BRIDGING LANGUAGE LEARNING AND AI TECHNOLOGIES IN HIGHER EDUCATION: INSIGHTS FROM A COMPREHENSIVE LITERATURE REVIEW

Abstract

The vast incorporation of Artificial Intelligence (AI) into higher education, particularly in language classes, has resulted to enthusiasm and challenges. While AI technologies such as Natural Language Processing (NLP), Intelligent Tutoring Systems (ITS), and automated feedback mechanisms have transformative potential, their implementation faces substantial obstacles, including technical infrastructure issues, ethical concerns, and pedagogical limitations, particularly in resource-constrained regions of the Global South. This exploration aims to highlight a comprehensive review of the current state, advantages, challenges, and future directions of AI in language learning within higher education. Through a comprehensive analysis of existing literature, the study identifies key findings: AI tools have significantly enhanced learning outcomes by offering personalized, adaptive, and interactive experiences, improving pronunciation, vocabulary acquisition, and communication skills while positively influencing affective factors such as motivation and reduced anxiety. However, challenges such as algorithmic biases, over-reliance on technology, and inequitable access highlight the need for robust frameworks to guide ethical and effective AI integration. The study’s findings are far-reaching, with concrete recommendations for educators, politicians, and developers looking to establish inclusive and sustainable AI-enhanced language learning settings. Educators are encouraged to find a middle ground between technology and human interaction, while governments should prioritize infrastructure and regulatory frameworks. Developers are urged to design culturally sensitive and resilient AI systems. Despite its contributions, the study acknowledges limitations, such as the lack of longitudinal data and underrepresentation of diverse contexts. Future research should focus on longitudinal studies, adversarial machine learning, and inclusive investigations to address these gaps. This study underscores the progressive influence of AI in language learning, while emphasizing the importance of addressing technical, ethical, and pedagogical barriers to ensure equitable access and sustained impact.