Beyond Teacher-Led Approaches: Student-Initiated Translanguaging With Artificial Intelligence Tools in Foreign Language Acquisition

This study explores student-initiated translanguaging in the English as a Foreign Language (EFL) context, particularly with AI translation tools, an area largely overlooked in favor of teacher-led approaches. Using a sequential explanatory mixed-method design, the research examined how 69 undergraduates at a Thai university, across various disciplines, integrated AI translation tools into their English learning, focusing on communicative competence within the frameworks of the Optimal Input Hypothesis and Translanguaging Theory. Data were collected through surveys, speaking assessments, and narrative frames, with quantitative data analyzed via path analysis and qualitative data through thematic analysis. The findings reveal a strong reliance on AI tools for comprehension, vocabulary acquisition, and building linguistic confidence, as indicated by high mean scores. Nonetheless, although these tools were effective in enhancing comprehension and shaping student preferences, they also showed a tendency to reduce overall usage due to increased proficiency or potential over-reliance. Qualitative results further accentuated the importance of these tools in decoding complex texts, aiding comprehension, and verifying understanding, with convenience being a major driver of their use. Despite these benefits, path analysis indicated that the impact of AI tools on speaking skills was not statistically significant (β = −.1044, p = .512), raising concerns that frequent reliance on these tools may not substantially improve speaking abilities and might even hinder deeper language mastery. These findings highlight the need for further research to better understand the comprehensive effects of AI-powered translanguaging on language learning outcomes.