## 5 Discussion and Conclusions
Teachers' answers to the information form, Mathematical Problem-Solving Beliefs Scale, and Marmara Creative Thinking Dispositions Scale were used to collect real data. This stage, in which the MPSB scores of the teachers are predicted based on their CTD, gender, age, educational level, school level, and teaching experience variables, is related to the first research problem. In particular, any academic program that deals with teachers' mathematical problem-solving skills can use the artificial intelligence tools designed in this research. This next generation approach can be used to identify qualified teachers to prepare students for twenty-first century skills. The real data used in this study were collected from schools; that is, from the field of education. This means that they are not data that exist in any information system or are readily available in a database. It is believed that this situation significantly adds to the research's validity. After the operation processes of artificial intelligence models, the participants' MPSB scores obtained artificially with ANN and ANFIS were obtained. For the second research problem, the real MPSB scores obtained from the answers given by the teachers to the \"Mathematical Problem-Solving Beliefs Scale\" and the artificial MPSB scores obtained with the ANFIS and ANN approaches were compared. There is a statistically significant, positive, and moderate relationship between teachers' real MPSB scores and both the artificially obtained ANN scores and ANFIS scores. Therefore, both the ANFIS and ANN models predict results close to teachers' real MPSB scores. In a similar study, [PERSON] and [PERSON] found a statistically significant but low-level relationship between the artificial scores obtained with the ANFIS and the real scores [118]. They attributed the low relationship to the estimation of artificial scores according to two variables. The inputs of the current study are six. The moderate level of relationship between the artificial scores and real scores may depend on the number of input variables. The prediction models of teachers' MPSBs were investigated for future teaching plans and approaches to problem solving. In light of the findings of this investigation, it was concluded that the MPSBs of teachers were predictable by the parameters used in the participant questionnaires. In conclusion, it is observed that fuzzy logic, ANN, and ANFIS methods were appropriate machine learning methods to forecast participants' skills, such as problem-solving skills, attitudes, and beliefs. On the other hand, this model, as with  
Figure 8: The process of obtaining results that can predict scores with AI methods. other predictive models, should not imply that teachers with relatively low mathematical problem-solving beliefs will have negative knowledge, skills, or feelings toward problem solving. As a result, the research suggests that ANN and ANFIS algorithms are significant approaches for accurately and effectively forecasting teachers' MPSBs. The results of this study highlight how artificial intelligence is increasingly being used to improve educational research. The study creates novel opportunities for incorporating technology into educational research by proving that ANN and ANFIS models can be used to predict teachers' beliefs. The results demonstrate how AI techniques can help educators and policymakers determine trends and adjust decisions to promote professional development. Practically speaking, predicting MPSBs based on demographic and dispositional inputs can assist in identifying teachers who might benefit from specific training or resources to improve their teaching effectiveness and problem-solving beliefs. This predictive capability is in line with the larger objectives of 21 st-century education, which emphasize evidence-based decision-making and personalized learning. The study sheds light on the connection between problem-solving beliefs and creative thinking dispositions, particularly in the context of mathematics education.