International Journal of Learning Spaces Studies

Abstract The study aims to design a tourism management model for the Cultural Heritage and Tourism Organization of Khuzestan Province. The present study is an applied-developmental study in terms of its purpose and is a cross-sectional survey study in terms of the method and time period of data collection. The statistical population of the present study was managers of different levels of the Cultural Heritage and Tourism Organization of Khuzestan Province. The qualitative part of the sampling method was based on non-probability sampling methods. In the quantitative phase of the research, a random stratified sampling method was used to validate the proposed research model. Given the exploratory, a semi-structured interview was used to collect the opinions and views of the experts of the National Iranian Oil Company regarding the identification of the indicators, components and dimensions of the research model. The qualitative method of grounded data analysis was used in ATLAS TI software. 6 main categories, 26 central categories and 160 primary codes were identified. Causal conditions include data management, information technology, employee status, tourist status and organizational structure. Contextual conditions include geographical location, tourism infrastructure, cultural heritage status, government conditions, and social culture. Intervening conditions include data analysis process, international conditions, management support, economic conditions, and advertising and marketing. Strategies include data-driven decision-making, improved financing, customer relationship management, tourism branding and cross-sectoral partnerships, organizational training and learning, and modeling from successful areas. Outcomes include revenue generation and profitability, sustainable regional development, tourist satisfaction, and improved tourism image.

Abstract This research aims to design a model for creating a culture of transformation in the public sector with a modern public service approach (case study: National Iranian Oil Company). The research is applied-developmental in terms of its purpose and survey-cross-sectional in terms of data collection method and time. Data were collected through semi-structured interviews with 15 managers and experts of the company, using purposive sampling until theoretical saturation was reached. Qualitative data analysis was performed with ATLAS.ti software and finally 5 main categories, 25 focal categories, and 176 initial codes were extracted. Causal conditions include environmental pressures, stakeholder expectations, the need for transparency, the development of digital technologies, and reduced organizational productivity. Contextual conditions include traditional administrative structure, bureaucratic culture, employee skill level, national macro policies, and existing technological infrastructure. Intervening conditions also include employee resistance, transformational leadership, support from senior policymakers, limited financial resources, and strategic inter-organizational interactions. The suggested strategies include empowering human resources, employing new technologies, institutionalizing transparency, developing organizational learning, and strengthening transformational leadership. The outcomes include increasing efficiency, improving service quality, strengthening public trust, reducing costs, and creating a competitive advantage for the government. The results show that realizing a culture of transformation requires synergy between causal, contextual, and intervening conditions and implementing appropriate strategies with continuous monitoring of results. Implementing this model can transform the National Iranian Oil Company into an innovative, responsive organization and a model of new governance in the public sector.

Abstract Artificial intelligence is increasingly transforming learning and human resource development (HRD) by enhancing instructional design, personalized learning, performance analysis, and professional development. However, existing reviews have primarily classified AI according to underlying technologies rather than their functional roles in learning and HRD. This systematic review aimed to identify AI tools used in learning and HRD and develop a function-oriented taxonomy of their applications. Following the PRISMA 2020 guidelines, a systematic search was conducted across Scopus, Web of Science, and ScienceDirect. Twenty-five peer-reviewed studies published between 2007 and 2024 met the inclusion criteria. Methodological quality was assessed using the Mixed Methods Appraisal Tool (MMAT, 2018). AI technologies were first coded deductively using the framework of Votto et al. (2021), followed by an inductive thematic synthesis to develop a functional taxonomy. Natural language processing emerged as the dominant AI technology, while teacher professional development was the most frequently investigated application context. The analysis identified five functional categories of AI applications: instructional response and teaching analytics, natural language processing and text analytics, video processing, data management, and interactive and personalized learning. These findings indicate that AI primarily supports instructional feedback, reflective practice, adaptive learning, and evidence-informed decision-making across educational and organizational settings. This review contributes a function-oriented taxonomy that complements existing technology-based classifications and provides a practical framework for researchers, instructional designers, and HRD practitioners. It also highlights the limited evidence in organizational HRD and identifies priorities for future research on the implementation and long-term effectiveness of AI-supported learning environments.