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Purpose: education plays a key role in empowerment of human resources. Thus, the present research was done to present and validate the human resources empowerment model with emphasis on education in the ornamental and building stone industry. Methodology: this descriptive-analytical study was of correlation type. The statistical population consisted of the staff at Iran's stone statistics and information center as many as 425 persons in 50 companies. The sample size was determined 200 according to Cochran formula; this number was chosen through stratified random sampling method based on the occupational unit. The research instruments included demographic information form capturing gender, age, level of education, years of working background, and a researcher-made questionnaire ( 50 items), whose face validity was confirmed based on expert opinion, while its reliability was calculated using Cronbach alpha method on 30 staff as 0.95. The data were analyzed by exploratory factor analysis and structural equations modeling methods in SPSS-25 and Smart PLS-3 software. Findings: the findings indicated that empowerment of human resources has 10 factors of change and innovation, managerial functions, learning, development of human resources, behavioral factors, organizational factors, the strategic factors of organization, environmental factors, cultural and leadership factors as well as knowledge beyond the technical knowledge. The factor load of all of its items was above 0.40; the convergent validity of the factors was found above 0.60 using the average variance extracted method. The reliability of all of the factors using Cronbach alpha and mixed method was above 0.70. Furthermore, the human resources empowerment model with emphasis on education in the ornamental and building stone industry had suitable and acceptable fitting, and the empowerment of human resources with emphasis on education had a direct and significant effect on all of the 10 factors (P<0.001). Conclusion: given the suitable model of human resources empowerment with emphasis on education in the ornamental and building stone industry, experts and planners can take effective steps for empowerment of human resources through the 10 identified factors.

Purpose: The purpose of the present research is to present a brain-based curriculum model focusing on interaction and motivation in the secondary school. Methodology: In order to achieve this goal, qualitative approach, field method and semi-structured in-depth interview techniques were used. The study population included prominent curriculum planning experts, researchers and faculty members and 15 people were interviewed based on the data saturation. The data collection tools included texts and semi-structured interviews, and the method of data analysis also included inductive thematic analysis (Attride-Stirling thematic networks). Findings: The results of the research showed that the curriculum design components included four dimensions: goals, content, teaching methods, and assessment methods, which affect students' interaction and motivation. Goals included indices of changing mental images, real learning environment, flexibility of brain-based curriculum, information storage during learning. Content included indices of development of brain ability, mechanism of brain function, cognitive learning, content understanding, triggering body and brain activity, teaching methods including practical learning, learning in the environment, cooperation in class activity, different learning style, getting students to take responsibility for learning, effectiveness of education, purposeful and conscious teaching, continuous information processing. Assessment method included self- assessment, understand the content easily, flexible learning, encouraging activities. The interaction included indices of sharing new experiences in learning; improving the brain ability; and discussing different topics. Stimulation of learning and motivation included the indices cognitive function of the brain, challenging and enjoyable learning. Conclusion: The results showed that the brain-based curriculum emphasizes on goals, content, teaching and assessment methods, which is designed with an emphasis on interaction and motivation.

Purpose: STEAM is the term given to a growing field of research and practice that integrates arts and social sciences into traditional sciences, technology, engineering, and mathematics subjects. The purpose of this study is to develop a STEAM-based educational learning package of social sciences textbooks and investigate its effect on the attitude, problem-solving ability, and creativity of sixth-grade primary school students of Bushehr in Iran. The design thinking model was used to design the training package. Methodology: The present study was quantitative applied research. The research design is quasi-experimental with pre-test and post-test with the experimental and control group (unequal control group). The statistical population of this study included all students studying in the sixth grades of public primary schools in Busher in the academic year 2020-2021. Due to the nature of the research which is quasi-experimental and also with respect to the subject drop, 44 participants were considered as the statistical sample (22 students in each group) and they were selected by "accessible" purposeful sampling and randomly divided into experimental and control groups. The STEAM training package was designed in 13 sessions and each training session took a 35-minute duration in the form of 14 projects, and its implementation lasted for 2 months. In order to design each social science lesson project, a focus group meeting was held with the members. Data were collected based on three questionnaires: Hepner and Kruskal's problem-solving skills test, Shaffer's creative attitude survey, and Aiken's scientific attitude test. The face validity of the educational package was confirmed using experts' opinions. In order to check the hypotheses of this research, covariance analysis was used. Findings: The presuppositions of the statistical test of covariance analysis, including the Kolmogorov- Smirnov test for the normality of the research variables and Levin's test for the homogeneity of the variances of the educational variables were confirmed. After that, the effects of the implementation of the steam-based educational package were investigated, and the results of the covariance analysis showed that the implementation of the educational package had an effect of (0.94) on creativity and problem-solving (0.90), and attitude (0.97) on students. The results of the Kolmogorov-Smirnov test showed that the creativity, attitude, and problem-solving skills variables of students have a normal distribution (p> 0.05). The results of the Levin test indicate that these assumptions are confirmed (p> 0.05). Conclusion: Based on the findings, it can be concluded that the STEAM-based training package increases problem-solving ability, creativity, and a positive attitude towards the course of social sciences.

This study presents a structured literature review on the application of AI in credit risk assessment, synthesizing empirical and conceptual research published between 2016 and 2022. It critically examines a range of AI models, including artificial neural networks (ANN), support vector machines (SVM), fuzzy logic systems, and hybrid architectures, with an emphasis on their predictive accuracy, robustness, and operational applicability. The review highlights that AI-based models consistently outperform traditional statistical techniques in handling nonlinear patterns, imbalanced datasets, and complex borrower profiles. Furthermore, AI enhances the inclusivity of credit evaluation by integrating alternative data sources and adapting to dynamic financial environments. However, the study also identifies ongoing challenges related to model interpretability, fairness, and regulatory compliance. By evaluating model performance metrics and methodological innovations across multiple contexts—including emerging markets, peer-to-peer platforms, and digital banking—the study offers a nuanced understanding of AI's strengths and limitations. The paper concludes with a call for balanced integration of explainable AI tools and ethical governance to ensure responsible deployment in financial institutions.