Artificial Intelligence in English Language Teaching and Learning: A Scoping Review of Intelligent Computer-Assisted Language Learning (2015–2025)

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.