## 6 Limitations and Recommendations  
The study's relatively small sample size of 133 teachers is one of its limitations. Small datasets have also been demonstrated to produce satisfactory results in ANN and ANFIS in recent studies [109; 119; 120]. In an effort to improve representativeness, stratified or random sampling techniques should be used in future research to include a bigger and more varied sample. Cross-cultural comparisons and insights into the findings' universality can also be gained by including people from different educational systems and cultural backgrounds. Aside from sample size, the sample comprised solely Turkish teachers; hence, extra caution should be used when extending results to other sociocultural contexts. The dataset is acknowledged as being unlikely to produce the same results, and the authors are willing to provide the data upon request. The participant surveys' questions can be further modified and varied to reflect different demographic and cultural backgrounds. In this study, convenience sampling was used and participants were selected according to ease of access. This has led to some limitations. For example, the findings may not be representative as the sample may not reflect the diversity of the population. The results may only be valid for the specific context in which the sample was collected, limiting their validity in different settings. For these limitations of the study, we can suggest the following:  
First, we should acknowledge the sampling methodology of the study and its possible biases. As a matter of fact, in cross-sectional descriptive studies, the study is conducted with specific participants in a specific time period. When the characteristics of the participants of this study are analyzed, there is a wide range of participants from 25 years old to 73 years old. The school levels and experience levels of the teachers also vary. We can say that these diversities contribute to the generalizability of the results. The limitation of the sample requires the careful interpretation of the results. Accordingly, the interpretation of the results was as careful as possible. This research is one of the exemplary studies in terms of the application of artificial intelligence methods to mathematics education. The results of the study provide evidence for the applicability of artificial intelligence to mathematics education. This evidence obtained from a small sample will encourage further studies with larger samples. Future research could be conducted using more rigorous sampling methods such as stratified or random sampling to validate the findings in a larger population. Future studies should include objective measurements such classroom observations, peer assessments, or the examination of instructional artifacts to lessen the biases present in self-reported data. The validity and reliability of the results can be improved by cross-referencing self-reported data with information from other sources. Further research on prediction algorithms, including machine learning and deep learning algorithms, can be carried out in the future. It may be possible to increase prediction accuracy and capture more complex correlations between variables by incorporating advanced methods for machine learning, like ensemble approaches or deep learning models. Furthermore, including feature selection methods like recursive feature removal or principal component analysis can assist in determining the most significant MPSB predictors. The explanatory power of the models may be improved by broadening the range of input variables to include systemic and environmental elements like instructional resources, professional development opportunities, and school atmosphere. Longitudinal studies that monitor how teachers' beliefs evolve over time may also shed light on how dynamic MPSBs are. Conceptualization, S.G.K., A.F. and S.G.Y. ; methodology, S.G.K. and S.G.Y. ; software, S.G.K. ; validation S.G.K. and S.G.Y. ; resources, A.F. and S.G.Y. ; data curation, S.G.K. and S.G.Y.