## 4 Result
### Feature Importance Analysis Using XGBoost and Comparative Review with SHAP
#### 4.2.3 Feature Importance Analysis for AI Literacy in Class Assessment Phase  
The Feature Importance Analysis for AI Literacy in the Class Assessment Phase is detailed in [Table 8, Figure 6]. In the regression analysis with XGBoost, the feature \"Importance of AI Literacy in Class Assessment Phase\" recorded the highest Feature Importance value (0.1973). Subsequent features include \"Importance of AI Literacy in Class Preparation Phase\" (0.1553), \"Importance of Teaching Methods in AI Literacy for AI-Integrated Education\" (0.1333), \"Importance of Skills in AI Literacy for AI-Integrated Education\" (0.0733), and \"Importance of AI Literacy in Class Implementation Phase\" (0.0638). Comparative analysis with SHAP showed that some features had higher importance in XGBoost but lower mean absolute values in SHAP. Specifically, the feature \"Importance of AI Literacy in Class Application of Assessment Results Phase\" recorded lower mean values in SHAP (XGBoost: 0.0624, SHAP: 0.0630). Conversely, features such as \"Importance of Skills in AI Literacy for AI-Integrated Education\" (XGBoost: 0.0733, SHAP: 0.1005), \"Perceived Need for AI Literacy Learning Level for Students\" (XGBoost: 0.0569, SHAP: \begin{table}
\begin{tabular}{c c c} \hline
**Feature** & **XGBoost** & **SHAP** \\
**Feature Importance** & **Mean Absolute Value** \\ \hline Importance of AI Literacy in Class Assessment Phase & 0.1973 & 0.1474 \\ \hline Importance of AI Literacy in Class Preparation Phase & 0.1553 & 0.1056 \\ \hline Importance of Teaching Methods in AI Literacy for AI-Integrated Education & 0.1333 & 0.0978 \\ \hline Importance of Skills in AI Literacy for AI-Integrated Education & 0.0733 & 0.1005 \\ \hline Importance of AI Literacy in Class Implementation Phase & 0.0638 & 0.0607 \\ \hline Importance of AI Literacy in Class Application of Assessment Results Phase & 0.0624 & 0.0630 \\ \hline Importance of Skills in AI Literacy & 0.0621 & 0.0897 \\ \hline Perceived Need for AI Literacy Learning Level for Students & 0.0569 & 0.0841 \\ \hline Importance of Knowledge in AI Literacy & 0.0531 & 0.0604 \\ \hline Importance of Knowledge in AI Literacy for AI-Integrated Education & 0.0529 & 0.0514 \\ \hline Importance of Teaching Methods in AI Literacy & 0.0481 & 0.0605 \\ \hline Perceived Need for AI Literacy Learning Level for Teachers & 0.0416 & 0.0745 \\ \hline \end{tabular}
\end{table} 
Table 8: Feature Importance of AI Literacy in the Class Assessment Phase. The background color is used to highlight cases with higher values. 0.0841), and \"Perceived Need for AI Literacy Learning Level for Teachers\" (XGBoost: 0.0416, SHAP: 0.0745) displayed higher mean absolute values in SHAP analysis, indicating a discrepancy between the models in assessing feature contributions. The deeper examination of SHAP values helps explain why these discrepancies occur and illuminates interaction effects among features. In this phase, factors like teachers' technical skill orientation and perceived AI literacy needs turned out to have a greater influence than their XGBoost ranks suggested. For instance, the importance of AI literacy skills (for AI-integrated education) and the perceived need for AI literacy training (for both students and teachers) show higher SHAP impact, implying that teachers who focus on practical AI skills or who are acutely aware of the need for AI literacy tend to perform differently in assessment tasks. These features likely drive significant outcome differences for certain subsets of teachers (e.g., those with very high or very low emphasis on skills and needs), which may be why SHAP attributes them more significance than the averaged XGBoost importance. In contrast, the value a teacher places on using AI in the results-utilization phase--while recognized by XGBoost--has a less uniform effect (lower average SHAP value), perhaps because its influence is only realized in conjunction with other competencies.