AI-powered platforms for dynamic writing present transformative opportunities to enhance English language acquisition and actively engage learners in writing tasks. However, the role of artificial intelligence in improving writing proficiency and fostering motivation among English as a Foreign Language (EFL) learners remains an area that has yet to be thoroughly investigated. This research endeavor seeks to address the prevailing knowledge gap in the field by exploring the influence of AI-powered writing platforms on the writing skills of EFL learners. The study's objectives encompass the assessment of the impact of these platforms on coherence, vocabulary usage, grammatical accuracy, task accomplishment, and learner motivation. Utilizing a mixed-methods approach, the study examined 65 intermediate EFL students from the Islamic Azad University, Varamin-Pishva branch, who were divided into two groups: one employed AI-powered tools, while the other followed traditional classroom-based writing exercises. Quantitative data were gathered via IELTS-based assessments and motivation scales, while qualitative insights were derived from semi-structured interviews. The findings revealed substantial enhancements in the AI group across all measured dimensions in comparison to the control group. Additionally, the AI group demonstrated a significant surge in motivation levels. Learners in the AI group reported positive attitudes toward AI-based instruction, citing improvements in engagement, autonomy, and confidence in their writing. The platform also fostered greater self-regulation and personalized learning experiences, which participants found effective and enjoyable. The findings emphasize the efficacy of AI-based writing platforms in enhancing linguistic proficiency and motivational levels among EFL learners. The study provides practical insights for incorporating AI technologies into writing instruction, encouraging educators to leverage such tools for more effective and engaging language learning practices.

Listening comprehension is a critical component of English language acquisition and a core foundation for effective communication and language development. This study investigates the impact of gamified and non-gamified AI-driven learning systems, Duolingo and Replika, on the improvement of English as a foreign language (EFL) listening comprehension. A concurrent mixed methods design was used to collect data from 53 Iranian high school students (aged 14–15) residing in Tehran Province. Quantitative data were collected through researcher-developed pre- and post-intervention listening comprehension assessments and a perception questionnaire, while qualitative data were derived from classroom observation checklists and semi-structured interviews (n = 15). Students were randomly allocated to two groups: Duolingo (n = 27) and Replika (n = 26), who participated in a 12-week intervention consisting of 24 sessions. Duolingo uses gamified elements such as rewards and points to increase engagement, while Replika uses conversation-based interactions. Quantitative analysis revealed significant improvements in listening comprehension for both groups, with the Duolingo group showing higher engagement metrics. Qualitative findings showed that Duolingo's adaptive algorithms and gamification elements fostered a more engaging and personalized learning experience. The study highlights how these AI-driven systems address previously overlooked aspects of EFL instruction through personalized, data-driven pathways, effectively improving adolescents' listening comprehension skills. The research reviewed here highlights how these systems address previously overlooked aspects of EFL instruction, particularly in improving adolescents' listening comprehension skills. By focusing on personalized, data-driven pathways, the study demonstrates how intelligent learning platforms can fill gaps in traditional teaching and effectively improve listening comprehension - a critical yet often underemphasized skill in language teaching.

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.

The integration of ChatGPT, a sophisticated AI chatbot by OpenAI, has introduced transformative possibilities in language education. Distinguished by its ability to generate diverse text outputs, answer queries, and provide instant translations, ChatGPT represents a valuable asset in modern AI applications. However, its specific role in enhancing students' language learning remains underexplored. This study addresses this gap by examining students' interactions with ChatGPT and their perceptions of its influence on language acquisition. A qualitative methodology was adopted, utilizing semi-structured interviews with ten EFL students at Islamic Azad University, Varamin-Pishva Branch, Iran. The findings indicate that ChatGPT effectively supports language learning by assisting students with diverse tasks and fostering personalized learning experiences. Participants exhibited critical evaluative skills, adapting prompts to optimize outcomes and addressing challenges related to academic integrity. These skills are essential in navigating the ethical dimensions of AI-assisted learning. The present study contributes to extant research by providing empirical insights into ChatGPT's utility from learners' perspectives, highlighting its capacity for immediate feedback and autonomous learning facilitation. The implications for pedagogy include the development of AI-enhanced language support systems, the promotion of learner autonomy, and the cultivating of critical thinking for evaluating AI-generated content. As educational institutions continue to integrate technological advancements, ChatGPT demonstrates significant potential in reshaping teaching and learning practices, paving the way for innovative approaches in language education.

This groundbreaking study introduces the first-ever integration of emotional intelligence (EI) with artificial intelligence in English-speaking instruction through an emotionally adaptive language learning system. Through a mixed-method design, the research examined this innovative approach’s impact on speaking proficiency among 40 high school students (aged 15-18) from Varamin County, Iran. The experimental group (n=20) engaged with the novel “Amazon Alexa-Speak” Speaking Assessment System, featuring AI-driven EI-based real-time feedback; in contrast, the control group (n=20) received conventional instruction over six sessions following a pretest to ensure group homogeneity. The study employed a concurrent mixed method design, collecting quantitative data through the “Amazon Alexa-Speak” Speaking Assessment System and the researcher-made perception questionnaire; qualitative data came from researcher-made classroom observation checklists and researcher-made semi-structured interviews (n=20), focusing on emotional state monitoring and anxiety reduction patterns. Statistical analyses revealed a significant positive correlation between EI and speaking performance (p < 0.05, η2 = 0.42), with the experimental group showing substantially enhanced proficiency (F(1,38) = 24.63, p < 0.05). The system’s emotional state detection algorithm demonstrated 94% accuracy in identifying and responding to learners’ affective states. This study presents a paradigm shift in language education technology by introducing the first system that simultaneously addresses cognitive and emotional aspects of language acquisition. The findings have significant implications for the global language learning market, particularly in addressing speaking anxiety and emotional barriers in language learning. This technology’s scalability and cross-cultural applicability make it a potentially transformative solution for language education worldwide, opening new avenues for emotionally intelligent educational technology development.

This groundbreaking study introduces the first-ever integration of emotional intelligence (EI) with artificial intelligence in English-speaking instruction through an emotionally adaptive language learning system. Through a mixed-method design, the research examined this innovative approach’s impact on speaking proficiency among 40 high school students (aged 15-18) from Varamin County, Iran. The experimental group (n=20) engaged with the novel “Amazon Alexa-Speak” Speaking Assessment System, featuring AI-driven EI-based real-time feedback; in contrast, the control group (n=20) received conventional instruction over six sessions following a pretest to ensure group homogeneity. The study employed a concurrent mixed method design, collecting quantitative data through the “Amazon Alexa-Speak” Speaking Assessment System and the researcher-made perception questionnaire; qualitative data came from researcher-made classroom observation checklists and researcher-made semi-structured interviews (n=20), focusing on emotional state monitoring and anxiety reduction patterns. Statistical analyses revealed a significant positive correlation between EI and speaking performance (p < 0.05, η2 = 0.42), with the experimental group showing substantially enhanced proficiency (F(1,38) = 24.63, p < 0.05). The system’s emotional state detection algorithm demonstrated 94% accuracy in identifying and responding to learners’ affective states. This study presents a paradigm shift in language education technology by introducing the first system that simultaneously addresses cognitive and emotional aspects of language acquisition. The findings have significant implications for the global language learning market, particularly in addressing speaking anxiety and emotional barriers in language learning. This technology’s scalability and cross-cultural applicability make it a potentially transformative solution for language education worldwide, opening new avenues for emotionally intelligent educational technology development.