International Journal of Learning Spaces Studies

Abstract This research aims to design a financial market analysis training model to improve the quality of institutional investors’ investment decisions. By examining the stock trends, gold, foreign exchange, and housing markets, this research has evaluated the future status of these markets as a basis for investment decision-making training. The results show that the stock market has experienced significant growth in different periods, including 2004, 2011, 2014, 2016, and 2019, and the peak of this trend in 2019 was accompanied by the recording of historical records of the Tehran Stock Exchange; However, after that, this market entered a period of stagnation and did not experience significant growth. The foreign exchange market also experienced relative growth in the first half of 2026, but then faced a downward trend. On the other hand, the housing and stock markets are still in a recession in 2026. Accordingly, the proposed model for teaching financial market analysis by emphasizing trend analysis, market forecasting and risk assessment can improve the decision-making ability of institutional investors and provide the basis for optimizing investment portfolios. Based on the forecasting results, a financial market analysis training model was proposed consisting of four main learning modules: market trend analysis, forecasting interpretation, risk assessment and portfolio diversification, and investment decision-making. The model integrates real market data, forecasting techniques, and case-based learning approaches to enhance institutional investors’ analytical competencies.

Abstract This study aims to explain the role of tax elasticity in reforming Iran's tax system and link it to an education-based approach. Tax elasticity, as a key indicator for measuring the response of tax revenues to changes in GDP, can provide an analytical basis for assessing the government's revenue-generating capacity, fiscal sustainability, and the quality of tax policymaking. Given the historical dependence of the Iranian economy on oil revenues as well as the intensification of financial constraints resulting from sanctions, chronic inflation, and economic fluctuations, the development of sustainable tax revenues has become one of the main necessities of economic governance. In this regard, the present study, using a descriptive-analytical method and a systematic review of new sources and studies, has examined the literature related to tax elasticity, tax reforms, and an education-based approach. The findings of the literature review show that the effectiveness of tax reforms depends more on expanding tax bases, digitalization, improving tax administration, reducing tax evasion, and improving analytical and institutional capacity than on increasing tax rates. This study adopts a framework development approach to design a data-driven learning environment for tax policymaking. Tax elasticity analysis is employed as an analytical foundation for generating fiscal insights that can be transformed into educational materials and simulation-based learning activities. Through a systematic review of literature on tax reforms, policy learning, and educational design, the study develops a conceptual framework linking fiscal analysis with professional learning environments.

Abstract Education and learning, as one of the fundamental pillars of sustainable water governance, play a decisive role in creating knowledge, changing attitudes, and modifying consumption patterns. The aim of the research is to design a learning model based on water resource conservation for sustainable development. This research is applied-developmental in terms of its purpose and qualitative and exploratory in terms of its research method. In this research, data were collected through semi-structured interviews with experts and specialists in the water field and analyzed using thematic analysis and MAXQDA software. The results of data analysis led to the extraction of initial concepts and themes, and by organizing the initial themes, 36 main components were finally identified, and after merging, refining, and integrating, 6 main dimensions were formed, which were considered and analyzed as the main strategies in water consumption conservation. The findings showed that the realization of water consumption conservation requires the coordinated interaction of economic, cultural, social, educational, technological, managerial, and political factors. And these results can be used as a model or practical framework for policy-making, planning and management of water consumption and provide the basis for promoting responsible consumption behavior, increasing public awareness and sustainability of water resources.

Abstract The integration of Artificial Intelligence (AI)—specifically Large Language Models (LLMs) and Graph Neural Networks (GNNs)—has fundamentally transformed knowledge processing in higher education. Despite rapid adoption, a critical gap exists between technological capability and institutional implementation. This systematic literature review investigates the mechanisms of AI-driven knowledge processing at both micro and macro levels within academic settings. Adhering to the PRISMA framework, this study synthesizes 56 peer-reviewed articles selected from an initial database of 9,894 records to identify key trends, pedagogical implications, and organizational challenges. Findings reveal that while AI enhances decision-support systems and personalized learning, its deployment is hampered by issues of data governance, algorithmic bias, and institutional heterogeneity. The analysis demonstrates that effective knowledge processing requires a shift from mere adoption to a strategic, AI-governance-centered approach. Furthermore, the results highlight a significant lack of model robustness analysis, suggesting that future research must prioritize the stability and long-term sustainability of AI applications in academic curricula. This paper contributes to the field by providing a comprehensive framework for institutional decision-makers to navigate the complexities of AI integration, ultimately bridging the gap between theoretical knowledge processing and practical, actionable educational strategies