In the face of growing urban problems such as overcrowding and pollution, we urgently need innovative ideas to build smarter and greener cities. Current urban development strategies often fail to address these challenges, revealing a significant research gap in integrating advanced technologies. This study addresses these gaps by integrating green technologies and artificial intelligence (AI), studying its impact on achieving smart and sustainable habitats and identifying barriers to effective use of these technologies, considering local variations in infrastructural, cultural, and economic contexts. By analyzing how AI and green technologies can be combined, this study aims to provide a vision that can be used to improve urban development planning. The results emphasize the significance of environmental responsibility and technological innovation in the development of sustainable urban environments and provide practical recommendations for improving the overall quality of life in cities through planning and urban planning.

The SMARTER model, an innovative educational framework, is designed for blended learning environments, seamlessly integrating both online and face-to-face instructional components. Employing a flipped classroom methodology, this model ensures an equitable division between online and traditional classroom interactions, aiming to cultivate a dynamic and collaborative learning atmosphere. This research focused on developing and rigorously evaluating the SMARTER model’s validity, practicality, and effectiveness. Adopting a research and development (R&D) approach informed by the methodologies of Borg, Gall, and Gall, this study utilized a mixed-methods strategy. This encompassed a robust validation process by experts in design, content, and media, alongside an empirical analysis of the model’s application in actual educational settings. The aim was to comprehensively assess its effectiveness and practicality. The findings from this study affirm the SMARTER model’s validity, practicality, and effectiveness in improving students’ information literacy skills. Comparative analysis between a control group, taught using a traditional expository approach, and an experimental group, educated under the SMARTER model, highlighted significant improvements in the latter group. This effectiveness underscores the model’s capacity not only to efficiently deliver content but also to actively engage students in a collaborative learning process. The results advocate for the model’s potential broader adoption and adaptation across similar educational contexts. They also establish a foundation for future research aimed at exploring the SMARTER model’s scalability and adaptability across diverse instructional environments.

This paper delves into the intricate dynamics of suburban transportation transformation within the Jakarta Metropolitan Area, with a specific focus on the evolution of the Commuter Line and Bus Rapid Transit (BRT) systems. Utilizing spatial analysis, qualitative descriptions, and stakeholder insights, the paper unveils self-organizing dynamics. It critically examines the role of transportation infrastructure in shaping the broader landscape of urban development. Unlike a traditional approach, the paper seeks to unravel the self-organization processes embedded in transportation planning, unveiling adaptive strategies crafted to tackle the distinct challenges of suburban transportation. By using autonomy, flexibility, adaptability, and collaboration frameworks, the paper contributes to a nuanced understanding of suburban transportation dynamics, with implications for policymakers, planners, and researchers grappling with similar challenges in diverse metropolitan regions.

This study investigated the utilization of Artificial Intelligence (AI) in the Recruitment and Selection Process and its effect on the Efficiency of Human Resource Management (HRM) and on the Effectiveness of Organizational Development (OD) in Jordanian commercial banks. The research aimed to provide solutions to reduce the cost, time, and effort spent in the process of HRM and to increase OD Effectiveness. The research model was developed based on comprehensive review of existing literature on the subject. The population of this study comprised HR Managers and Employees across all commercial banks in Jordan, and a census method was employed to gather 177 responses. Data analysis was conducted using Amos and SPSS software packages. The findings show a statistically significant positive impact of AI adoption in the Recruitment and Selection Process on HR Efficiency, which in turn positively impacted OD Effectiveness. Additionally, the study indicated that the ease-of-use of AI technologies played a positive moderating role in the relationship between the Recruitment and Selection Process through AI and HR Efficiency. This study concludes that implementing AI tools in Recruitment is vital through improving HR Efficiency and Organization Effectiveness.

The aviation industry is experiencing over and over again a technological revolution, nowadays with airports at the forefront of embracing smart technologies to enhance operational efficiency, security and passenger experience. This article comprehensively analyzes the benefits, challenges, and legal implications of adopting smart technologies in airport facilitation and security control. It examines the regulatory framework established by the International Civil Aviation Organization (ICAO) on an international level and by sovereign states on a national level. It explores using smart solutions such as automated systems, data and biometric verification, artificial intelligence (AI), and the Internet of Things (IoT) devices in airport operations. The authors’ purpose is to highlight the improvements in airport facilities and security measures brought about by these technologies, while addressing concerns over privacy, cost, technological limitations and human factors. By emphasizing the importance of a balanced approach and considering innovation alongside legal and operational imperatives, the article underscores the transformative potential of smart and integrated technologies in shaping the future of air travel.

Using generative artificial intelligence systems in the classroom for law case analysis teaching can enhance the efficiency and accuracy of knowledge delivery. They can create interactive learning environments that are appropriate, immersive, integrated, and evocative, guiding students to conduct case analysis from interdisciplinary and cross-cultural perspectives. This teaching method not only increases students’ interest and participation in learning but also helps cultivate their interdisciplinary thinking and global vision. However, the application of generative artificial intelligence systems in legal education also faces some challenges and issues. If students excessively rely on these systems, their ability to think independently, make judgments, and innovate may be weakened, leading to over-trust in machines and reinforcement of value biases. To address these challenges and issues, legal education should focus more on cultivating students’ questioning skills, self-analysis abilities, critical thinking, basic legal literacy, digital skills, and humanistic spirit. This will enable students to respond to the challenges brought by generative artificial intelligence and ensure their comprehensive development in the new era.