Journal of Study and Innovation in Education and Development

The objective of this study was to design and validate a model for developing foresight skills among managers of Sepah Bank in Alborz Province. This applied research employed a cross-sectional survey design. The statistical population consisted of 985 managers and employees of Sepah Bank, from which 278 participants were selected using the Krejcie and Morgan table; ultimately, 289 valid questionnaires were collected. Data were gathered using a researcher-made questionnaire comprising 112 items, 21 components, and 6 main factors, whose validity was confirmed by banking and academic experts and whose reliability was approved using Cronbach’s alpha. Data were analyzed using confirmatory factor analysis (CFA) with SPSS and LISREL software. Model fit was assessed through indices such as chi-square/df ratio, RMSEA, GFI, and AGFI. CFA results indicated that all model paths were statistically significant at the 0.05 level, with all t-values exceeding ±1.96. The highest factor loading was observed for organizational culture and behavior (0.97), followed by communication skills and flexible organizational structure. All causal, contextual, intervening, strategic, and outcome factors demonstrated significant and strong relationships with the main latent construct. Model fit indices showed acceptable and strong fit (χ²/df<3, RMSEA<0.08, GFI and AGFI>0.90). Strategic factors such as continuous learning, research skills, and effective communication exhibited the strongest effects, while outcomes such as strategic planning, value creation, competitive advantage, and decision-making agility were directly influenced by foresight skill development. The findings indicate that developing foresight skills among banking managers is a multidimensional process shaped by organizational, environmental, and technological factors, and it enhances organizational agility, competitive value creation, and strategic decision-making within the banking sector.

The objective of this study was to identify the key drivers shaping academic and career guidance for upper secondary school students in Tehran Province. This study employed a qualitative exploratory design using thematic analysis. Participants included university experts and educational practitioners with specialized experience in academic and career guidance. Using purposive and snowball sampling, 24 participants were interviewed through semi-structured interviews until theoretical saturation was reached. The interviews were transcribed and coded in MAXQDA and analyzed based on Braun and Clarke’s six-phase thematic analysis framework. Data trustworthiness was ensured through credibility, dependability, confirmability, and transferability criteria. The analysis resulted in 5 overarching themes, 16 organizing themes, and 74 basic themes. The overarching themes included individual enablers, environmental enablers, human enablers, structural enablers, and curriculum-based enablers. These themes encompassed components such as students’ abilities and interests, cultural attitudes toward occupations, family conditions, labor market considerations, parental awareness, teacher and counselor competencies, the necessity of revising guidance programs, the role of technological tools, redesigned policies, revised content, and improved assessment mechanisms. The final thematic network demonstrated that effective academic and career guidance requires the coordinated interaction of individual, social, structural, and curricular factors. The findings highlight that systematic attention to the identified drivers can enhance the quality of academic and career guidance and support students in making informed and realistic educational and occupational choices aligned with their abilities, interests, and labor market needs.

The study aimed to examine the feasibility of integrating artificial intelligence into legal education, focusing on technological, educational, and ethical–legal requirements within Iran’s higher education system. This research employed an exploratory sequential mixed-methods design. In the qualitative phase, semi-structured interviews were conducted with 24 law professors and educational technology experts in Tehran, and data were analyzed thematically. Based on these findings, a researcher-made questionnaire was distributed among 250 law professors and students. Quantitative data were analyzed using descriptive statistics, confirmatory factor analysis, and structural equation modeling. Results indicated a high willingness to adopt AI in legal education, though infrastructural readiness remains insufficient. Confirmatory factor analysis validated five core constructs—awareness and attitude, technological requirements, ethical–legal considerations, educational preparedness, and willingness to use—showing good model fit (CFI=0.94, RMSEA=0.056). Educational preparedness and technological awareness were the strongest predictors of willingness to use AI, with ethical–legal factors mediating their relationship. Implementing AI in legal education requires synergy among technology, pedagogy, and ethics. Strengthening digital infrastructure, training faculty in AI literacy, and establishing ethical and legal frameworks are essential for transitioning toward intelligent legal education.

This study aimed to identify and prioritize the factors influencing teachers’ insight transformation toward online and AI-based instruction to develop a comprehensive model for professional empowerment. The research employed an applied, descriptive–survey design using a mixed-method exploratory sequential approach. In the qualitative phase, semi-structured interviews were conducted with 11 educational experts, and data were analyzed through Braun and Clarke’s thematic analysis using MAXQDA software. In the quantitative phase, the Fuzzy Delphi method was used to validate and prioritize indicators. The quantitative population comprised 456 elementary school teachers in Mazandaran Province, from which 210 were selected through stratified random sampling. Data were analyzed using SPSS 26, SmartPLS 3, and Excel 23. The analysis identified ten main dimensions influencing teachers’ AI-related insight: online professional learning context, affective dimension, collaborative orientation, managerial support, curriculum alignment, online content knowledge, content design ability, content application skills, professional AI-based training, and online learning effectiveness. The highest fuzzy mean scores were associated with cognitive engagement, continuous learning, and professional collaboration. Teachers’ insight transformation toward AI-based education is a multidimensional process shaped by technological literacy, self-efficacy, emotional factors, and organizational support. Strengthening this insight requires continuous professional development programs and institutional commitment to fostering AI-driven educational competencies.

This study aims to explore and interpret the lived experience of hope and fear among university students in Tehran in their interaction with social media from a phenomenological perspective. Using a qualitative phenomenological approach, data were collected through 35 semi-structured in-depth interviews with 29 students and 6 experts in social sciences and communication. Data were analyzed based on Braun and Clarke’s six-step thematic analysis model. Participants were selected through purposive sampling, and data collection continued until theoretical saturation was reached. Credibility was ensured through participant validation and constant comparison techniques. The findings revealed that hope among Tehran students is a multidimensional and dynamic phenomenon rooted in learning, faith, self-development, and moral action. Fears primarily stem from economic instability, social pressure, media-driven comparison, and institutional distrust. Students cope with these challenges by employing strategies such as limiting social media exposure, focusing on learning and creativity, civic engagement, and spirituality. Their lived experience represents a dialectic of resistance and meaning-making within Tehran’s socio-digital environment. Hope for the young generation in Tehran is not merely an emotional state but a cognitive-social mechanism for maintaining meaning amid instability. Despite pervasive fears, students reconstruct an intrinsic and resilient form of hope. The results carry policy implications for enhancing psychological resilience, institutional trust, and media literacy education among youth.

This study aimed to design and validate a leadership skills development model using neuro-linguistic programming (NLP) techniques in state-owned companies, focusing on Iran Insurance Company. This developmental and quantitative research was conducted in two phases. In the first phase, key indicators, components, and dimensions of leadership skill development were identified through a systematic review and a Delphi process involving 25 NLP experts. In the second phase, a researcher-made questionnaire was distributed among 152 managers of Iran Insurance Company, and data were analyzed using inferential statistics such as KMO and Bartlett’s tests, confirmatory factor analysis (CFA), and analytic hierarchy process (AHP). The Cronbach’s alpha of 0.87 and a CVR value above 0.37 confirmed the reliability and validity of the instrument. The designed NLP-based leadership development model consisted of three dimensions (intrapersonal, interpersonal, and organizational), eight components, and 29 indicators. AHP results revealed that intrapersonal leadership skills ranked highest (weight = 0.279), followed by organizational (0.254) and interpersonal (0.172) skills. Among components, “self-management” (0.433) and “thinking and awareness” (0.421) held the highest importance. CFA confirmed the model’s goodness of fit with the data. NLP techniques effectively enhance leadership skills among managers in governmental organizations. Integrating NLP-based training into leadership development programs can strengthen individual, interpersonal, and organizational competencies, leading to improved performance, adaptability, and innovation in public sector leadership.

The study aimed to identify and rank the fundamental components of the peace education curriculum in primary schools from the perspective of experts. This applied research adopted a descriptive–analytical approach and a mixed exploratory design (qualitative–quantitative). In the qualitative phase, ten professors and Ph.D. students in curriculum studies at Allameh Tabataba’i University were selected through purposive sampling until theoretical saturation. Semi-structured interviews were analyzed using a three-stage thematic analysis method. In the quantitative phase, the statistical population comprised 890 primary school teachers in Khorramabad during the 2024–2025 academic year. A researcher-made questionnaire based on qualitative findings was distributed among 115 participants selected through convenience sampling. Data were analyzed using confirmatory factor analysis with PLS software. The qualitative findings identified five key components of peace education: peaceful coexistence, human dignity, cognitive factors, emotional factors, and behavioral factors. Quantitative results confirmed the significance of all components at the 99% confidence level. Among them, cognitive factors had the highest factor loading (0.85), followed by human dignity, behavioral factors, emotional factors, and peaceful coexistence. Peace education in primary schools is a multidimensional process requiring simultaneous attention to children’s cognitive, emotional, behavioral, and moral domains. The findings highlight that integrating peace education into the curriculum through cognitive development, emotional literacy, and social skill training can foster peace-oriented attitudes and responsible citizenship among young learners.

The study aimed to identify and design a competency model for human resource (HR) managers and to rank its key components within governmental organizations in Tehran. This research employed a mixed-method design. In the qualitative phase, content analysis and semi-structured interviews with 14 academic and executive HR experts were conducted and analyzed using Atlas.ti (interview reliability = 86.66%). A three-round Delphi technique validated the extracted indicators, and the Analytic Hierarchy Process (AHP) ranked the ten most important components. In the quantitative phase, structural equation modeling using SmartPLS-3 tested the model’s validity. The sample comprised 147 HR managers and experts from various governmental ministries in Tehran, selected through convenience sampling. Cronbach’s alpha values exceeding 0.9 confirmed instrument reliability. Three main dimensions—general managerial competencies, technical and professional competencies, and behavioral competencies—were identified, encompassing 18 components and 48 indicators. The model showed a good fit (GOF = 0.590). Based on the AHP results, “Leadership and Guidance” received the highest ranking, while “Analytical Designer and Interpreter” had the lowest. The proposed model demonstrates that HR managers’ competencies are vital for organizational effectiveness in the public sector. The integrated structure of behavioral, technical, and managerial competencies provides a practical framework for recruitment, training, and development of HR managers. This model offers strategic value for enhancing managerial capability and institutional performance in Iran’s governmental organizations.