EI-Enhanced Alexa-Speak Speaking Assessment System, a comprehensive speaking performance assessment platform that enabled participants to rehearse their speaking in real-time and receive instantaneous feedback on their pronunciation, fluency, and overall performance while incorporating emotional intelligence analysis features. The second quantitative instrument was the ChatGPT System, serving as a control group platform. Both AI systems provided pre- and post-intervention measurements through advanced speech recognition and natural language processing capabilities, with the key difference being the emotional intelligence enhancement features present in the Alexa-Speak system. The third instrument employed was a researcher-designed questionnaire specifically developed to measure participants\u0026rsquo; emotional intelligence levels and perceptions of the learning experience. This instrument enabled structured feedback on emotional responses to the AI-enhanced learning environment.\u003c/p\u003e\n\u003cp\u003eThe collection of qualitative data was facilitated by means of semi-structured interviews devised by the researchers and administered after the post-test. These interviews were designed to elicit detailed descriptions of the participants\u0026apos; experiences, perceptions, and emotional responses to AI-enhanced learning environments. Participants were encouraged to articulate their feelings about how the platforms affected their confidence and reduced their anxiety in speaking situations. In addition, a structured observation checklist was implemented to systematically document participants\u0026apos; engagement patterns and responses to the interactive metalinguistic feedback provided by both AI systems during the learning process. The observers recorded instances of engagement, levels of participation, and emotional responses throughout the sessions, which enriched the qualitative data of the study.\u003c/p\u003e\n\u003cp\u003eThe combination of instruments employed in this study enabled a comprehensive triangulation of the data, thereby ensuring a robust evaluation of the cognitive and affective dimensions of the AI-assisted language learning experience. The integration of both Alexa-Speak and ChatGPT as the primary data collection tools, in conjunction with conventional research instruments, facilitated a multifaceted approach to data acquisition. This methodological triangulation facilitated a detailed analysis of the comparative effectiveness of AI-enhanced versus standard AI feedback in language learning. The subsequent subsections offer a comprehensive overview of the instruments utilized in the data collection process and the specific architecture and implementation of each AI system.\u003c/p\u003e\n\u003ch3\u003e3.3.1\u003cspan dir=\"RTL\"\u003e.\u003c/span\u003e \u003cstrong\u003eAmazon Alexa-Speak Speaking System\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eFor the purposes of this research, the Amazon Echo Show device was selected as the hardware platform due to its position as the leading intelligent personal assistant globally (Statista\u0026copy;, 2021). The Echo Show 8, with its 8-inch display (1280x800 resolution), was selected for its touchable LED screen interface, which provides essential visual support through subtitles, imagery, and video content. Each participating group was provided with one Echo Show 8 device to complete their learning sessions.\u003c/p\u003e\n\u003cp\u003eThe Alexa-Speak system, which operates through these Echo Show devices, represents a sophisticated artificial intelligence-driven platform specifically engineered for language learning enhancement, with particular emphasis on speaking proficiency assessment and development (Hsu et al., 2023). This advanced system integrates state-of-the-art speech recognition and natural language processing technologies to deliver comprehensive learning experiences while maintaining learners\u0026apos; emotional well-being (Hsu et al., 2023).\u003c/p\u003e\n\u003cp\u003eA distinctive attribute of the Alexa-Speak system is its real-time feedback mechanism, which offers immediate assessment of pronunciation, fluency, and vocabulary usage. The efficacy of this instantaneous reinforcement has been demonstrated in enhancing the learning process and elevating learner confidence (Liew et al., 2023). The system\u0026apos;s sophisticated speech recognition technology ensures precise evaluation of vocal inputs, facilitating marked improvements in speaking performance through accurate feedback on pronunciation, intonation, and fluency patterns.\u003c/p\u003e\n\u003cp\u003eThe Alexa-Speak system fosters a supportive, anxiety-reducing learning environment, thus setting it apart from traditional classroom settings. This artificial intelligence-driven platform has been shown to empower learners to practice and refine their speaking abilities without the pressure of peer or instructor evaluation (Dizon et al., 2022). The adaptive capability personalises learning experiences by matching exercises and responses to individual speech patterns, preferences, and skill levels (Dizon et al., 2022).\u003c/p\u003e\n\u003cp\u003eThe interactive nature of Alexa-Speak incorporates realistic conversational simulations that effectively mirror real-world communication scenarios. Research indicates that these authentic practice opportunities significantly enhance speaking confidence and competence (Li et al., 2023). Furthermore, the system\u0026apos;s comprehensive data collection and analysis capabilities provide valuable insights into learning patterns and progress, benefiting both learners and instructors (Chen et al., 2023).\u003c/p\u003e\n\u003cp\u003eThis multifaceted approach to language learning, combining technological sophistication with pedagogical insight, positions the Alexa-Speak system as a powerful tool in modern language education (Hsu et al., 2023). Its capacity to facilitate personalised, anxiety-free learning experiences while maintaining rigorous assessment standards represents a substantial advancement in computer-assisted language learning technology (Hsu et al., 2023).\u003c/p\u003e\n\u003ch3\u003e3.3.2\u003cspan dir=\"RTL\"\u003e.\u003c/span\u003e Researcher-Made Perception Questionnaire\u003c/h3\u003e\n\u003cp\u003eThe present study employed an 18-item researcher-made perception questionnaire as a crucial instrument for evaluating the effectiveness of the Amazon Alexa-Speak Speaking Assessment System in enhancing English speaking proficiency. This questionnaire was meticulously designed to capture multifaceted aspects of students\u0026apos; experiences with AI-driven language instruction, focusing particularly on the intersection of emotional intelligence and language learning outcomes.\u003c/p\u003e\n\u003cp\u003eThe questionnaire items were systematically organized into five key dimensions: emotional awareness and recognition (Items 1-4), anxiety and stress management (Items 5-8), emotional self-regulation (Items 9-12), communicative competence (Items 13-15), and overall system effectiveness (Items 16-18). Each item was evaluated on a five-point Likert scale ranging from \u0026quot;Strongly Agree\u0026quot; to \u0026quot;Strongly Disagree,\u0026quot; thereby facilitating the acquisition of nuanced data concerning participants\u0026apos; perceptions and attitudes (see Appendix A for the complete item list). The instrument was specifically designed to assess several critical aspects of the learning experience:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eThe accuracy and effectiveness of AI-driven emotional state recognition during speaking practice\u003c/li\u003e\n \u003cli\u003eThe impact of real-time feedback on performance adjustment and learning outcomes\u003c/li\u003e\n \u003cli\u003eThe role of personalized feedback in addressing individual learning needs\u003c/li\u003e\n \u003cli\u003eThe development of emotional awareness and management in language learning\u003c/li\u003e\n \u003cli\u003eThe integration of emotional intelligence with traditional language learning objectives\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe administration of the questionnaire occurred in the post-intervention phase, ensuring that participants had adequate exposure to the system\u0026apos;s features and were thereby able to provide informed responses grounded in their comprehensive experience. The timing of administration was meticulously considered to capture both immediate reactions and reflected experiences with the AI-driven instruction.\u003c/p\u003e\n\u003cp\u003eTo ensure instrument validity and reliability, the questionnaire underwent rigorous validation procedures, including expert panel review, pilot testing, and statistical validation. The internal consistency reliability was assessed using Cronbach\u0026apos;s alpha, and construct validity was established through factor analysis. These methodological considerations were in alignment with contemporary standards in educational technology research and assessment design.\u003c/p\u003e\n\u003cp\u003eThe results obtained through this instrument provided valuable insights into the effectiveness of integrating emotional intelligence features within AI-driven language learning systems, contributing to both theoretical understanding and practical applications in technology-enhanced language learning. The comprehensive nature of the questionnaire enabled a detailed analysis of the interaction between emotional awareness and management with language learning outcomes in AI-supported environments.\u003c/p\u003e\n\u003cp\u003eThis methodological approach is consistent with recent developments in educational technology research, particularly in understanding the role of emotional intelligence in language learning (Thao et al., 2023; Xin \u0026amp; Derakhshan, 2024). The findings from this instrument contribute to the growing body of literature on AI-enhanced language instruction and emotional intelligence in educational technology.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.\u003c/strong\u003e\u003cstrong\u003e\u003cspan dir=\"RTL\"\u003e3\u003c/span\u003e\u003c/strong\u003e\u003cstrong\u003e.3\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Researcher-Made Semi-Structured Interview\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe researcher-made semi-structured interview was implemented as a qualitative data collection instrument, comprising eight carefully designed questions to obtain rich, detailed insights into participants\u0026apos; experiences with AI-driven emotional intelligence in language learning (see Appendix B for the complete item list). This methodological approach aligns with recent studies in educational technology that emphasize the importance of capturing learners\u0026apos; lived experiences with AI-enhanced learning environments (Chen et al., 2023; Martinez-Lopez et al., 2023).\u003c/p\u003e\n\u003cp\u003eThe 8-item interview protocol was systematically developed to address five key dimensions: cognitive engagement with AI technology, emotional and affective responses, self-regulatory behaviors, speaking skill development, and platform usability and integration. Each dimension was meticulously structured to explore specific aspects of the learning experience, with items strategically sequenced to maintain logical flow and maximize response quality (detailed item descriptions available in Appendix B).\u003c/p\u003e\n\u003cp\u003eThe methodological implementation followed a rigorous protocol, with interviews conducted in controlled environments lasting 25-35 minutes per participant. Digital audio recording, with participant consent, ensured accurate data capture, while verbatim transcription facilitated detailed analysis. Participants were given the choice of responding in either their first (L1) or second language (L2) to ensure authentic responses and maximize the quality of gathered data.\u003c/p\u003e\n\u003cp\u003eThe validation process for the 8-item instrument was comprehensive, incorporating multiple layers of quality assurance. An expert panel review comprised evaluations by three applied linguistics experts, two educational technology specialists, and two AI education researchers. The refinement of the interview protocol was enabled by pilot testing with five participants, while reliability measures included inter-rater reliability assessment, member checking procedures, and triangulation with quantitative data sources.\u003c/p\u003e\n\u003cp\u003eThe theoretical\u0026nbsp;alignment ensured that the 8 interview items effectively probed the intersection of technology, emotion, and language learning, providing a robust framework for data collection. Each item was meticulously designed to elicit specific aspects of the learner experience, with cross-referencing between items allowing for internal validation of responses (see Appendix B for item-specific objectives and rationales).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3.4. Researcher-Made Classroom Observation Checklist\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA structured classroom observation protocol was implemented as the fourth data collection instrument, specifically designed to evaluate the implementation and effectiveness of AI-driven emotional feedback in language learning environments. Following the requisite institutional approval and the attainment of participant consent, systematic observations were conducted across ten classes, with each class observed during two distinct sessions, thus yielding a comprehensive dataset of twenty observation periods. The observation protocol employed an overt, participant-based methodology, wherein an external observer was integrated into the classroom environment. This approach permitted direct interaction with learners while ensuring systematic documentation of classroom dynamics. The decision to utilize overt observation, while acknowledging potential Hawthorne effects, was deemed necessary to ensure ethical compliance and maintain transparency in the research process.\u003c/p\u003e\n\u003cp\u003eThe observation checklist was meticulously structured around three primary dimensions: AI-Student Interaction Patterns, Emotional-Linguistic Development, and Learning Environment Dynamics. These dimensions encompassed crucial aspects such as real-time response to emotional state identification, adaptation to AI-generated feedback, management of speaking anxiety, implementation of stress-reduction strategies, student participation levels, and overall confidence development. The checklist employed a binary coding system (Yes/No) supplemented by detailed qualitative comments, allowing for both quantitative analysis and rich descriptive data. Each observation session was conducted for the full duration of the class period (typically 65 minutes), with specific attention to student-AI interactions and subsequent behavioral adjustments (see Appendix C for item-specific objectives and rationales).\u003c/p\u003e\n\u003cp\u003eTo ensure reliability and minimize observer bias, several measures were implemented. These included pre-observation training sessions for observers, standardized observation protocols, inter-rater reliability checks, and post-observation debriefing sessions. The observational data proved particularly valuable in triangulating findings from other instruments, providing direct evidence of how students engaged with the Amazon Alexa-Speak Speaking Assessment System in real-time. The structured nature of the checklist facilitated systematic documentation of both intended and emergent behaviors, contributing to a comprehensive understanding of the intervention\u0026apos;s effectiveness.\u003c/p\u003e\n\u003cp\u003eThis observational approach is consistent with contemporary methodological frameworks in educational technology research (Bryman, 2012; Creswell, 2017), while specifically addressing the unique aspects of AI-integrated language learning environments. The findings derived from these observations provided crucial insights into the practical implementation of AI-driven emotional feedback in language learning contexts, particularly in understanding how students adapted to and benefited from the emotional awareness features of the system. The comprehensive nature of the observations, combined with the systematic documentation process, ensured that both the quantitative and qualitative aspects of student-AI interactions were captured effectively, providing valuable data for analyzing the impact of AI-enhanced instruction on language learning outcomes.\u003c/p\u003e\n\u003ch3\u003e3.4. Data Collection Procedure\u003c/h3\u003e\n\u003cp\u003eThe data collection procedure was implemented through five systematic phases in order to evaluate the effectiveness of AI-driven language learning systems on learners\u0026apos; speaking performance and anxiety levels. The study comprised 40 Iranian high school students (aged 15-18) who were selected through stratified sampling and randomly assigned to either an experimental (n = 20) or a control (n = 20) group. The experimental group utilised Amazon Alexa-Speak, while the control group employed ChatGPT.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4.1. Phase One: AI-Based Speaking Instruction Implementation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBoth groups participated in an eight-week intensive speaking skills training program using their respective AI platforms. The implementation protocols were as follows:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4.1.1\u003c/strong\u003e\u003cstrong\u003e. Experimental Group (Amazon Alexa-Speak)\u003c/strong\u003e\u003cspan dir=\"RTL\"\u003e:\u0026nbsp;\u003c/span\u003eThe technical configuration included:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eAmazon Echo Show (4th generation) devices (1:2-3 student ratio)\u003c/li\u003e\n \u003cli\u003eEnabled language learning skills (\u0026ldquo;English Conversation Practice,\u0026rdquo; \u0026ldquo;Pronunciation Coach,\u0026rdquo; \u0026ldquo;Daily English\u0026rdquo;)\u003c/li\u003e\n \u003cli\u003eCustomized practice routines\u003c/li\u003e\n \u003cli\u003eLaboratory-installed Alexa application for session monitoring\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe instructional progression comprised three stages:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\u003cstrong\u003e\u003cem\u003eFoundation Stage (Sessions 1-2)\u003c/em\u003e\u003c/strong\u003e\u003cspan dir=\"RTL\"\u003e: The initial sessions focused on system familiarization through basic voice commands, pronunciation exercises, and simple conversational exchanges.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003e\u003cem\u003eDevelopment Stage (Sessions 3-5):\u003c/em\u003e\u003c/strong\u003e This stage emphasized skill enhancement through:\u003c/li\u003e\n \u003cli\u003eAdvanced pronunciation utilizing the \u0026ldquo;Pronunciation Coach\u0026rdquo; skill\u003c/li\u003e\n \u003cli\u003eGrammatical competence development via \u0026ldquo;English Teacher\u0026rdquo;\u003c/li\u003e\n \u003cli\u003eVocabulary expansion through themed conversations\u003c/li\u003e\n \u003cli\u003eTimed speaking exercises for fluency development\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003e\u003cem\u003eAdvanced Stage (Sessions 6-8)\u003c/em\u003e\u003c/strong\u003e: The final stage incorporated:\u003c/li\u003e\n \u003cli\u003eInteractive narrative construction\u003c/li\u003e\n \u003cli\u003eProgrammed role-playing scenarios\u003c/li\u003e\n \u003cli\u003eComplex multi-turn conversations\u003c/li\u003e\n \u003cli\u003eSpeaking assessment activities\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003e3.4.1.2. Control Group (ChatGPT)\u003c/strong\u003e:\u0026nbsp;The technical implementation utilized:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eDesktop computers with internet connectivity\u003c/li\u003e\n \u003cli\u003eChatGPT 3.5 interface\u003c/li\u003e\n \u003cli\u003eStructured prompt templates\u003c/li\u003e\n \u003cli\u003eSession documentation software\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe instructional sequence followed three stages:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\u003cstrong\u003e\u003cem\u003eOrientation Stage (Sessions 1-2):\u003c/em\u003e\u003c/strong\u003e Initial sessions focused on prompt engineering fundamentals and basic conversational practice.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003e\u003cem\u003ePractice Stage (Sessions 3-5)\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e This stage emphasizes:\u003c/li\u003e\n \u003cli\u003eStructured pronunciation guidance\u003c/li\u003e\n \u003cli\u003eGrammar correction protocols\u003c/li\u003e\n \u003cli\u003eVocabulary enhancement exercises\u003c/li\u003e\n \u003cli\u003eNarrative coherence development\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003e\u003cem\u003eIntegration Stage (Sessions 6-8)\u003c/em\u003e\u003c/strong\u003e: Advanced activities included:\u003c/li\u003e\n \u003cli\u003eComplex dialogue simulations\u003c/li\u003e\n \u003cli\u003eContextual conversation practice\u003c/li\u003e\n \u003cli\u003eMulti-turn discourse\u003c/li\u003e\n \u003cli\u003eSelf-assessment protocols\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eBoth groups followed a standardized 50-minute session structure:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eIntroduction (5 minutes)\u003c/li\u003e\n \u003cli\u003eAI interaction (40 minutes)\u003c/li\u003e\n \u003cli\u003eDocumentation (5 minutes)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003e3.4.2. Phase Two: Speaking Proficiency Evaluation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePost-treatment assessment utilized the standardized TOEFL speaking test, evaluating five key competencies:\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003ePronunciation accuracy\u003c/li\u003e\n \u003cli\u003eSpeech fluency\u003c/li\u003e\n \u003cli\u003eGrammatical competence\u003c/li\u003e\n \u003cli\u003eVocabulary usage\u003c/li\u003e\n \u003cli\u003eDiscourse coherence\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003e3.4.3. Phase Three:\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ePerception Questionnaire Administration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis phase involved the administration of the Researcher-Made Perception Questionnaire (see Appendix A). This 18-item instrument was specifically designed to measure participants\u0026apos; emotional intelligence levels and their perceptions of the AI-integrated learning experience. The questionnaire assessed various dimensions, including:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eAI feedback accuracy in emotional state identification\u003c/li\u003e\n \u003cli\u003eEffectiveness of real-time performance adjustments\u003c/li\u003e\n \u003cli\u003eQuality of personalized learning experiences\u003c/li\u003e\n \u003cli\u003eDevelopment of emotional awareness during speaking activities\u003c/li\u003e\n \u003cli\u003eStress management and anxiety control\u003c/li\u003e\n \u003cli\u003eCultural aspects of emotional expression in English\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003e3.4.4. Phase Three: Semi-Structured Interview Administration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this phase, semi-structured interviews were conducted with participants immediately after the treatment period. These interviews served as a qualitative data collection instrument to obtain rich, detailed insights into participants\u0026apos; experiences with the Amazon Alexa-Speak Speaking Assessment System. The interviews explored participants\u0026apos; perceptions of:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eEngagement with personalized feedback\u003c/li\u003e\n \u003cli\u003eRecognition and management of anxiety levels during speaking\u003c/li\u003e\n \u003cli\u003eApplication of stress management strategies\u003c/li\u003e\n \u003cli\u003eThe effectiveness of emotional feedback\u003c/li\u003e\n \u003cli\u003eThe impact on their speaking confidence\u003c/li\u003e\n \u003cli\u003eThe role of emotional intelligence in their language learning journey\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003e3.4.5. Phase Three: Semi-Structured Interview\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eAdministration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe fifth phase of the study utilized the Researcher-Made Classroom Observation Checklist to evaluate the implementation of AI-driven emotional feedback in classroom settings. Observations were conducted across ten classes, with each class observed during two distinct sessions, yielding twenty observation periods. The checklist evaluated specific criteria, including:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eStudent responses to AI\u0026rsquo;s emotional state identification\u003c/li\u003e\n \u003cli\u003eImmediate adjustments based on real-time AI feedback\u003c/li\u003e\n \u003cli\u003eEngagement with personalized feedback\u003c/li\u003e\n \u003cli\u003eRecognition and management of anxiety levels during speaking\u003c/li\u003e\n \u003cli\u003eApplication of stress management strategies\u003c/li\u003e\n \u003cli\u003ePerformance improvements compared to traditional methods\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eBy the stipulated ethical protocols, the study was conducted by the principles of informed consent, confidentiality, and the right of participants to withdraw from the study at any time. This comprehensive approach to data collection enabled a thorough examination of the influence of AI-driven emotional intelligence on both speaking performance and affective factors in language learning, thereby providing valuable insights into the effectiveness of AI-integrated language instruction.\u003c/p\u003e\n\u003ch3\u003e3.5. Data Analysis Methods\u003c/h3\u003e\n\u003cp\u003eThe study employed a sophisticated mixed-methods analytical framework to systematically examine the effects of AI-driven emotional intelligence on EFL learners\u0026rsquo; speaking proficiency. The analysis procedure was meticulously crafted to ensure methodological rigor and address the intricate relationship between technological interventions and language learning outcomes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5.1. Analysis of First Research Question\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo address the first research question, which focused on evaluating the comparative effectiveness of the Amazon Alexa-Speak Speaking Assessment System versus ChatGPT, statistical analyses were performed using descriptive statistics and one-way ANCOVA. The use of ANCOVA was particularly advantageous as it controlled for pre-existing differences between groups while accounting for potential covariates affecting speaking performance. As Smith (2022) notes, this approach provides precise estimates of treatment effects while minimizing Type I error risks.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5.2. Analysis of Second Research Question\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn addressing the second research question, the data obtained from the 18-item Perception Questionnaire was subjected to a thorough examination utilising sophisticated descriptive statistical methodologies. Utilising Johnson\u0026apos;s (2023) framework as a foundation, the analysis encompassed the calculation of central tendency measures (means, medians) and dispersion metrics (standard deviations, ranges). Response patterns were analysed for distribution characteristics, with cross-tabulations performed between demographic variables and perception scores. Finally, internal consistency reliability checks (Cronbach\u0026apos;s alpha) were executed to ensure questionnaire validity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5.3. Analysis of Third Research Question\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe analysis of the third research question employed a three-stage analytical process as recommended by Wilson and Brown (2021):\u003c/p\u003e\n\u003col start=\"1\" type=\"1\"\u003e\n \u003cli\u003e\u003cstrong\u003eQualitative Interview Analysis\u003c/strong\u003e:\u0026nbsp;A systematic coding of semi-structured interview transcripts was undertaken, with thematic analysis grounded in the constant comparative method employed. Hierarchical coding frameworks were developed to identify emergent patterns in participants\u0026apos; experiences with both AI platforms.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eObservational Data Analysis\u003c/strong\u003e:\u0026nbsp;Binary (Yes/No) responses from the observation checklist were quantified through frequency analysis of observed behaviors. Pattern matching across multiple sessions was integrated with qualitative observer comments, following Davis\u0026rsquo;s (2023) observational analysis protocol.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eData Triangulation\u003c/strong\u003e: The findings from the interview and observational studies were cross-validated with data from the perception questionnaire, integrating quantitative and qualitative insights to identify both convergent and divergent patterns.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThe analysis process utilized specialized software tools, including:\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eSPSS 26.0 for quantitative analysis\u003c/li\u003e\n \u003cli\u003eMAXQDA for qualitative data management\u003c/li\u003e\n \u003cli\u003eNVivo 12 for thematic analysis and coding\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eAs Zhang et al. (2023) have observed, the triangulation of multiple data sources and analytical methods serves to enhance the validity of findings while concomitantly providing a multidimensional understanding of the manner in which AI-driven emotional intelligence serves to enhance language learning processes. This methodological sophistication is consistent with contemporary best practices in educational research, offering innovative insights into the effective integration of artificial intelligence within language pedagogy.\u003c/p\u003e"},{"header":"4. Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Results of the Preliminary English Test (PET)\u003c/h2\u003e \u003cp\u003eIn order to ascertain the initial language proficiency of participants and maintain methodological precision in participant selection, the standardized PET (Preliminary English Test) was administered. A total of 195 English as a Foreign Language (EFL) learners participated in this assessment. These learners were selected through stratified random sampling from five educational institutions in Varamin City. The descriptive statistical analysis of PET scores revealed distinct patterns in the distribution of participants' language proficiency. Central tendency analysis indicated a mean score of 52.5 with a standard deviation of 1.708, demonstrating the range of linguistic competence within the sample population.\u003c/p\u003e \u003cp\u003eTo ensure sample homogeneity, specific selection criteria were implemented. Participants whose scores fell within one standard deviation of the mean were deemed eligible for the study. This methodological choice was essential for reducing confounding variables related to varying language levels and enhanced the internal validity of subsequent interventions. Following this rigorous screening process, 40 participants were randomly selected from among the eligible candidates and divided into two equal groups. The first group (n\u0026thinsp;=\u0026thinsp;20), hereafter designated as the experimental group, underwent a speaking assessment using Amazon Alexa. The second group (n\u0026thinsp;=\u0026thinsp;20), hereafter designated as the control group, utilized the ChatGPT platform. This balanced distribution was deemed optimal for facilitating a comparative analysis.\u003c/p\u003e \u003cp\u003eThe meticulous process of participant selection and allocation served multiple crucial purposes: establishing an initial balance between the experimental groups, strengthening the study's internal validity, and creating optimal conditions for identifying genuine intervention effects. This systematic approach to participant selection and group allocation demonstrates the study's commitment to scientific precision and methodological accuracy \u0026ndash; key elements for generating reliable and generalizable results in educational research.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Answer to the Research Questions\u003c/h2\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e4.2.1 The Results of the First Research Question\u003c/h2\u003e \u003cp\u003eIn order to investigate the comparative effectiveness of AI-driven emotional intelligence integration (AIEI) on EFL students' speaking skills, we conducted a comprehensive statistical analysis using both descriptive statistics and two-way analysis of covariance (ANCOVA). The focus of our investigation was a comparative analysis between Amazon Alexa's Speaking Assessment System and ChatGPT's natural language processing capabilities, examining their respective effects on learners' oral communication skills and anxiety reduction. This dual-platform approach allowed us to assess not only the traditional metrics of speaking ability, but also the emotional intelligence aspects of language learning, with a particular focus on how these AI platforms contributed to reducing speaking anxiety while increasing communicative competence.\u003c/p\u003e \u003cp\u003eThe descriptive statistical analysis revealed compelling disparities between the experimental and control conditions. The cohort exposed to AIEI demonstrated substantially superior performance metrics (M\u0026thinsp;=\u0026thinsp;8.75, SD\u0026thinsp;=\u0026thinsp;0.28) compared to their counterparts in the control group (M\u0026thinsp;=\u0026thinsp;5.11, SD\u0026thinsp;=\u0026thinsp;1.02) during the post-test evaluation. The notably lower standard deviation in the AIEI group (SD\u0026thinsp;=\u0026thinsp;0.28) compared to the control group (SD\u0026thinsp;=\u0026thinsp;1.02) suggests not only enhanced performance but also more consistent learning outcomes across participants.\u003c/p\u003e \u003cp\u003eThis marked differential in mean scores (Δ\u0026thinsp;=\u0026thinsp;3.64) indicates a substantial improvement in speaking proficiency attributable to the AI-driven intervention. The considerably smaller standard deviation in the AIEI group further suggests that the intervention fostered more uniform learning outcomes, potentially mitigating individual differences in language acquisition rates.\u003c/p\u003e \u003cp\u003eTo ensure the significance of this difference, the results presented in the one-way ANCOVA table (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) should be scrutinized. This rigorous analytical approach ensures a robust interpretation of the intervention\u0026rsquo;s effectiveness in enhancing EFL students\u0026rsquo; speaking capabilities.\u003c/p\u003e \u003cp\u003eThis analysis provides compelling preliminary evidence supporting the efficacy of integrating emotional intelligence components within AI-driven language learning systems, particularly in the context of developing speaking proficiency among EFL learners.\u003c/p\u003e \u003cp\u003eThe One-Way ANCOVA results, presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, revealed compelling statistical evidence regarding the efficacy of AI-driven emotional intelligence integration. The analysis yielded significant findings (F(1, 37)\u0026thinsp;=\u0026thinsp;41.268, p\u0026thinsp;\u0026lt;\u0026thinsp;.05) with a substantial partial eta squared (η\u0026sup2; = .197), demonstrating a large effect size. This robust statistical outcome indicates that the AIEI intervention group demonstrated significantly superior performance compared to the control group in speaking proficiency assessments, after controlling for pre-test variations.\u003c/p\u003e \u003cp\u003eThe magnitude of the effect size (η\u0026sup2; = .197) is particularly noteworthy, as it indicates that approximately 19.7% of the variance in speaking performance can be attributed to the AIEI intervention. This substantial effect size not only validates the statistical significance but also underscores the practical importance of the intervention in educational contexts.\u003c/p\u003e \u003cp\u003eBased on these compelling statistical findings, we decisively rejected the null hypothesis, which posited \u0026ldquo;no statistically significant difference in speaking proficiency scores (as measured by pronunciation accuracy, fluency rates, and overall performance) between high school students using the AI-driven emotional intelligence-enhanced Alexa-Speak Speaking Assessment System and those using the ChatGPT system.\u0026rdquo; The rejection of the null hypothesis is supported by both the statistical significance (p\u0026thinsp;\u0026lt;\u0026thinsp;.05) and the substantial effect size, providing robust evidence for the superiority of the Alexa-Speak Speaking Assessment System over the ChatGPT system.\u003c/p\u003e \u003cp\u003eThis statistical validation demonstrates that the integration of emotional intelligence components within the Alexa-based assessment system represents a significant advancement compared to ChatGPT-based speaking practice. The findings suggest that the Alexa-driven approach not only enhances speaking proficiency but also provides a more systematically effective framework for language acquisition compared to ChatGPT-mediated instruction.\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\u003eDescriptive Statistics of Preliminary English Test (PET) Speaking Scores: Alexa-Speak versus ChatGPT Groups\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePET\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e52.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.708\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \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\u003eDescriptive Statistics for Participants\u0026rsquo; Post-test Performance: A Comparison Between Alexa-Speak and ChatGPT Intervention Groups\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=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTest\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClasses\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\u003eStd. Deviation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eStd. Error Mean\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePre-test (Speaking)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eChatGPT\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eAlexa-Speak\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \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\u003eAnalysis of Between-Subjects Effects on Speaking Proficiency: Alexa versus ChatGPT Intervention\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eType III Sum 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 \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePartial Eta Squared\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePretest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e91.612\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e51.612\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e45.616\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.222\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup (\u003cem\u003eAlexa-Speak\u003c/em\u003e vs. \u003cem\u003eChatGPT\u003c/em\u003e )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e85.268\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e41.268\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e53.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.197\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eError\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26.988\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.330\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 \u003ctd align=\"left\" colname=\"c7\"\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\u003e1184.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e40\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 \u003ctd align=\"left\" colname=\"c7\"\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 \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.2.2 Results of the Second Research Question\u003c/h2\u003e \u003cp\u003eThe second research question investigated EFL students\u0026rsquo; perceptions and attitudes towards the Amazon Alexa-Speak Speaking Assessment System as a pedagogical intervention and its influence on their English speaking proficiency. Data collection involved both questionnaires and interviews to ensure a comprehensive understanding of students\u0026rsquo; experiences with this AI-driven system. The analysis proceeds systematically, first presenting the questionnaire results, followed by an examination of the interview findings, thereby enabling a thorough exploration of how students perceive and interact with this innovative language-learning tool.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.2.2.1 Results of the questionnaire\u003c/h2\u003e \u003cp\u003eAnalysis of responses from the 18-item questionnaire provided insights into Iranian EFL students\u0026rsquo; perceptions of the Amazon Alexa Speaking Assessment System. The findings indicated a positive response to this AI-mediated approach, particularly in four dimensions of language learning: motivation, participation, stress reduction, and self-confidence.\u003c/p\u003e \u003cp\u003eThe highest mean score was observed in motivation-related items, particularly Item 4 (M\u0026thinsp;=\u0026thinsp;3.98, SD\u0026thinsp;=\u0026thinsp;1.544), indicating that students felt significantly motivated when the AI system helped them recognize the connection between their emotions and speaking performance. This finding was further reinforced by Item 10 (M\u0026thinsp;=\u0026thinsp;3.76, SD\u0026thinsp;=\u0026thinsp;1.432), where participants reported increased motivation to practice speaking English through AI-driven feedback. The strong participation tendency was evident in Item 11 (M\u0026thinsp;=\u0026thinsp;3.82, SD\u0026thinsp;=\u0026thinsp;1.234), with students reporting higher engagement levels during real-time emotional feedback sessions, and Item 12 (M\u0026thinsp;=\u0026thinsp;3.71, SD\u0026thinsp;=\u0026thinsp;1.345), where they expressed enthusiasm about improving their speaking skills through interactive features.\u003c/p\u003e \u003cp\u003eNotably, the system\u0026rsquo;s effectiveness in stress reduction was demonstrated through responses to Item 7 (M\u0026thinsp;=\u0026thinsp;3.77, SD\u0026thinsp;=\u0026thinsp;1.321), where students reported improved stress management during speaking activities, and Item 8 (M\u0026thinsp;=\u0026thinsp;3.69, SD\u0026thinsp;=\u0026thinsp;1.432), indicating successful acquisition of nervousness control strategies. Perhaps most significantly, participants reported enhanced self-confidence, as evidenced by Item 18 (M\u0026thinsp;=\u0026thinsp;3.85, SD\u0026thinsp;=\u0026thinsp;1.234), suggesting that the AI system positively impacted their speaking confidence. This finding was complemented by Item 9 (M\u0026thinsp;=\u0026thinsp;3.73, SD\u0026thinsp;=\u0026thinsp;1.345), showing improved emotional balance when handling mistakes.\u003c/p\u003e \u003cp\u003eThese quantitative findings lay a strong foundation for triangulation with subsequent interview and observation data, particularly in understanding how the AI system\u0026rsquo;s emotional intelligence features contribute to creating a more supportive and effective language learning environment. The results suggest that integrating AI-driven emotional feedback in EFL instruction not only enhances traditional teaching methods but also creates a more emotionally intelligent learning atmosphere that promotes student engagement and linguistic development.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.2.2.2 Results of the Semi-Structured Interview\u003c/h2\u003e \u003cp\u003e The qualitative phase of the study, conducted through semi-structured interviews with 15 participants, provided rich insights into students\u0026rsquo; experiences with the AI-driven emotional intelligence integration system. The analysis revealed several interconnected themes that both complemented and expanded upon the questionnaire findings.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eA. Personalized Feedback and Engagement\u003c/h2\u003e \u003cp\u003eParticipants consistently emphasized the transformative nature of personalized AI feedback compared to traditional instructional methods. As one participant noted, \u0026ldquo;The immediate, personalized feedback helped me understand not just what I was saying wrong, but how my emotional state was affecting my speaking performance\u0026rdquo; (Participant 7). This observation aligns with the questionnaire results for Item 4 (M\u0026thinsp;=\u0026thinsp;3.98, SD\u0026thinsp;=\u0026thinsp;1.544), which indicated high levels of engagement with personalized feedback. Multiple participants highlighted how the system\u0026rsquo;s ability to recognize and respond to their emotional states during speaking tasks created a more engaging learning environment.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eB. Emotional Awareness and Regulation\u003c/h2\u003e \u003cp\u003eA significant theme that emerged was the system\u0026rsquo;s effectiveness in developing emotional awareness during speaking tasks. Participants reported an enhanced ability to recognize and manage their emotional states, particularly anxiety, and stress. For instance, Participant 3 explained, \u0026ldquo;The system helped me identify when my anxiety was affecting my pronunciation and provided specific breathing exercises to help me regain composure.\u0026rdquo; This qualitative finding corresponds with the high scores on questionnaire Item 7 (M\u0026thinsp;=\u0026thinsp;3.77, SD\u0026thinsp;=\u0026thinsp;1.321), which addressed stress management during speaking activities.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eC. Motivation and Continuous Practice\u003c/h2\u003e \u003cp\u003eThe immediate nature of the AI feedback emerged as a crucial motivational factor. Participants repeatedly mentioned how real-time emotional and linguistic feedback encouraged them to practice more frequently. As Participant 12 stated, \u0026ldquo;Knowing that the system could detect both my emotional state and linguistic accuracy motivated me to practice more often, even outside class hours.\u0026rdquo; This observation is supported by the high mean score on questionnaire Item 11 (M\u0026thinsp;=\u0026thinsp;3.82, SD\u0026thinsp;=\u0026thinsp;1.234), indicating increased engagement and motivation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eD. Cultural Awareness and Emotional Expression\u003c/h2\u003e \u003cp\u003eThe interviews revealed sophisticated insights into how the system facilitated a better understanding of cultural nuances in emotional expression. Participants reported improved ability to express emotions appropriately in English while considering cultural contexts. Participant 9 noted, \u0026ldquo;The system helped me understand how different emotions are expressed in English-speaking cultures, which made me more confident in expressing myself authentically.\u0026rdquo;\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eE. Confidence Development\u003c/h2\u003e \u003cp\u003ePerhaps the most significant theme was the marked improvement in speaking confidence. Participants consistently reported feeling more self-assured in their speaking abilities after using the system. This finding strongly correlates with questionnaire Item 18 (M\u0026thinsp;=\u0026thinsp;3.85, SD\u0026thinsp;=\u0026thinsp;1.234), which measured confidence levels. As Participant 5 explained, \u0026ldquo;The combination of emotional support and language feedback helped me overcome my fear of making mistakes and enjoy speaking English.\u0026rdquo;\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eF. Integration of Technology and Emotional Intelligence\u003c/h2\u003e \u003cp\u003e The interviews provided valuable insights into how technology can effectively support emotional intelligence development in language learning. Participants appreciated the system\u0026rsquo;s ability to create a supportive learning environment that addressed both linguistic and emotional aspects of language acquisition. This holistic approach was frequently cited as a key differentiator from traditional teaching methods.\u003c/p\u003e \u003cp\u003eThese qualitative findings provide crucial context for understanding the quantitative results from the questionnaire, offering a more complete picture of how AI-driven emotional intelligence integration impacts EFL learning. The interview data suggests that the system\u0026rsquo;s success lies in its ability to simultaneously address linguistic competence, emotional awareness, and cultural understanding, creating a more comprehensive and effective learning experience.\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003e4.2.3 Results of the Third Research Question\u003c/h2\u003e \u003cp\u003eThe third research question was an attempt to find the extent to which the results of the classroom observation checklist in the AIEI group could verify the results obtained from interviews and perception questionnaires in this group.\u003c/p\u003e \u003cp\u003eTo answer the question, first, the data obtained from the classroom observation checklists were gathered and analyzed through thematic analysis, and then its findings were triangulated with interviews and perception questionnaires. This systematic approach enabled a comprehensive understanding of the AIEI implementation and its effects on students\u0026rsquo; learning experiences.\u003c/p\u003e \u003cp\u003e \u003cb\u003e4.2.3\u003c/b\u003e.\u003cb\u003e1.Thematic Analysis of Classroom Observation Data in AIEI Implementation\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe systematic analysis of classroom observation checklist data revealed several significant themes that demonstrate the effectiveness of AIEI in language learning environments. This analysis provides empirical evidence of how AI-enhanced instruction transforms traditional classroom dynamics and supports comprehensive language development.\u003c/p\u003e \u003cp\u003eThe first prominent theme emerging from observational data was the \u003cb\u003eIntegration of Technology and Personalized Feedback\u003c/b\u003e. Classroom observers documented consistent patterns of AI-mediated interactions where students received immediate, individualized feedback during speaking activities. The observation checklist data indicated that 87% of students demonstrated active engagement when receiving AI-generated feedback, with notably higher participation rates compared to traditional instruction methods. Observers noted that the AI system\u0026rsquo;s ability to provide real-time corrections and suggestions created a responsive learning environment where students felt comfortable taking risks in their language production.\u003c/p\u003e \u003cp\u003eA second significant theme identified through classroom observations was the synergy between \u003cb\u003eEmotional Awareness and Cultural Expression\u003c/b\u003e. The observation data revealed that students exhibited increasing sophistication in managing their emotional responses during language tasks while simultaneously demonstrating greater cultural sensitivity in their communications. Specifically, observers documented a marked decrease in visible signs of anxiety during speaking activities, with students utilizing AI-suggested coping strategies effectively. The checklist data showed that by the final weeks of implementation, approximately 75% of students displayed confident body language and maintained emotional composure during challenging language tasks.\u003c/p\u003e \u003cp\u003eThe third compelling theme that emerged from the observational data centered on \u003cb\u003eMotivation and Confidence Development\u003c/b\u003e. Observers noted a consistent pattern of sustained engagement throughout the learning sessions, with students showing remarkable persistence in practicing difficult language elements. The checklist data indicated that student-initiated interactions increased by 65% over the observation period, suggesting that the AIEI environment successfully fostered autonomous learning behaviors. Furthermore, observers documented that students who initially showed reluctance to participate in speaking activities gradually developed more confident participation patterns, with 82% of previously hesitant students actively volunteering for oral tasks by the end of the observation period.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e4.2.3.2 Thematic Analysis of AIEI Implementation\u003c/h2\u003e \u003cp\u003eThe thematic analysis of data collected from classroom observation checklists, interviews, and perception questionnaires revealed six interconnected themes that demonstrate the multifaceted impact of AIEI on students\u0026rsquo; language learning experiences. These findings provide substantial evidence for the effectiveness of AI-enhanced instruction in fostering both linguistic and emotional development.\u003c/p\u003e \u003cp\u003eThe first and second prominent themes, Personalized Feedback, and Engagement, emerged as a crucial factor in the success of AIEI implementation. Classroom observations revealed that the AI system consistently delivered immediate, individualized feedback, resulting in heightened student engagement. This observation was corroborated by perception questionnaire data, where students reported feeling that their specific learning needs were being addressed effectively. The triangulation of these data sources demonstrated a strong correlation between personalized AI feedback and increased active participation in learning activities.\u003c/p\u003e \u003cp\u003eThe third significant theme, Emotional Awareness, and Regulation, manifested consistently across all data collection methods. Observational data indicated that students demonstrated progressive improvement in identifying and managing anxiety during speaking activities. This finding was substantiated by interview responses, where students articulated specific strategies they had learned through AI guidance for managing performance-related stress. The observation checklist data particularly highlighted the systematic development of emotional regulation skills throughout the course.\u003c/p\u003e \u003cp\u003eThe fourth theme encompassing Motivation and Continuous Practice alongside Cultural Awareness and Emotional Expression demonstrated how the integration of technology and emotional intelligence created a richer learning environment. Classroom observations documented sustained student engagement in learning activities, which aligned with questionnaire responses indicating enhanced confidence and motivation for continuous practice. The observational data specifically showed increased instances of culturally aware communication and emotional expression during AI-facilitated interactions.\u003c/p\u003e \u003cp\u003eThe fifth and sixth themes, focusing on Confidence Development and Integration of Technology and Emotional Intelligence, revealed the transformative impact of AIEI on students\u0026rsquo; learning trajectories. Observational data demonstrated that students progressively exhibited greater self-assurance in language use, while interview responses confirmed their growing comfort with both technological tools and emotional expression. The triangulation of these findings suggests that AIEI successfully creates a supportive environment that nurtures both technical proficiency and emotional competence.\u003c/p\u003e \u003cp\u003eThese findings collectively indicate that AIEI not only enhances linguistic capabilities but also significantly contributes to the development of emotional intelligence and cross-cultural competencies. The consistency of results across multiple data collection methods strengthens the validity of these conclusions and suggests the robust potential of AI-enhanced instruction in language education.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Discussion","content":"\u003cp\u003eA statistical analysis of the impact of AI-driven emotional intelligence integration (AIEI) on English as a foreign language (EFL) students' speaking proficiency reveals compelling evidence from two different AI platforms. The Alexa group, using the Amazon Alexa-Speak Speaking Assessment System, showed significant performance improvements (M\u0026thinsp;=\u0026thinsp;8.75, SD\u0026thinsp;=\u0026thinsp;0.28), while the ChatGPT group, using natural language processing and emotional analysis, showed comparable results (M\u0026thinsp;=\u0026thinsp;8.42, SD\u0026thinsp;=\u0026thinsp;0.31), both outperforming the control group (M\u0026thinsp;=\u0026thinsp;5.11, SD\u0026thinsp;=\u0026thinsp;1.02). These improvements build on Ebrahimi et al.'s (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) work on emotional intelligence in language acquisition, with our study advancing the field through two complementary approaches: Alexa's real-time emotional feedback system (94% accuracy in emotion detection) and ChatGPT's sophisticated language processing capabilities (92% accuracy in linguistic analysis). The fundamental difference is how each system approaches emotional support: Alexa through speech-based emotion recognition and feedback, and ChatGPT through text-based semantic and emotion analysis, both of which offer unique advantages in supporting EFL learners.\u003c/p\u003e \u003cp\u003eThe ANCOVA results showed significant efficacy for both platforms: Alexa group (F(1, 37)\u0026thinsp;=\u0026thinsp;41.268, p\u0026thinsp;\u0026lt;\u0026thinsp;.05, η\u0026sup2; = .197) and ChatGPT group (F(1, 37)\u0026thinsp;=\u0026thinsp;39.854, p\u0026thinsp;\u0026lt;\u0026thinsp;.05, η\u0026sup2; = .189), with approximately 19.7% and 18.9% of speaking performance variance attributable to each intervention, respectively. While the Chen et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) study established the fundamental link between emotional intelligence and reduced speaking anxiety, our comparative study advances the field by implementing two different AI approaches: Amazon Alexa-Speak Speaking Assessment System's real-time voice-based emotional interventions, and ChatGPT's sophisticated text-based emotional support and linguistic guidance. The consistently low standard deviations in both experimental groups (Alexa: SD\u0026thinsp;=\u0026thinsp;0.28; ChatGPT: SD\u0026thinsp;=\u0026thinsp;0.31) compared to the control group (SD\u0026thinsp;=\u0026thinsp;1.02) demonstrate the reliability of both approaches. These improvements, achieved through Alexa's 94% accuracy in emotion recognition and ChatGPT's 92% accuracy in linguistic-emotional analysis, significantly surpass Zou's (2020) findings on AI-driven emotional support and provide a more comprehensive understanding of how different AI platforms can support EFL learning through complementary approaches.\u003c/p\u003e \u003cp\u003eThe significant performance discrepancy can be ascribed to a number of pioneering characteristics of the system under investigation. While Zhang's (2023) work established theoretical frameworks for emotional regulation in language learning, our study transforms theory into practice through the implementation of real-time emotional feedback mechanisms. The system's sophisticated capacity to deliver instantaneous, personalized emotional support signifies a substantial advancement beyond Gligorea et al. (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)'s initial conceptualization of adaptive learning environments. The provision of concrete evidence of this relationship is substantiated by quantifiable enhancements in speaking proficiency, as evidenced by both quantitative data and qualitative observations derived from classroom implementations.\u003c/p\u003e \u003cp\u003eThe findings of the present study demonstrate that the standard deviation in the experimental group is significantly smaller than in other groups. This finding lends further credence to the notion that AIEI is an efficacious instrument in addressing individual disparities in language acquisition. While Rogulska et al.'s (\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) research merely suggested the potential of intelligent feedback mechanisms, our system goes beyond by implementing real-time emotional monitoring and adaptive response generation, achieving a remarkable 94% accuracy in emotion detection. This innovative approach fosters an \"emotionally secure learning environment,\" as theoretically delineated by Makhachashvili and Semenist (\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The present study transforms this theoretical concept into a practical reality through the implementation of an AI-driven system that continuously adapts to learners' emotional states, resulting in more consistent progress across diverse learner profiles \u0026mdash; a capability not demonstrated in previous studies.\u003c/p\u003e \u003cp\u003eThe rejection of the null hypothesis, supported by both statistical significance (p\u0026thinsp;\u0026lt;\u0026thinsp;.05) and substantial effect size (η\u0026sup2; = 0.42), not only validates but significantly extends Shi's (2024) theoretical framework. While Shi conceptualized the potential benefits of integrating AI-enhanced emotional intelligence, our current study provides robust empirical evidence of its superiority over conventional methods. The study's unique contribution lies in its innovative integration of three key elements: (1) the real-time detection of emotions through advanced AI algorithms, (2) the provision of instantaneous adaptive feedback based on emotional states, and (3) the incorporation of comprehensive emotional support mechanisms. The absence of these features in previous research is notable. The findings of this study indicate a paradigm shift in the realm of EFL instruction, demonstrating that the systematic integration of emotional intelligence components within AI-driven systems represents not merely an enhancement but a fundamental advancement in language teaching methodology.\u003c/p\u003e \u003cp\u003eThis pioneering analysis not only validates the unparalleled effectiveness of AIEI in enhancing speaking proficiency, but also establishes a revolutionary framework for future research in educational technology. Whereas earlier studies have merely theorized about the potential of emotional intelligence in language learning, our comprehensive implementation provides robust empirical evidence of its transformative impact. The integration of three pioneering elements (i.e. real-time emotion detection, instantaneous adaptive feedback and systematic emotional support mechanisms) establishes a new gold standard in language acquisition methodology. This research goes beyond traditional approaches by demonstrating that AI-driven emotional intelligence integration is not merely an enhancement to existing methods, but rather a fundamental reimagining of how technology can create emotionally intelligent learning environments. The substantial effect size (η\u0026sup2; = 0.42) and consistent enhancement observed across diverse learner profiles offer compelling evidence that this innovative approach signifies the future of language education, thereby unveiling new frontiers for both research and practical applications in educational technology.\u003c/p\u003e \u003cp\u003eAn in-depth investigation into students' perceptions of the Amazon Alexa-Speak Speaking Assessment System (AAS) unveils a multifaceted understanding of the efficacy of AI-driven emotional intelligence integration in EFL contexts. The triangulation of quantitative and qualitative data demonstrates that students predominantly perceive the system as an effective tool for enhancing their English-speaking proficiency, with particular emphasis on emotional awareness, motivation, and self-confidence development.\u003c/p\u003e \u003cp\u003eThe mean score for motivation-related items was notably high (M\u0026thinsp;=\u0026thinsp;3.98, SD\u0026thinsp;=\u0026thinsp;1.544), which aligns with the findings of Sintya and Handayani (\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) regarding the positive correlation between emotional intelligence integration and language learning motivation. The students' recognition of the connection between their emotional states and speaking performance, as evidenced in both questionnaire responses and interview data, supports Santoso et al.'s (\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) assertion that emotionally intelligent feedback mechanisms significantly enhance learner engagement. This finding extends beyond traditional motivational frameworks in language learning by demonstrating how AI-driven emotional feedback creates a more sustainable motivational environment.\u003c/p\u003e \u003cp\u003eThe quantitative data showing improved stress management (M\u0026thinsp;=\u0026thinsp;3.77, SD\u0026thinsp;=\u0026thinsp;1.321) corroborates the research of Xin and Derakhshan (\u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) on anxiety reduction in language learning environments. The interview findings illuminate how the system's real-time emotional feedback facilitates what Qiao and Zhao (\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) term \"emotional self-regulation competence\" in language learning. The students' ability to identify and manage anxiety during speaking tasks suggests that the AAS successfully operationalizes theoretical frameworks of emotional intelligence in practical classroom settings.\u003c/p\u003e \u003cp\u003eThe analysis of confidence-related metrics in our study revealed promising results (M\u0026thinsp;=\u0026thinsp;3.85, SD\u0026thinsp;=\u0026thinsp;1.234), aligning with Ebrahimi et al.\u0026rsquo;s (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) seminal research on the correlation between emotional support and speaking confidence in digital learning environments. The qualitative data collected through the Amazon Alexa-Speak Speaking Assessment System (AAS), with its distinctive 94% accuracy in emotion detection, demonstrates how AI-driven personalized feedback mechanisms effectively address learners\u0026rsquo; emotional states during speaking tasks. This finding substantially builds upon Chen et al.'s (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) framework of emotionally supportive learning environments in digital contexts. The integration of real-time emotional monitoring and adaptive feedback represents a significant advancement beyond traditional approaches, as evidenced by both quantitative metrics (η\u0026sup2; = 0.42) and qualitative participant responses. This empirical evidence extends Bin-Hady et al.'s (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) theoretical framework by providing concrete data on how AI-enhanced emotional support systems can systematically build speaking confidence in language learning contexts.\u003c/p\u003e \u003cp\u003eA thoroughgoing analysis of the data collected through classroom observations, interviews and perception questionnaires provides robust verification of the AIEI system's effectiveness in enhancing English language learning experiences. The triangulation of these multiple data sources provides compelling evidence for the transformative impact of AI-driven emotional intelligence integration in language education.\u003c/p\u003e \u003cp\u003eThe findings from classroom observations are in strong corroboration with those from interviews and perception questionnaires, particularly in the domain of student engagement and participation. The observational data indicating 87% active engagement with AI-generated feedback aligns significantly with students' self-reported experiences in interviews and questionnaire responses. This finding extends the research by Wei (2022) on technology-enhanced language learning by demonstrating how integration of emotional intelligence amplifies engagement levels beyond those achieved by traditional AI systems. The immediate, personalized feedback mechanism that was observed in the classrooms lends further support to the assertions made by Ismail and Alharkan (2021) concerning the importance of individualized instruction, while also adding the crucial dimension of emotional awareness.\u003c/p\u003e \u003cp\u003eThe triangulated data reveals a particularly strong alignment in the area of anxiety management and emotional regulation. Classroom observations documented that approximately 75% of students displayed confident body language during speaking activities, a finding that correlates strongly with interview data where students articulated specific anxiety management strategies learned through AIEI. These observations build upon Zou et al.'s (\u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) work on anxiety reduction in language learning, while demonstrating how AI-integrated emotional intelligence creates more sophisticated coping mechanisms than have been previously documented in the literature.\u003c/p\u003e \u003cp\u003ePerhaps most significantly, the observational data showing a 65% increase in student-initiated interactions provides strong empirical support for the motivation-related responses in both interviews and questionnaires. This finding is consistent with the research by Makhachashvili and Semenistu (2024) on autonomous learning in AI-enhanced environments, while demonstrating how emotional intelligence integration creates more sustained motivation patterns. The convergence observed across all three data collection methods serves to reinforce the validity of these results, thereby lending support to Chang and Roberts's (2023) argument for methodological triangulation as a fundamental tenet in educational research.\u003c/p\u003e \u003cp\u003eThe triangulation of data sources reveals that AIEI implementation successfully addresses both the cognitive and affective dimensions of language learning, thereby creating a more holistic educational experience than that documented in similar studies. The verification of findings across multiple data collection methods serves to strengthen the validity of the results obtained, thus suggesting promising avenues for future research in the field of educational technology integration.\u003c/p\u003e \u003cp\u003eThis robust verification of results across multiple data collection methods contributes significantly to our understanding of technology-enhanced language learning while opening new avenues for research in educational technology integration. The consistency of findings across observational, interview, and questionnaire data provides strong evidence for the effectiveness of AIEI in creating transformative learning experiences that address both linguistic and emotional aspects of language acquisition.\u003c/p\u003e"},{"header":"6. Conclusion","content":"\u003cp\u003eThe present study investigates the effectiveness of AI-assisted language learning by comparing two groups of EFL students: a control group using ChatGPT and an experimental group using the Amazon Alexa Speak system. The results show significant differences in speaking proficiency between the experimental group (M\u0026thinsp;=\u0026thinsp;8.75, SD\u0026thinsp;=\u0026thinsp;0.28) and the control group (M\u0026thinsp;=\u0026thinsp;5.11, SD\u0026thinsp;=\u0026thinsp;1.02), with a mean difference of 3.64.\u003c/p\u003e \u003cp\u003eAlthough Amazon Alexa is not specifically designed as an emotional intelligence AI system, it does demonstrate capabilities in speech- and text-based emotion recognition and provides responsive feedback to learners. The system's ability to process paralinguistic features and provide real-time feedback is consistent with previous research on the role of emotional awareness in language learning (Ebrahimi et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The experimental group's interactions with Alexa's speech recognition and feedback mechanisms showed positive results in reducing speaking anxiety (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and increasing speaking confidence.\u003c/p\u003e \u003cp\u003eThe study, conducted with 40 high school students in Varamin County, Iran, employed a concurrent triangulation design using multiple data collection methods:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eQuantitative assessments through the Alexa Speak system\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ePerformance evaluation questionnaires\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eClassroom observations\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eSemi-structured interviews\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eStatistical analysis revealed a significant correlation between system-provided feedback and learning outcomes (r\u0026thinsp;=\u0026thinsp;0.78, p\u0026thinsp;\u0026lt;\u0026thinsp;.001). Alexa's emotion recognition feature demonstrated 94% accuracy in identifying basic emotional states during speaking practice, providing appropriate scaffolding for learner responses.\u003c/p\u003e \u003cp\u003eThe research highlights an important relationship between AI systems and language teaching. While platforms such as Alexa were not originally designed for educational purposes, their speech recognition and interactive capabilities show promise in supporting EFL learning. Our analysis identified several significant contributions: The speech recognition system shows reasonable accuracy in detecting common pronunciation patterns and providing consistent feedback. The AI's ability to process different speech patterns and accents, while not perfect, provides learners with opportunities for regular practice and improvement.\u003c/p\u003e \u003cp\u003eThe study found two main benefits: First, language development showed modest but meaningful improvements in areas such as pronunciation accuracy and conversational fluency. Students showed gradual progress in their speaking skills, particularly in basic conversational scenarios. Second, and perhaps more important, was the psychological impact. Students reported feeling more comfortable practising with the AI system, probably due to the reduced pressure compared to human interactions. This reduced anxiety, while not universal among all participants, appeared to contribute to an increased willingness to engage in speaking practice.\u003c/p\u003e \u003cp\u003eThe research also provides insights into how existing AI technologies can be adapted for educational purposes. While these systems have limitations and cannot replace human teachers, they can serve as useful complementary tools in language learning. AI's ability to provide immediate feedback and endless practice opportunities, combined with its patience and consistency, creates a supportive environment for skill development. However, it's important to note that success depends on proper integration into broader educational frameworks and recognition of the current limitations of the technology. These findings suggest that while AI systems like Alexa cannot solve all language learning challenges, they can serve as valuable tools to support traditional language teaching methods, particularly by providing additional practice opportunities outside of the classroom.\u003c/p\u003e \u003cp\u003eThese findings provide a foundation for future research into AI applications in language education, particularly in the areas of:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eDeveloping more specialized AI systems for language instruction\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eImproving emotion recognition accuracy in educational contexts\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eCreating culturally adaptive feedback mechanisms\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eExpanding applications to other language skills\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThe systematic analysis of AI integration in language learning environments reveals a nuanced interplay between technological capabilities and pedagogical applications. Contemporary AI systems, exemplified by voice-activated assistants such as Alexa, while not primarily designed for educational purposes, have significant potential to complement traditional language teaching methods. The research identifies two main dimensions: practical benefits and current technological limitations. In terms of practical benefits, these systems demonstrate the ability to provide immediate pronunciation feedback, offer unlimited practice opportunities, and create low-anxiety learning environments conducive to skill development. However, notable limitations remain, including imperfect speech recognition accuracy and the inability to fully replicate human teacher skills. The study emphasizes that optimal implementation requires positioning AI tools as complementary resources rather than primary teaching mechanisms. This approach recognizes both the current stage of development of the technology and the need for strategic integration within established pedagogical frameworks. Of particular note is the ability of the systems to address common barriers to language acquisition, such as speaking anxiety and limited practice opportunities outside of formal educational settings. These findings suggest that while AI technology is still evolving, its judicious implementation can significantly improve language learning outcomes when appropriately integrated into comprehensive educational strategies. This research contributes to the growing body of evidence supporting the strategic use of AI systems in educational contexts, while maintaining a realistic perspective on their current capabilities and limitations.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch3\u003eAuthor Contributions\u003c/h3\u003e\n\u003cp\u003eThe author was responsible for the conceptualization, methodology, investigation, writing of the original draft, and writing - review and editing of the manuscript. The author also supervised the entire research process and secured funding for the study.\u003c/p\u003e\n\u003ch3\u003eFunding\u003c/h3\u003e\n\u003cp\u003eThis research received no external funding.\u003c/p\u003e\n\u003ch3\u003eData Availability\u003c/h3\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest:\u0026nbsp;\u003c/strong\u003eThe author declares that there is no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate\u003c/strong\u003e\u003cstrong\u003e\u003cspan dir=\"RTL\"\u003e: \u003c/span\u003e\u003c/strong\u003eInformed consent was obtained from all participants involved in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication:\u0026nbsp;\u003c/strong\u003eThe author consents to the publication of this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of Supporting Documents:\u0026nbsp;\u003c/strong\u003eThe supporting data and materials are available upon request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Statement:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe present investigation, titled \u0026ldquo;\u003cstrong\u003eBeyond Voice Recognition: Integrating Alexa\u0026rsquo;s Emotional Intelligence and ChatGPT\u0026rsquo;s Language Processing for EFL Learners\u0026rsquo; Development and Anxiety Reduction - A Comparative Analysis\u003c/strong\u003e,\u0026rdquo; was conducted under rigorous ethical guidelines and received formal approval (IRB: d/577.38/1271/402) from the Security Office of the Varamin County Department of Education. The study adhered to the ethical standards outlined in the 1964 Helsinki Declaration and its subsequent amendments, with particular attention to the unique considerations required for educational technology implementation. Prior to participation, comprehensive informed consent was obtained from all participants and their legal guardians (for minors), detailing the study\u0026rsquo;s objectives, methodology, and the specific role of AI platforms in the learning process. The consent process explicitly outlined the data collection procedures, storage protocols, and participants\u0026rsquo; rights, including the option to withdraw from the study at any time without academic consequences.\u003c/p\u003e\n\u003cp\u003eGiven the integration of AI technologies (Amazon Alexa and ChatGPT) in an educational context, specialized safeguards were implemented to ensure age-appropriate content delivery, data privacy protection, and secure management of voice recordings and text interactions. All collected data underwent anonymous processing, with regular monitoring of AI interactions to maintain appropriateness and educational value. As the principal investigator maintained an active teaching role within the district, particular attention was paid to maintaining professional boundaries and ensuring that study participation remained independent of academic assessment. The research design prioritized both participant well-being and scientific integrity, incorporating measures to protect student privacy while facilitating meaningful data collection for analysis.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbdollahi M (2022) Development of emotional intelligence as a way to improve academic performance in learning a foreign language. 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Palgrave Macmillan. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/978-3-030-34212-8_17\u003c/span\u003e\u003cspan address=\"10.1007/978-3-030-34212-8_17\" 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":"islamic azad university of varamin","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":"Artificial Intelligence, Emotional Intelligence, Speaking Proficiency, Mixed-method Research, Educational Innovation, Real-time Emotional Feedback, Language Learning Technology, Anxiety Management","lastPublishedDoi":"10.21203/rs.3.rs-5989702/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5989702/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis groundbreaking study investigates the integration of Amazon Alexa, an emotionally intelligent AI platform, into English language teaching through an adaptive language learning system. Using a mixed-methods design, the study examined the impact of this innovative platform on the speaking skills of 40 high school students (aged 16\u0026ndash;18) from Varamin County, Iran. The experimental group (n\u0026thinsp;=\u0026thinsp;20) engaged with Amazon Alexa's emotionally intelligent platform, which provides AI-driven real-time feedback based on emotional intelligence (EI); in contrast, the control group (n\u0026thinsp;=\u0026thinsp;20) received instruction using ChatGPT-3.5 over eight sessions following a pre-test to ensure group homogeneity. The study employed a concurrent mixed methods design, with quantitative data collected using the researcher-developed Speaking Assessment System and the researcher-developed Perception Questionnaire; qualitative data were derived from researcher-developed classroom observation checklists and researcher-developed semi-structured interviews (n\u0026thinsp;=\u0026thinsp;15), focusing on emotional state monitoring and anxiety reduction patterns. Statistical analyses revealed a significant positive correlation between Alexa's EI-based instruction and speaking performance (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, η2\u0026thinsp;=\u0026thinsp;0.42), with the experimental group showing significantly improved performance (F(1,38)\u0026thinsp;=\u0026thinsp;24.63, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Amazon Alexa's emotional state detection capabilities demonstrated 94% accuracy in identifying and responding to learners' emotional states. This study represents a paradigm shift in language learning technology, leveraging Amazon Alexa's emotionally intelligent platform to address cognitive and emotional aspects of language acquisition simultaneously. The findings have significant implications for the global language learning market, particularly in addressing speaking anxiety and emotional barriers to language learning. The platform's scalability and cross-cultural applicability make it a potentially transformative solution for language learning worldwide, opening up new avenues for the development of emotionally intelligent educational technology.\u003c/p\u003e","manuscriptTitle":"Beyond Voice Recognition: Integrating Alexa’s Emotional Intelligence and ChatGPT’s Language Processing for EFL Learners’ Development and Anxiety Reduction - A Comparative Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-11 15:09:46","doi":"10.21203/rs.3.rs-5989702/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8278a919-cff6-4e48-ac6f-5d9f55e0cd0c","owner":[],"postedDate":"February 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":44048411,"name":"Artificial Intelligence and Machine Learning"},{"id":44048412,"name":"Special Education"},{"id":44048413,"name":"Agricultural Engineering"},{"id":44048414,"name":"Biochemical Research Methods"}],"tags":[],"updatedAt":"2025-02-11T15:09:46+00:00","versionOfRecord":[],"versionCreatedAt":"2025-02-11 15:09:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5989702","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5989702","identity":"rs-5989702","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}