## 5 Discussion  
This study organizes the discussion around four key aspects necessary for cultivating teachers' AI Literacy: comprehensive enhancement of knowledge, skills, teaching methods, and value judgments; differentiated support across teaching phases; integration of educational decision-making and ethical judgments; and concrete strategy support based on a data support system. Firstly, the cultivation of AI Literacy in teachers should not be limited to knowledge and skills alone but should also encompass teaching methods, values, and attitudes. AI Literacy education must focus on equipping teachers with sufficient knowledge about AI and its practical applications and, importantly, on developing teaching methods that effectively integrate AI within educational contexts. This study evaluated how teachers perceive and perform AI Literacy, revealing a significant correlation (r = 0.76) between the Importance of Knowledge in AI Literacy and the Importance of Skills in AI Literacy. This suggests that teachers who value theoretical knowledge also appreciate the importance of practical skills [60]. Therefore, AI Literacy education should support teachers in incorporating AI ethically and appropriately into education, which requires training that includes practical exercises and case studies to apply these concepts in real classroom settings [60]. Secondly, to support the effective use of AI Literacy, differentiated support and integration tailored to each teaching phase are necessary. The performance analysis of the XGBoost model applied in this study showed that the application of AI Literacy varies across teaching phases, with particularly high predictive performance noted in the Class Preparation Phase and Class Implementation Phase (R\({}^{2}\) = 0.8206, R\({}^{2}\) = 0.8153), suggesting that AI Literacy manifests clearly and specifically in these phases [76]. Conversely, the Class Application of the Assessment Results Phase showed a lower R\({}^{2}\) value (0.7746) and higher error metrics (MSE = 0.1616, RMSE = 0.4019, MAE = 0.1581), indicating the inclusion of more complex elements in this phase. Thus, teacher training should provide practical training to utilize AI technologies during the preparation and implementation phases while offering additional courses that develop skills in data interpretation, ethical considerations, and educational decision-making during the assessment and results utilization phases [77]. Thirdly, the assessment and results utilization phase should support AI Literacy that includes educational decision-making and ethical judgments. Analysis of self-assessed AI Literacy across teaching phases revealed high levels of self-assessed AI Literacy in the Class Preparation Phase and Class Implementation Phase (R\({}^{2}\) = 0.85, 0.83), with both XGBoost and SHAP analysis confirming the significant role of AI Literacy in these phases (R\({}^{2}\) = 0.82, SHAP value = 0.25). This indicates that teachers recognize the importance of AI Literacy and are effectively using it in the preparation and implementation of lessons. However, the roles of AI Literacy extend beyond mere technical application in the Class Assessment Phase and Utilizing Assessment Results Phase. While XGBoost rated the necessity of AI Literacy highly in these phases (R\({}^{2}\) = 0.76), the SHAP analysis showed comparatively lower influence (SHAP value = 0.15), suggesting that these phases involve a complex process that goes beyond simple technical analysis and includes providing individualized feedback to students and making educational decisions. Teachers must understand and clearly explain the processes involved in deriving AI evaluation results, ensure algorithm transparency and data accuracy, and prioritize student privacy. Moreover, teachers must use AI technology to assess student achievement and apply results in a way that considers the overall learning context and individual characteristics of students, making ethical decisions accordingly.