This study examines how AI-powered platforms—including chatbots, automated feedback tools, and intelligent tutoring systems—are reshaping English language teaching at the tertiary level. The authors find that AI platforms enhance learner engagement and provide timely, personalized feedback that traditional instruction cannot easily replicate. The paper concludes with recommendations for integrating AI tools into university ELT curricula while maintaining human pedagogical oversight.

This systematic review synthesizes research on digital technology use in English language teaching across multiple educational contexts. The review identifies key trends including increased adoption of AI tools, mobile-assisted language learning, and blended instruction models. The author highlights persistent gaps in teacher training and calls for more longitudinal studies on technology-mediated ELT outcomes.

This article explores how AI technologies can support and enhance professional learning communities (PLCs) among language teachers. The study proposes an AI-enhanced PLC framework that uses data analytics and intelligent recommendation systems to personalize professional development. Findings suggest that AI-augmented PLCs can improve collaborative reflection and teaching practice more effectively than traditional peer-learning models.

This paper investigates the integration of generative AI tools, including large language models, into K–12 classrooms, focusing on both opportunities and risks. The authors survey teachers across multiple school systems and find significant variation in readiness, confidence, and institutional support for AI adoption. The study concludes that structured professional development and clear policy frameworks are essential prerequisites for responsible generative AI use in schools.

This mixed-methods study investigates how AI conversational agents affect self-regulated learning (SRL) behaviors among EFL learners in a Chinese university context. Quantitative data show significant improvements in goal-setting, monitoring, and self-evaluation among students using AI agents, while qualitative interviews reveal that learners value the non-judgmental, always-available nature of AI conversation partners. The author argues that AI agents can serve as effective scaffolding tools for developing autonomous language learners.

This study proposes a machine learning–based adaptive learning framework that tailors instructional content to individual learner styles in higher education. The system classifies students using Felder-Silverman learning style dimensions and dynamically adjusts course materials, pacing, and assessment types. Results demonstrate improved learning outcomes and student satisfaction compared to a non-adaptive control group.

This paper describes the development and psychometric validation of a survey instrument measuring teachers' behavioral intention to adopt AI tools in classroom instruction. Drawing on the Technology Acceptance Model (TAM) and Unified Theory of Acceptance and Use of Technology (UTAUT), the authors identify perceived usefulness, ease of use, and institutional support as the strongest predictors of AI adoption. The validated instrument is offered as a resource for future studies on teacher AI acceptance.

This systematic review examines over 80 empirical studies on AI-driven personalized learning systems published between 2015 and 2024. The review identifies three dominant design paradigms—recommendation engines, intelligent tutoring systems, and adaptive assessment platforms—and evaluates their effectiveness across different educational levels and subject domains. The authors conclude that while evidence for learning gains is promising, methodological inconsistencies across studies limit definitive claims.

This review article surveys current AI techniques—including machine learning, natural language processing, and reinforcement learning—used in adaptive educational systems. The authors map each technique to specific pedagogical functions such as learner modeling, content sequencing, and real-time feedback generation. The paper identifies reinforcement learning as an emerging and particularly promising approach for dynamic curriculum adaptation.

This study evaluates the accuracy and pedagogical value of AI-generated corrective written feedback for EFL learners at the intermediate level. Using a quasi-experimental design, the authors compare AI feedback to teacher feedback on grammatical accuracy, coherence, and lexical choice. Findings indicate that AI feedback is comparable to teacher feedback in accuracy but less effective at addressing discourse-level issues, suggesting a complementary rather than replacement role for AI in writing instruction.

This systematic mapping study reviews 74 papers on AI-enabled adaptive learning systems, identifying key research themes, methodologies, and gaps in the literature. The authors find that most systems focus on content personalization and learner modeling, with comparatively less attention paid to social and affective dimensions of learning. The paper provides a comprehensive taxonomy of AI-enabled adaptive learning research and highlights priority areas for future investigation.

This systematic review evaluates the state of AI education in K–12 settings, focusing on curriculum frameworks, learning outcome assessments, and equity considerations. Analyzing 65 studies, the authors find that most K–12 AI programs emphasize computational thinking and basic ML concepts, with limited attention to ethical AI literacy. The review recommends more holistic frameworks that integrate technical skills with critical thinking about AI's societal impacts.

This narrative review synthesizes research on four major technology categories—AI tools, virtual reality, mobile learning applications, and learning management systems—as applied to English language teaching. The authors trace the evolution of each technology and evaluate evidence for their effectiveness in developing the four language skills (listening, speaking, reading, writing). The review concludes that integrated, multimodal approaches combining several technologies yield stronger learning outcomes than single-technology interventions.

This conceptual and empirical paper explores how generative AI tools such as ChatGPT and Gemini are being used by K–12 teachers to design lessons, generate assessments, and differentiate instruction. Case studies from three school districts reveal diverse implementation patterns and highlight both efficiency gains and concerns about academic integrity and over-reliance on AI-generated content. The author proposes a pedagogical framework for responsible generative AI integration that centers student agency and critical evaluation skills.

This paper presents the design, implementation, and evaluation of OpenLang, an open educational platform that integrates generative AI to support language learning and digital literacy development. The platform uses AI to generate personalized exercises, provide adaptive feedback, and recommend learning pathways based on learner proficiency profiles. Pilot evaluation with adult learners across three European countries shows significant improvements in language proficiency scores and digital competence self-assessments.

This study investigates the effects of AI-assisted language learning tools on learner outcomes, engagement, and intrinsic motivation in a Ukrainian university foreign language program. Using a mixed-methods approach, the authors find that students using AI tools demonstrate higher accuracy in writing tasks and report greater motivation compared to peers in traditional instruction groups. The study also identifies important moderating variables including prior digital literacy and attitudes toward technology.

This large-scale study examines the implementation of an adaptive intelligent tutoring system (ITS) as a supplement to regular classroom instruction in middle school mathematics. Analyzing data from over 2,000 students across 43 schools, the authors find that students using the ITS as a supplement—rather than a replacement—for teacher-led instruction show significantly greater learning gains. The study underscores the importance of teacher facilitation and classroom culture in realizing the benefits of adaptive technology.

This paper examines the design and effectiveness of AI-generated vocabulary tasks tailored for business English learners at the intermediate-to-advanced level. The author develops a corpus-informed framework for prompt engineering that produces contextually relevant, discipline-specific vocabulary exercises. Pre- and post-test results indicate significant vocabulary growth, and learner surveys reveal high satisfaction with the relevance and authenticity of AI-generated task content.

This comparative study evaluates the learning outcomes of students using an adaptive learning platform versus those receiving traditional teacher-led instruction in a university-level course. Results show that adaptive learning is most effective when students have foundational knowledge gaps and when the system is used in a blended rather than fully online format. The authors argue that adaptive learning systems are most powerful as precision tools for differentiated instruction rather than wholesale replacements for human teaching.

This meta-analysis synthesizes findings from 47 empirical studies on AI-enabled adaptive learning systems, examining their overall effect on student academic performance across educational levels and subject domains. Results reveal a moderate-to-large positive effect size (d = 0.62), with stronger effects observed in STEM subjects and among younger learners. The authors identify system adaptivity, feedback immediacy, and learner agency as key moderating variables that amplify the effectiveness of AI-enabled adaptive learning.