Boundary Conditions of "More Use, Better Outcome": The Role of Self-Regulation in AI-Assisted Oral Proficiency
TL;DR
Findings reframe the more use, better outcome logic by revealing a dual-path mechanism: usage frequency ensures the floor of language exposure, while AI learning engagement willingness determines the ceiling of deep processing.
Abstract
The rapid development of generative AI (GenAI) technology has led to the widespread adoption of AI oral practice tools among university EFL (English as a Foreign Language) learners, yet the boundary conditions under which such tools improve oral proficiency remain underexplored. This study aims to investigate whether the assumption that more frequent use leads to better outcomes is universally valid or is conditioned by learners’ AI learning engagement willingness. Based on questionnaire responses from 248 university students across multiple institutions in China, this study examined the relationships among AI oral tool usage frequency, AI learning engagement willingness (comprising task value perception, willingness to invest resources, and AI-augmented self-efficacy), and self-reported English oral proficiency. Statistical analyses, including one-way ANOVA and hierarchical multiple regression, revealed that both AI learning engagement willingness and usage frequency were independently and positively associated with oral proficiency, displaying an additive rather than interactive relationship. These findings reframe the more use, better outcome logic by revealing a dual-path mechanism: usage frequency ensures the floor of language exposure, while AI learning engagement willingness determines the ceiling of deep processing. The study provides theoretical and practical implications for AI tool design and pedagogical implementation in college English education.