Trust-Driven Adoption of Ai-Powered Translation Tools Among Arabic Language Students In Malaysian Higher Education
DOI:
https://doi.org/10.37134/Keywords:
Arabic language learning , Artificial Intelligence, technology acceptance model , perceived trust , perceived risk, behavioral intentionAbstract
Generative artificial intelligence (GenAI) translation tools are increasingly embedded in language-learning practices, yet their adoption in Arabic-language education raises questions about reliability, opacity, and risk. This study examines the factors shaping Arabic-language undergraduates' behavioral intention to use AI-supported translation tools in Malaysian public universities. An extended Technology Acceptance Model was evaluated using survey data from 300 valid respondents across four institutions. The model incorporated perceived usefulness, perceived ease of use, perceived intelligence, perceived trust, and perceived risk. Partial least squares structural equation modelling showed that perceived trust was the strongest direct predictor of behavioral intention (β = 0.299, p < 0.001), followed by perceived risk (β = 0.207, p < 0.001) and perceived usefulness (β = 0.163, p = 0.044). Perceived ease of use and perceived intelligence had no significant direct effects on intention. However, perceived trust fully mediatedthe relationship between perceived intelligence and behavioral intention (β = 0.143, p = 0.002) and partially mediated the relationship between perceived usefulness and behavioral intention (β = 0.065, p = 0.029). The unexpected positive association between perceived risk and intention suggests a possible form of calculated pragmatism: students may remain willing to use AI translation tools while recognizing their limitations. The findings support trust calibration, critical AI literacy, and institutionally governed use of GenAI in Arabic-language education.
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