THE POSSIBILITY OF INTEGRATING ARTIFICIAL INTELLIGENCE SERVICES INTO LANGUAGE COURSES
Keywords:
Generative AI · Large Language Models · Language Acquisition · Personalized Learning · Language Anxiety · AGILE-LE ModelAbstract
The swift advancement of Generative Artificial Intelligence (GenAI), particularly Large Language Models (LLMs), has created an urgent need for empirically validated pedagogical frameworks to guide its integration into language education. This study addresses a critical gap in Computer-Assisted Language Learning (CALL) research by proposing and testing the Adaptive Generative AI (AG-AI) Integration Model for Language Education (AGILE-LE). AGILE-LE is a novel framework that mandates a tripartite integration strategy, focusing on Cognitive Adaptivity (personalized feedback and scaffolding), Affective Moderation (reducing language anxiety through AI dialogue agents), and Pedagogical Alignment (AI use as a tool for critical production, not merely consumption). A mixed-methods experimental design was employed, involving 150 intermediate-level English as a Foreign Language (EFL) learners divided into control (traditional methods) and experimental (AGILE-LE implementation) groups over one academic semester. The findings reveal that learners using the AGILE-LE model demonstrated statistically significant improvements in both written and spoken fluency, a 35% reduction in self-reported language anxiety scores, and a demonstrably higher level of self-regulation compared to the control group. The results validate the AGILE-LE model as a robust, theoretically grounded, and highly effective pedagogical approach, representing a new scientific discovery in AI-enhanced language pedagogy. These findings offer practical, evidence-based guidelines for educators and course developers seeking to harness GenAI's transformative potential while mitigating ethical and pedagogical risks.
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