## 4 Result
### Feature Importance Analysis Using XGBoost and Comparative Review with SHAP
#### 4.2.4 Feature Importance Analysis for AI Literacy for Class Application of Assessment Results Phase  
The Feature Importance Analysis for the Class Application of Assessment Results Phase is as follows [see Table 9, Figure 7]. The regression analysis with XGBoost indicates that the feature \"Importance of AI Literacy in Class Application of Assessment Results Phase\" recorded the highest Feature Importance value (0.2117). This was followed by \"Importance of Teaching Methods in AI Literacy for AI-Integrated Education\" (0.1868), \"Importance of AI Literacy in Class Preparation Phase\" (0.0899), \"Importance of Skills in AI Literacy for AI-Integrated Education\" (0.0788), and \"Importance of Skills in AI Literacy\" (0.0724). Figure 7: Feature Importance Comparison: XGBoost and SHAP Analysis for AI Literacy for Class Application of Assessment Results Phase Results. In comparison with SHAP analysis, certain features, such as \"Importance of AI Literacy in Class Assessment Phase\", displayed lower mean absolute values in SHAP (XGBoost: 0.0600, SHAP: 0.0462). Conversely, features like \"Importance of Skills in AI Literacy for AI-Integrated Education\" (XGBoost: 0.0788, SHAP: 0.0959), \"Importance of Skills in AI Literacy\" (XGBoost: 0.0724, SHAP: 0.0972), and \"Perceived Need for AI Literacy Learning Level for Teachers\" (XGBoost: 0.0395, SHAP: 0.0612) showed higher mean absolute values in SHAP analysis, suggesting a variance in how the models assess contributions of features. By delving into the SHAP outcomes, we can interpret why some variables assume greater or lesser importance in this phase and identify how they interact. The notably lower SHAP contributions for the top XGBoost features (e.g., the importance placed on AI Literacy in the results phase and on AI-integrated teaching methods) imply that these factors, while critical, do not uniformly translate into performance gains unless certain underlying conditions are met. In other words, a teacher's stated priority on using AI in applying assessment results or on innovative teaching methods must be backed by other capacities to fully impact their practice. Supporting this, several foundational competencies and perspectives--such as AI Literacy skills and the teacher's self-identified need for AI learning--exhibited higher SHAP influence than their XGBoost rankings would suggest. This highlights that a teacher's ability to effectively utilize AI when applying assessment results is strongly driven by their actual skill level and awareness of their own learning needs, which can outweigh the influence of simply valuing AI use in that phase. Furthermore, SHAP dependence plots point to key interactions: the benefit of valuing AI in the results-application phase is significantly amplified for teachers with high technical proficiency. Teachers who both consider AI important for using assessment results and possess strong AI Literacy skills achieve much greater predicted performance in this phase (manifested as higher SHAP values for the combination of these features), whereas those lacking in skills gain little from merely holding that belief. Another interaction is observed between pedagogical approach and self-reflection: the positive effect of emphasizing AI-integrated teaching methods on utilizing assessment results is stronger when a teacher also recognizes a high personal need for AI Literacy development. This suggests that educators who are both methodologically innovative and attuned to improving their own AI capabilities can more effectively translate student assessment data into actionable insights using AI. In sum, the SHAP analysis for the results-utilization phase reveals that successful integration of AI in post-assessment activities depends not only on acknowledging the importance of AI