Jun 2026· Lubelski Rocznik Pedagogiczny· Vol 45, pp. 217-236· 0 citations
TL;DR
Artificial Intelligence integration in ELT is still formative, characterised by fragmented evidence and limited synthesis of risks and responses, and stronger empirical inquiry and integrated frameworks are required for effective implementation.
Abstract
Introduction: Artificial Intelligence (AI) shows significant potential in English Language Teaching (ELT); however, its use presents diverse challenges, highlighting a gap in systematic reviews that have not yet fully synthesised these issues and their associated remedies.
Research Aim: Guided by PRISMA guidelines and Okoli’s (2015) four-phase framework, this study critically reviews literature on AI-related risks and associated remedial strategies in ELT, with a focus on higher education contexts.
Evidence-based Facts: From an initial corpus of 350 studies, twenty peer-reviewed articles (2023–2024) were selected based on relevance, citation impact, and indexing in high-impact journals. A deductive thematic analysis informed by eleven predefined categories was conducted using reflexive thematic analysis (RTA) principles through MAXQDA 24. The synthesis indicates uneven conceptualisations of AI-related risks across studies. Ethical concerns, particularly academic integrity threats and risks linked to rapid AI adoption, dominate the literature and set the context for other challenges. These are closely followed by technical limitations and pedagogical shifts that involve insufficient digital literacy, teacher readiness, assessment-related constraints, and issues of access and equity. Learner responses vary in motivation, engagement, critical thinking, and emotional factors, although AI is generally associated with increased autonomy and participation. Skill development remains inconsistent across language domains. Proposed remedies cover pedagogical, ethical, technological, and governance dimensions, including training development, but remain largely conceptual with limited evidence of effectiveness.
Summary: AI integration in ELT is still formative, characterised by fragmented evidence and limited synthesis of risks and responses. Stronger empirical inquiry and integrated frameworks are required for effective implementation.
The rapid integration of Artificial Intelligence (AI) into academic environments has accelerated significantly in the post-COVID-19 era. This study presents a systematic literature review (SLR) evaluating the reported pedagogical benefits and structural challenges of AI applications in education. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework guidelines, a comprehensive search was executed across major academic databases, targeting peer-reviewed empirical literature published between 2021 and 2026. Applying strict inclusion and exclusion criteria, a final analytical sample of twenty (N = 20) international studies was synthesized. The thematic analysis demonstrates that while AI tools offer significant educational benefits — such as learning personalization, enhanced language acquisition, and the automation of administrative workloads — they present critical pedagogical and ethical challenges. These risks focus heavily on cognitive disengagement, diminished student critical thinking, and algorithmic data privacy vulnerabilities. This review highlights that the successful deployment of AI relies on balancing technological integration with robust ethical frameworks while preserving the central instructional responsibility of human educators.
Fatma Maamri· Journal of education and sci...· 0 citations
Artificial intelligence (AI) is increasingly embedded in higher education, with growing interest in its effects on students’ academic performance. Despite its rapid adoption, evidence regarding its benefits, risks, and implementation conditions remains limited. This systematic literature review synthesizes recent empirical evidence on the impact of AI on academic performance in higher education, and identifies the key opportunities and challenges associated with AI integration. Following the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines, a systematic search of the Scopus database was conducted for English language open-access journal articles published between 2023 and 2025. Using predefined inclusion criteria, 3,344 records were screened, resulting in 20 eligible studies for qualitative synthesis. Data were analyzed thematically across methodologies, contexts, and outcomes. The findings indicate that AI positively influences academic performance primarily through enhanced engagement, personalization, predictive analytics, and self-efficacy. Predictive models have achieved high accuracy in identifying at-risk students and supporting early intervention. However, challenges, including AI dependency, academic integrity concerns, data privacy risks, and unequal benefits across student groups, have consistently been reported. AI has substantial potential to enhance academic performance in higher education, but its effectiveness depends on thoughtful pedagogical integration, institutional readiness, and ethical governance. Balanced, inclusive, and well-regulated AI adoption is essential for maximizing benefits while mitigating risks.
Abdulkadir Abdullahi Mohamed, Ahmed Abdullahi Mohamud, Abdiwali Ali Addow· Journal of Natural Language...· 0 citations
Artificial intelligence has increasingly transformed educational practices, particularly in writing instruction. Among these innovations, AI-based feedback tools have appeared as favourable solutions for giving personalized, immediate, and accessible support to learners. This study aims to assess the effect of AI-based feedback on English writing performance, filling research gaps regarding the impact of English language proficiency level, duration of intervention, and implementation setting on the effectiveness of AI-based feedback tools in improving students’ English language writing performance. A systematic review and meta-analysis were utilized to examine 24 empirical studies published from January 2022 to March 2026 across five databases (including EBSCO, ProQuest, ERIC, Web of Science, and Wiley Online Library). The results indicated that AI-based feedback tools had a large overall effect size (Hedges' g = 1.298, p < 0.001) on students’ writing performance. Moreover, moderator analyses revealed that learners with intermediate proficiency benefited more substantially compared to advanced learners, suggesting that AI feedback is particularly effective for developing writers. In terms of implementation settings, blended learning environments yielded stronger effects than traditional classroom settings, highlighting the importance of flexible and technology-supported contexts. Although the duration of intervention did not significantly moderate the results, consistently strong effects were observed across the three periods of intervention: short, medium, and long-term. These findings suggest that AI-based feedback tools represent a powerful and scalable approach to enhancing English writing skills.
Mona Alzahrani· Journal of English language...· 0 citations
The findings indicate that GenAI adoption in African HEIs is expanding but uneven, concentrated in digitally advanced nations, enhancing personalization, multilingual learning, and research productivity, yet raises ethical concerns about academic integrity.
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This review investigates how recent studies conceptualise the theoretical foundations, pedagogical practices, learning outcomes, and the integration of technology and artificial intelligence within genre-based writing instruction, and proposes an integrated framework that links the genre-based approach with emerging AI-supported writing practices.
Zirui Chen, Nazeera Ahmed Bazari, G. Narayanan· Arab World English Journal· 0 citations
Artificial Intelligence (AI) is rapidly transforming educational practices and redefining the professional responsibilities of teachers across diverse learning environments. While substantial research has examined AI adoption, learning outcomes, intelligent tutoring systems, and generative AI applications, limited attention has been devoted to understanding how AI is reshaping the roles of mathematics teachers. This systematic literature review aims to synthesize existing evidence on the evolving roles of mathematics teachers within AI-enhanced educational contexts and to develop a comprehensive framework explaining role transformation. Following the PRISMA 2020 guidelines, a systematic search was conducted across major academic databases, including Scopus, Web of Science, ERIC, ScienceDirect, SpringerLink, Taylor & Francis Online, Wiley Online Library, IEEE Xplore, and Google Scholar. Studies published between 2022 and 2026 were screened using predefined inclusion and exclusion criteria. A total of 41 empirical studies met the eligibility requirements and were subjected to thematic synthesis. The findings reveal that AI-driven transformation of mathematics teachers’ roles occurs across seven interconnected domains: (1) traditional roles retained due to inequitable access to AI, (2) human-centred roles that remain uniquely human, (3) existing roles enhanced through AI, (4) emerging AI-related roles, (5) corrective roles necessitated by limitations of AI-generated content, (6) protective roles addressing challenges arising from AI use, and (7) professional partnership roles extending beyond classroom boundaries. Based on these findings, an AI-Driven Mathematics Teacher Role Transformation Framework is proposed. The review contributes a holistic conceptualization of teacher role transformation and provides implications for teacher education, educational policy, school leadership, and future human–AI collaboration in mathematics education.
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