Artificial Intelligence for Language Learning & Linguistics

Abstract

Artificial Intelligence (AI) has emerged as a transformative force in both language learning and linguistic research, reshaping how languages are taught, acquired, analyzed, and preserved. This chapter explores the interdisciplinary integration of AI with language education and linguistics, highlighting how advances in Natural Language Processing, machine learning, speech recognition, and data-driven modeling address long-standing challenges related to linguistic complexity, learner diversity, and large-scale language analysis. In language learning contexts, AI enables personalized instruction, intelligent tutoring systems, automated assessment, and real-time feedback, fostering learner autonomy, engagement, and differentiated pedagogy. These innovations support more inclusive and adaptive learning environments, particularly in second and foreign language acquisition. From a linguistic perspective, AI has revolutionized research methodologies by facilitating large-scale corpus analysis, computational modeling of language structure and acquisition, multilingual and translation studies, and the documentation and preservation of endangered languages. The chapter also emphasizes the reciprocal relationship between linguistic theory and AI, demonstrating how theoretical insights inform AI design while computational models expand empirical linguistic inquiry. Alongside these opportunities, critical ethical and linguistic challenges—such as data privacy, algorithmic bias, overstandardization, and the potential reduction of human interaction—are examined, underscoring the need for responsible and human-centered AI integration. By synthesizing educational, theoretical, and ethical perspectives, this chapter positions AI not as a replacement for human expertise but as a complementary tool that enhances language learning outcomes, deepens linguistic understanding, and supports linguistic diversity in an increasingly globalized and digital world.