Jul 2026· Arab World English Journal· 1 citation· ⚡ 1 influential· 35 references
TL;DR
The significance of this study is that it uses Intelligent Computer-Assisted Language Learning as an interpretive lens to make sense of a rapidly shifting field, offering a framework to help educators navigate modern generative tools.
Abstract
This scoping review primarily aims to synthesize empirical research on artificial intelligence in English language teaching and learning published from 2015 to 2025. The main question investigates how the integration, applications, and pedagogical roles of AI have evolved over the past decade. The significance of this study is that it uses Intelligent Computer-Assisted Language Learning as an interpretive lens to make sense of a rapidly shifting field, offering a framework to help educators navigate modern generative tools. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidance, 129 empirical studies were identified and analyzed using descriptive mapping and thematic analysis. The main findings indicate three overlapping evolutionary phases: an early system construction phase focused on tutoring, a mobile-and-voice phase emphasizing speech practice, and a generative phase dominated by large language models. The evidence base remains heavily concentrated in higher education, where AI frequently acts as a tutor, practice partner, or co-writer. For further use, this study recommends adopting teacher-mediated task designs, shifting assessments to focus on the learning process, and prioritizing longitudinal research in primary and under-resourced educational settings.
Artificial intelligence (AI) is extending the range of input, feedback, practice and personalised support for English as a second language learning. AI has been applied widely in language education for automated writing evaluation, chatbot-based interaction, adaptive learning, and instructional assistance, and is now helping to enhance the efficiency of learning and student autonomy. However, the rapid development of AI has also brought about several issues, such as a lack of long-term studies, inconsistent integration into teaching, over-reliance on machine-generated answers, and unresolved ethical problems of privacy, bias and academic integrity. This paper examines the application of artificial intelligence in English as a foreign language learning and explores how to improve it. Based on narrative literature review and conceptual analysis of representative studies in AI-enhanced language education, this paper will collect relevant research results and point out existing deficiencies. According to the above analysis, AI will act as a support for teaching and learning, not a replacement for it. The future direction will be focused on personalised guidance, multimodal learning, stronger empirical validation, and responsible governance.
Wenxuan Guan· Communications in Humanities...· 0 citations
Artificial Intelligence is increasingly influencing the methods through which English is taught, practised, assessed, and learned. The emergence of generative AI, intelligent tutoring systems, automated writing evaluation, adaptive learning environments, conversational agents, speech-recognition applications, and AI-supported assessment has created opportunities to move beyond uniform teacher-centred instruction towards more personalized, interactive, feedback-rich, and learner-responsive approaches. This conceptual research paper examines how Artificial Intelligence is redefining teaching methods in English language learning and how such changes may influence student performance. The study adopts an integrative literature review and conceptual analysis of recent scholarship on AI-assisted language learning, generative AI, automated feedback, academic writing, learner autonomy, personalized instruction, and digital pedagogy. Particular attention is given to changes in teaching strategies, including differentiated instruction, AI-supported conversation, adaptive language practice, automated formative feedback, process-oriented writing instruction, and data-informed assessment. The paper distinguishes between performance enhancement, in which AI improves the immediate quality or speed of task completion, and learning improvement, in which learners demonstrate transferable language competence without technological dependence. Evidence from recent empirical research indicates that structured AI-supported instruction can improve academic writing performance and support English proficiency and self-regulation. However, excessive dependence may reduce cognitive engagement, originality, and independent problem-solving, and authentic human interaction. The paper proposes the PERFORM-AI Framework, which integrates personalization, engagement, responsive feedback, formative assessment, originality, reflective learning, monitored AI use, and independent transfer. It concludes that the most effective model for English language learning is not AI replacing teachers but an instructional partnership in which teachers redesign pedagogy, AI extends opportunities for individualized practice and feedback, and students remain active agents responsible for their own learning.
C. Shabharishwaran, Mr. P. Kavinkumar, Mr B.Manojkumar· Stanzaleaf International Jou...· 0 citations
Artificial Intelligence has emerged as a transformative force in English Language Teaching by reshaping classroom pedagogy, assessment practices, and learner engagement. In the contemporary educational environment, AI-based tools such as intelligent tutoring systems, automated writing evaluation platforms, chatbots, speech recognition applications, adaptive learning systems, and generative AI models are increasingly influencing how English is taught and learned. This paper examines the role of Artificial Intelligence in English Language Teaching with special focus on three major areas: pedagogical transformation, assessment innovation, and learner engagement. The study adopts a qualitative and analytical approach based on recent developments in AI-supported language education. It argues that AI can support personalized learning, immediate feedback, learner autonomy, differentiated instruction, and interactive language practice. At the same time, the paper highlights important challenges such as academic integrity, overdependence on technology, unequal digital access, ethical concerns, teacher preparedness, data privacy, and the risk of reducing language learning to automated performance. The paper concludes that AI should not be viewed as a replacement for English teachers, but as a pedagogical partner that can enhance human teaching when used critically, ethically, and contextually.
Dr.M.Leena Chandrika, Dr. V. V. Malinee· Stanzaleaf International Jou...· 0 citations
This paper examines the existing research on generative artificial intelligence (GenAI) in language education and highlights its potential to reshape teaching practices, learner engagement, and pedagogical design. By leveraging co-word analysis and BERTopic modeling on 908 publications from 2023 to the end of 2025, the article traces thematic patterns and conceptual developments in the GenAI field. The co-word analysis identifies key clusters at the intersection of GenAI and English language instruction, including its use in academic writing, translation, assessment, and learner-centered pedagogy. These themes demonstrate the critical role of GenAI in enabling personalized feedback, adaptive learning environments, and enhanced teacher–student interaction. The application of BERTopic modeling adds a semantic layer to this analysis and reveals diverse topics such as AI-assisted writing, teacher engagement with AI tools, emotional and motivational dynamics, and technology acceptance among learners. Results also indicate a progression from broad pedagogical experimentation toward more focused inquiries into learner psychology, ethical use, and professional development. This trend reflects the maturation of GenAI applications in both formal and informal educational contexts. The findings offer implications for language educators, curriculum designers, and policymakers by highlighting the need to integrate GenAI through pedagogically grounded, ethically responsible, and learner-centered approaches. They also suggest that future language education practices should combine AI literacy, transparent assessment policies, and teacher professional development to support effective and equitable GenAI adoption. Ultimately, the article positions GenAI as a transformative force in language education that offers technological innovation and new frameworks for inclusive, responsive, and emotionally attuned instruction.
Abderahman Rejeb, K. Rejeb, Heba F. Zaher et al.· Quality & Quantity· 0 citations
This study examines the role of artificial intelligence (AI) in foreign language learning,
focusing on widely used AI-based tools such as ChatGPT, Duolingo, Grammarly, ELSA Speak,
Google Translate, and Memrise. The primary objective of the research is to explore the contribution
of these technologies to the development of learners’ linguistic competencies, including reading,
writing, speaking, and vocabulary acquisition, and to evaluate their pedagogical implications within
language education contexts. The study employs a qualitative comparative and exploratory research
design. Data were collected through document analysis and interaction with selected AI-based
language learning tools using standardized language learning tasks. The outputs generated by each
platform were systematically analyzed based on criteria such as accuracy, feedback quality,
adaptability, and instructional value. The results reveal that AI-based tools enhance language
learning by offering immediate feedback, individualized learning support, and expanded
opportunities for autonomous practice. At the same time, the findings indicate that excessive
dependence on AI technologies may reduce learners’ cognitive engagement and limit opportunities
for authentic communicative interaction. The study highlights that AI-based language learning tools
should be considered as complementary resources in foreign language education. Their effective
integration, alongside traditional instructional methods, can contribute to more efficient and
personalized language learning outcomes.
Irada Gasimova, A. Aliyeva, A. Allahverdiyev et al.· Scientific Works· 0 citations
This study examines how educational priorities have changed in artificial intelligence (AI) research involving scholars affiliated with institutions in 34 Asian countries and territories. It analyses 1,585 English-language Scopus journal articles published between 2010 and 2026, with partial coverage for 2026 up to 23 May. Biblioshiny was used to map keyword co-occurrence, thematic positioning, and thematic evolution across three periods: 2010–2016, 2017–2020, and 2021–2026. TALL was applied to a random sample of 792 abstracts to elaborate on the educational meanings embedded in the bibliometric structures. The findings indicate a reorientation from computer-aided instruction, intelligent tutoring systems, and learning systems towards generative AI, ChatGPT, technology adoption, and emerging human–AI discourses. Higher education and students provide the field’s principal educational focus, although students are frequently positioned as technology users or as adopters. Pedagogical mechanisms, teacher agency, assessment redesign, and responsible-use competencies remain less integrated. AI literacy, academic integrity, ethics, and critical thinking are visible but remain embedded within the broader technology and adoption structure. Abstract-level patterns further revealed benefit-oriented framing around support, enhancement, and future potential, which should not be interpreted as evidence of educational effectiveness. The study contributes an education-centred account of how technological expansion has outpaced the conceptual integration of pedagogy, human agency, assessment, and responsible institutional conditions.
Moch. Rizal Fuadiy, Ethem Gürhan, Adiyono Adiyono et al.· Journal of Educational Resea...· 0 citations
