Educators and AI in Collaboration: Enhancing Multilingual Teaching and Learning in Higher Education Through Natural Language Processing

Abstract

The increasing linguistic diversity in higher education has led to a growing need for inclusive and effective multilingual instructional strategies. This conceptual paper examines the potential of Natural Language Processing (NLP) to support multilingual instruction by addressing the communication barriers faced by non-native speakers in diverse classroom settings. Drawing on current theoretical perspectives and technological developments, the paper explores how NLP tools—such as real-time translation, speech recognition, and automated feedback systems—can enhance comprehension, accessibility, and collaborative learning in higher education. These technologies offer opportunities to bridge language gaps, enabling students to engage more effectively with content delivered in multiple languages. The paper also discusses challenges related to language diversity, technological limitations, and cultural sensitivity in implementing NLP in educational contexts. While acknowledging these limitations, the paper argues that integrating NLP into multilingual instruction represents a promising direction for fostering inclusivity and equity in higher education. Ultimately, this theoretical exploration urges institutions to embrace innovative language technologies to create more dynamic and supportive learning environments for students of all linguistic backgrounds.