Modeling Students’ Acceptance of AI-Assisted Translation Tools: An Extension of the Technology Acceptance Model

Wang Xiaoyun1, Vladan Devedžić2,3,4, Wan Yinjia1

  1. Shandong Jiaotong University
    Jinan, China
    wangxiaoyun167@gmail.com
  2. Key Laboratory of Computing Power Network and Information Security, Ministry of Education,
    Shandong Computer Science Center (National Supercomputer Center in Jinan), Qilu University of Technology (Shandong Academy of Sciences), Jinan, China
  3. Faculty of Organisational Sciences, University of Belgrade
    Jove Ilica 154, 11000 Belgrade, Serbia
    vladan.devedzic@fon.bg.ac.rs (corresponding author)
  4. Serbian Academy of Sciences and Arts, Knez Mihailova 35, Belgrade, Serbia

Abstract

This study investigates students’ acceptance of AI-assisted translation tools by proposing an extended Technology Acceptance Model (TAM-AI) for AI-assisted 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.

Key words

Technology Acceptance Model, TAM-AI, AI-Assisted Translation, Students’ Acceptance, Translation Learning, Translation Tool Design

Digital Object Identifier (DOI)

https://doi.org/10.2298/CSIS260112034X

Publication information

Volume 23, Issue 4 (September 2026)
Year of Publication: 2026
ISSN: 2406-1018 (Online)
Publisher: ComSIS Consortium

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How to cite

Wang, X., Devedžić, V., Wan, Y.: Modeling Students’ Acceptance of AI-Assisted Translation Tools: An Extension of the Technology Acceptance Model. Computer Science and Information Systems, 23(4) (2026). https://doi.org/10.2298/CSIS260112034X