Modeling students’ acceptance of AI-assisted translation tools: An extension of the technology acceptance model

Abstract

This study investigates students’ acceptance of AI-assisted translation tools by proposing an extended Technology Acceptance Model (TAM-AI) for AIassisted translation learning contexts. Unlike prior additive approaches, the proposed model explains how translation-specific perceptions influence behavioral intention (BI) through underlying cognitive mechanisms. A survey was conducted among undergraduate translation students and analyzed using Structural Equation Modeling (SEM). The results indicate that perceived usefulness (PU) and perceived ease of use (PEOU) significantly predict BI. In addition, translation-specific factors influence technology acceptance indirectly: perceived translation quality operates through trust, feedback clarity through cognitive understanding, and cognitive load reduction through effort reduction. The TAM-AI model demonstrates greater explanatory power than the baseline TAM. These findings provide a deeper understanding of technology acceptance in AI-assisted learning environments and offer practical implications for the design of AI-assisted translation tools. Future studies with larger and more diverse samples are encouraged to further validate the proposed model.