INFLUENCE OF AI-DRIVEN ADMISSION TOOLS’ PERCEIVED USEFULNESS, AI PERSONALIZATION, TRUST IN AI COMMUNICATION, AND PRIVACY CONCERN ON INTERNATIONAL STUDENTS’ APPLICATION INTENTION
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This study aims to: 1) explore the impact of perceived usefulness, AI-driven personalization, trust in AI communication, and privacy concerns on international students' willingness to apply to private universities in China; 2) identify the strongest positive and negative predictors of application intention in the context of AI-driven admissions services; and 3) provide practical suggestions for improving the application of AI admissions tools in international student recruitment. This study employed a cross-sectional quantitative research design, collecting data from 400 potential international students who had used AI admissions services. Data analysis methods included descriptive statistics, reliability analysis, correlation analysis, and multiple regression analysis. The results show that: 1) perceived usefulness, AI-driven personalization, and trust in AI communication have a positive impact on international students' application intention; 2) privacy concerns have a negative impact on application intention; and 3) the regression model explains 55.1% of the variance in application intention, indicating strong explanatory power. Among all predictor’s, perceived usefulness was the strongest positive factor, while privacy concerns were the most significant negative factor. The findings enrich the research literature on the application of artificial intelligence (AI) in higher education admissions and provide practical suggestions for improving AI-powered admissions services in Chinese private universities, including enhancing functional accuracy, personalized communication, trust mechanisms, and privacy protection.
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