DIGITAL SEMANTIC STABILIZATION IN AI - MEDIATED ENGLISH LEARNING: IMPLICATIONS FOR HIGHER EDUCATION

บทคัดย่อ

This paper examines how semantic meaning is constructed and stabilized in AI - mediated English learning environments. As artificial intelligence increasingly functions as a learning agent in language education, understanding the underlying cognitive and linguistic mechanisms of meaning development has become essential. However, existing studies have largely focused on performance outcomes and technology adoption, leaving the processes of semantic construction underexplored. To address this gap, this paper proposes the concept of Digital Semantic Stabilization (DSS) as a theoretical framework for explaining how learners construct, negotiate, and stabilize meaning through interaction with AI systems. The framework conceptualizes meaning development as a dynamic process shaped by iterative cycles of inquiry, AI - generated feedback, verification, and consolidation within a learner - AI interaction loop.

The DSS model identifies four possible patterns of semantic stabilization: simplified, equivalent, contextual, and distorted stabilization. These patterns reflect varying levels of cognitive engagement and semantic accuracy in AI - mediated English learning. While AI enhances accessibility, autonomy, and linguistic support, it also introduces risks related to cognitive offloading and superficial understanding. This paper contributes to applied linguistics and digital pedagogy by offering a new perspective on semantic development in AI - mediated learning and by highlighting the importance of critical AI literacy and structured meaning verification in contemporary English education.