An Analysis of the Mechanism by Which Human-Machine Trust Influences AI-Assisted English Deep Learning

Abstract

With the rapid advancement of artificial intelligence technology, AI applications in education have become increasingly widespread, and AI-assisted English learning has emerged as a prevalent instructional approach. In this context, human-computer trust—a critical factor influencing learning outcomes—has gained heightened significance. This study aims to thoroughly examine the mechanisms by which human-computer trust affects deep learning in AI-assisted English instruction. Through questionnaire surveys, data on learners 'trust levels toward AI and their English learning progress were collected, complemented by interviews to gain insights into learners' experiences and perceptions. The research identifies specific mechanisms through which human-computer trust impacts learning motivation, strategy adoption, and learning outcomes. Based on these findings, the study proposes strategies to enhance human-computer trust—including optimizing AI technology, refining instructional design, and guiding learners—to provide theoretical foundations and practical recommendations for improving the effectiveness of AI-assisted deep learning in English education.