Reframing AI-Supported Language Learning in Higher Education: A Systematic Literature Review of Learner, Teacher, and Institutional Roles

Abstract

Artificial intelligence (AI) is reshaping language learning in higher education through personalized learning, AI-mediated interaction, immediate feedback, and opportunities for independent practice. However, the educational value of AI depends not only on technological affordances but also on learner agency, teacher competence, and institutional capacity. This systematic literature review (SLR) therefore reframes AI-supported language learning as a multi-level educational process and synthesizes evidence concerning the interconnected roles of learners, teachers, and higher-education institutions. Following the PRISMA framework, a structured search of Scopus and Web of Science was conducted, resulting in 357 initial records. After screening, eligibility assessment, and quality appraisal, 36 studies were included in the qualitative synthesis. The findings reveal a substantial imbalance in the literature: 23 studies focused on learners, six on teachers, and seven on institutions. Learner-focused studies primarily addressed AI-enhanced language development, perceptions, motivation, engagement, autonomy, AI literacy, and self-regulated learning. Teacher-focused research highlighted AI adoption, pedagogical integration, professional development, and institutional support, whereas institution-focused studies emphasized inclusion, accessibility, ethics, infrastructure, cultural context, and digital inequality. The review demonstrates that effective AI-supported language learning cannot be reduced to technology adoption; it requires coordinated learner agency, pedagogically informed teacher practices, and institutional readiness and governance. The study consequently advocates a human-centred, inclusive, and ethically grounded approach to AI integration and calls for future longitudinal and mixed-methods research examining interactions among these three levels.