From anxiety to adaptation: A study on the relationship between college students' AI interaction experience and mental health in higher education.

In addition to the successful application of technology in empowering education through generative artificial intelligence, there may be certain dangers posed to the mental well-being of college students due to their pressure to adapt to technology. With the help of the Job Demands-Resources model (JD-R) and the Conservation of Resources theory (COR), we propose an analysis framework that evaluates the association between the experience of interaction with generative artificial intelligence and the mental health of college students. By analyzing a series of 902 empirical data sets, we have found that AI anxiety is negatively associated with the mental well-being of college students. The experience of adjusting to and being effective at using AI can partly explain the adverse consequences of AI anxiety. Meanwhile, this relationship does not represent a straightforward linear reduction of anxiety. Instead, it indicates a U-shaped pattern, which implies that people who are more experienced in using technologies are likely to encounter a more complicated situation of technical and social anxiety. Our research has extended the current view from educational psychology by adding the concept of data compliance and legal governance. In turn, our empirical findings will enable universities to adopt better digital teaching intervention strategies to minimize privacy and security risks when implementing artificial intelligence in the field of education.