THE ROLE OF ARTIFICIAL INTELLIGENCE IN EAST ASIAN LANGUAGE EDUCATION: PEDAGOGICAL POTENTIALS AND STRUCTURAL CONSTRAINTS

Abstract

Artificial intelligence (AI) has become an increasingly significant component in computer-assisted language learning (CALL) and intelligent tutoring systems (ITS). In East Asian language education, including Japanese, Chinese, and Korean, AI technologies are expected to enhance individualized instruction, automated assessment, and learner engagement. However, empirical evidence from non-Western higher education contexts remains limited. This study examines the pedagogical affordances and structural constraints of AI integration in East Asian language education, with a focus on Japanese language learning in higher education settings. The analysis is grounded in second language acquisition (SLA) theories, particularly the declarative–procedural model, the Output Hypothesis, and interactionist perspectives. Findings suggest that AI systems primarily support input enhancement, adaptive learning pathways, and formative feedback mechanisms. However, limitations persist in the modeling of pragmatic meaning, discourse-level competence, and interactional features such as timing, pause, and contextual inference. The study concludes that AI should be positioned as a complementary component within a teacher-mediated instructional ecology rather than as a substitute for human instruction.