AI Insight
This study investigated factors influencing foreign language students' intentions to adopt Chinese generative AI tools using an extended Technology Acceptance Model that incorporated social factors. Analyzing data from 423 foreign language majors at Chinese universities through structural equation modeling, researchers found that peer influence and student attitudes directly predicted adoption intention, while supportive teacher attitudes had significant positive effects but restrictive peer influence did not. The findings suggest that prohibition or passive endorsement alone are insufficient strategies for promoting effective AI adoption in educational contexts.
Why it matters
The research provides evidence-based guidance for educators and institutions on how to effectively integrate generative AI tools into foreign language education. It highlights that active teacher support and positive peer dynamics are more influential than restrictive approaches in shaping student technology adoption.
Understand the Science
This study explores the key determinants shaping foreign language majors’ adoption intention toward Chinese generative artificial intelligence (GenAI) tools. Using an extended Technology Acceptance Model (TAM), it examines the underexplored roles of two critical social factors: perceived teacher attitude (PTA) and peer influence (PI). A quantitative methodology was adopted, utilizing an online questionnaire to collect data from 423 foreign language majors across multiple universities in China. The proposed theoretical model and hypothesized relationships were rigorously analyzed through structural equation modeling (SEM). The results identified PI and attitude as direct positive predictors of adoption intention. Notably, restrictive PI was not significantly associated with adoption intention, whereas supportive PTA showed a significant positive association. This research extends the TAM and implied that simple prohibitions or passive advocacy are insufficient to promote the effective application of AI. Educational stakeholders should take practical measures to go with the future trends in Human-Machine Collaboration.