AI and English Language Teaching Research Gaps: A Systematic Review

Abstract

Artificial Intelligence (AI) has emerged as a central driver of innovation in English  Language Teaching (ELT), yet despite widespread adoption of AI-enabled tools, the field of research remains scattered and under-theorized. This systematic review synthesizes peer-reviewed literature published between 2015 and 2025 to identify the state of current research and reveal the key gaps that continue to limit knowledge development. Guided by PRISMA principles, 112 studies were initially retrieved from Scopus, Web of Science, ERIC, and Google Scholar. Following a rigorous inclusion and exclusion process, 48 studies were analyzed using thematic synthesis. The review identifies six major gaps in existing research: the lack of pedagogical integration of AI tools with established second language acquisition (SLA) frameworks, the overconcentration of research in technologically advanced nations with limited evidence from Global South contexts, the neglect of cognitive and affective variables such as cognitive load, motivation, and learner anxiety, the scarcity of longitudinal and experimental research designs, the insufficient focus on equity and accessibility in diverse learning environments, and the absence of robust investigations into ethical challenges such as plagiarism, data privacy,  and algorithmic bias. The results indicate that although AI holds great promise for revolutionizing second language acquisition, the current body of research falls short in offering thorough or fair insights. The article ends with a research agenda for the future that prioritizes theory-driven, longitudinal, and contextually inclusive methods, especially in high-stakes test preparation contexts like India’s IELTS.