INTEGRATING ARTIFICIAL INTELLIGENCE IN ENGLISH LANGUAGE LEARNING: A COMPARATIVE STUDY OF HIGHER EDUCATION PRACTICES IN INDONESIA, CHINA, AND INDIA
Keywords:
Artificial Intelligence, English Language Learning, Higher Education, Comparative Study, Indonesia, China, IndiaAbstract
This study investigates the integration of Artificial Intelligence (AI) in English Language Learning (ELL) within higher education institutions in Indonesia, China, and India, focusing on adoption patterns, influencing factors, and perceived effectiveness. Data were collected from 450 participants, comprising 150 from each country, including EFL instructors, instructional designers, and undergraduate students enrolled in English-major or English-intensive programs. A mixed-methods design was employed, with 300 participants (100 per country) completing a structured questionnaire for the quantitative phase, and 45 participants (15 per country) participating in in-depth interviews for the qualitative phase. Quantitative analysis using descriptive statistics, one-way ANOVA, and multiple regression revealed significant cross-national differences, with China reporting the highest scores in perceived usefulness, ease of use, pedagogical integration, and institutional support, followed by India and Indonesia. Regression results indicated that perceived usefulness and pedagogical integration were the strongest predictors of AI-assisted ELL effectiveness. The qualitative findings provided contextual insights, highlighting the importance of national policy alignment, institutional readiness, and faculty training in shaping adoption outcomes. The study concludes that successful AI integration in ELL requires a context-sensitive approach that combines technological capability, pedagogical alignment, and supportive institutional ecosystems, offering both theoretical contributions to CALL and EdTech literature and practical implications for higher education policy and practice in multilingual contexts.
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