Institute of Educational

The aim of the present study was to investigate the relationship between ethical climate and organizational citizenship behavior with the mediating role of organizational justice among high school teachers in Tehran. included all teachers (382 people) of high school teachers in Tehran were selected by Multi-stage cluster sampling method. For data collection were used the three Questionnaire of Ethical Climate (Victor& Collen, 2006), Lee & Allen (2004 Organizational citizenship behavior, and organizational justice (Moorman and Niehoff, 2001). Data analysis was done by using structural equation modeling in AMOS And Spss-₂₂  software. The results showed that there is a positive and significant correlation (0.56) between the ethical atmosphere of the organization and the citizenship behavior of teachers. Organizational justice also has an influential role (0.25) in relation to the moral climate and organizational citizenship behavior of teachers. The more positive and meaningful the ethical climate in schools, the better organizational justice will be and the better and more effective citizenship behavior teachers will have. In general, it can be concluded that considering the direct and positive effect of ethical atmosphere on organizational citizenship behavior, it is necessary for school principals to take effective measures to create a favorable ethical atmosphere in schools to improve and enhance teachers' citizenship behavior.

Advancement in technology such as the Artificial Intelligence provides an opportunity to help teachers and students solve problems and improve teaching and learning functioning. This research thus aims to investigate the artificial intelligence functioning in quality-giving to teaching in math teaching to students. This basic research was conducted using a research synthesis method. The research population consists of documents retrieved by searching authentic Persian and Latin databases (all documents published from 2013 to June, 2024, n=110). The most relevant resources (n=71) were reviewed and materials were extracted with regard to the research objective. The thematic analysis method was used to analyze the data. Finally, to validate the proposed model, a total of six experts in different fields, such as curriculum and educational planning, educational technology, and electronic learning were selected using a purposive sampling method. The researcher-made questionnaire for content validation of conceptual model was then sent for them. The final model was approved after modifications. Two principal components, including enriching learning environment (with the sub-components: enhancing individual knowledge, personalizing learning, constructive and motivational support, creating communication networks, performance analysis and evaluation) and professional empowering teachers (with the sub-components: helping with identifying learning disorders, helping teachers with educational process, and enriching educational tools) were obtained. Finally, it can be said that artificial intelligence technology has evolved the way of math learning and understanding for learners. In conclusion, it can be said that artificial intelligence technology has revolutionized the way in which learners understand and learn complex mathematical topics.