The rapid integration of artificial intelligence (AI) into higher education has significantly transformed career planning services, particularly in private colleges in China where institutional resources are often limited. Despite the growing application of AI-driven career planning tools, existing studies have predominantly focused on their effectiveness in improving career outcomes, while insufficient attention has been given to the behavioral mechanisms that influence students' adoption of such technologies. Addressing this gap, this study aims to examine the determinants of students' adoption of AI-driven career planning tools by proposing a mediation model that incorporates perceived usefulness, trust in AI, user engagement, and adoption intention. Specifically, the study aims to identify the relationships among perceived usefulness, trust in AI, user engagement, and adoption intention, as well as to examine the mediating role of user engagement in the AI adoption process. Grounded in the Technology Acceptance Model (TAM) and Self-Determination Theory (SDT), a quantitative cross-sectional survey design was employed using a structured questionnaire with a five-point Likert scale administered to undergraduate students in private colleges in Shaanxi Province, China. A total of 320 valid responses were collected and analyzed using SPSS and Structural Equation Modeling (SEM) to test the proposed hypotheses and mediation effects. The findings reveal that perceived usefulness and trust in AI have significant positive effects on students' adoption intention. In addition, both variables significantly predict user engagement, which in turn exerts a strong positive influence on adoption intention. The mediation analysis further confirms that user engagement partially mediates the relationship between perceived usefulness, trust, and adoption intention, indicating that students' active involvement plays a crucial role in transforming technological perceptions into actual behavioral intentions. Future studies are encouraged to examine additional psychological and contextual variables influencing AI adoption across different higher education environments.
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