Artificial intelligence attitudes and mental health in Chinese university students: Nonlinear relationships and the mediating role of self-efficacy

Figure 1  Distribution of attitudes toward AI use, ranging from “strongly disagree” to “strongly agree”.

Figure 2 Linear regression results for the associations among AI attitudes, self-efficacy, and psychological outcomes. Model 1: Psychological outcomes regressed on AI attitudes and basic demographic covariates. Model 2: Psychological outcomes regressed on AI attitudes, basic demographic covariates, and self-efficacy.

Figure 3  Nonlinear relationships between AI attitudes and mental health outcomes, estimated using restricted cubic splines.

Figure 4 Path analysis results showing the mediating role of self-efficacy in the relationships between AI attitudes and mental health outcomes. A: Self-efficacy mediates the relationship between increased anxiety and mental health outcomes; B: Self-efficacy mediates the relationship between self-doubt and mental health outcomes; C: Self-efficacy mediates the relationship between increased procrastination and mental health outcomes. All models demonstrated good fit, with 2 < CMIN/DF < 3, CFI > 0.95, TLI > 0.94, RMSEA < 0.050, and NFI > 0.95. All models were adjusted for basic demographic variables. ^cP < 0.001. NSSI: Non-suicidal self-injury.