# The Impact of Digital Safety Competence on Cognitive Competence, AI Self-Efficacy, and Character
## 3 Results  
Descriptive statistics and correlation analyses of all variables are presented in Table 1. The distribution of all variables is in the acceptable range, with skewness values ranging from 0.13 to 0.91 and kurtosis values ranging from \(-\)0.02 to 1.63 [83].  
Table 1 shows the relationships among all variables. Digital safety competence is positively associated with AI ethics (\(r=0.44,p<0.01\)), AI self-efficacy (\(r=0.29,p<0.05\)), cognitive competence (\(r=0.29,p<0.01\)), and moral competence (\(r=0.17,p<0.05\)). AI ethics is positively related to AI self-efficacy (\(r=0.24,p<0.01\)) and cognitive competence (\(r=0.16,p<0.05\)) but not moral competence (\(p>0.05\)). Finally, moral competence is positively correlated with cognitive competence (\(r=0.29,p<0.01\)) but not significant with AI-related variables (\(p>0.05\)).  
The structural modeling analysis (SEM) was used to test the relationships between DSC, higher-order thinking skills (cognitive competence, moral competence), and AI-related outcomes (AI self-efficacy, AI ethics). This model shows a good fit (X\({}^{2}\): 192.721, df = 134, RMSEA = 0.058 (90%CI: 0.039-0.076), CFI = 0.902, SRMR = 0.084). As predicted, digital safety competence demonstrates a positive impact on cognitive competence (\(\beta=0.58,p<0.01\)), moral competence (\(\beta=0.98,p<0.01\)), AI self-efficacy (\(\beta=0.29,p<0.01\)), and AI ethics (\(\beta=0.30,p<0.01\)), confirming all hypotheses. All paths are displayed in Figure 2.  
We took the reviewer's suggestion and examined the potential nonlinear effects of all outcome variables. The results of multiple regression analysis show that the nonlinear effects of DSC are significant for cognitive competence (unstandardized beta = 0.12; standardized beta = 1.30, \(p=0.04\)) and moral competence (unstandardized beta = 0.17;  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline  & **M** & **SD** & **\(\alpha\)** & **Skewness** & **Kurtosis** & **1** & **2** & **3** & **4** & **5** \\ \hline
1. DSC & 4.12 & 0.71 & 0.81 & 0.71 & \(-\)0.01 & - & & & & \\
2. AIET & 3.85 & 0.91 & - & 0.91 & 0.58 & 0.44 ** & - & & & \\
3. MC & 3.24 & 0.57 & 0.48 & 0.57 & 0.62 & 0.17 * & 0.03 & - & & \\
4. AISE & 3.36 & 0.81 & 0.87 & 0.81 & 1.63 & 0.29 ** & 0.24 ** & 0.03 & - & \\
5. CC & 3.88 & 0.56 & 0.73 & 0.56 & \(-\)0.02 & 0.29 ** & 0.16 * & 0.29 ** & 0.13 & - \\ \hline \end{tabular} DSC: digital safety competence; AIET: artificial intelligence ethics; MC: moral competence; AISE: artificial intelligence self-efficacy; CC: cognitive competence **\(p<0.01\); * \(p<0.05\).  
\end{table}
Table 1: Descriptive statistics and correlations for all variables.  
Figure 2: The impact of digital safety competence on cognitive competence, moral competence, AI self-efficacy, and AI ethics. All paths are standardized coefficients. \({}^{**}p<0.01\).  
standardized beta = 1.69, \(p\) = 0.02) but not for the two AI-related variables (\(p\) > 0.05). To further investigate whether there is a minimum level of DSC required before its effects are manifested, we used linear mixed models. Results of fit indices (BIC and AIC) indicated the nonlinear model with quadratic term demonstrated a better fit than the linear model (see Table 2). Based on the estimates of the linear and quadratic coefficients for DSC on cognitive competence and moral competence, we computed the turning points and found that the effects of DSC reach its minimum at 3.14 and 3.70, respectively.