This study examines the integration of generative AI (GenAI) in government policy making, focusing on Taiwan’s Science & Technology Policy Research and Information Center (STPI). By adopting a boundary perspective, it explores challenges such as the literacy, knowledge, and ownership boundaries encountered during GenAI implementation. The research highlights how STPI leverages retrieval-augmented generation (RAG) technology to overcome these obstacles, enhancing the accuracy and efficiency of policy formulation. This case study underscores the potential of GenAI to transform evidence-based policy making by addressing interdisciplinary data integration, technical literacy gaps, and cross-departmental collaboration barriers, providing insights for future government applications of AI technologies.

The Middle East and Africa (MEA) region presents a unique set of needs and opportunities for generative artificial intelligence (AI), driven by economic diversification, digital transformation, and social challenges. Key sectors such as health care, education, finance, and governance require AI-driven solutions tailored to linguistic, cultural, and infrastructural nuances. In the Middle East, AI is central to national visions like Saudi Arabia’s Vision 2030 and the United Arab Emirates’s (UAE’s) AI strategy, strengthening innovation in smart cities and cybersecurity. Africa, with its diverse economies and growing tech hubs, demands AI for localized content creation, agricultural optimization, and financial inclusion. Addressing data scarcity, ethical AI deployment, and skill development remains critical for Africa. A region-specific approach to generative AI can enhance economic growth, societal well-being, and sustainable development in MEA.

The rapid advancement of information technologies has accelerated the adoption of innovative solutions like serverless computing (SC). SC and artificial intelligence (AI) act as complementary technologies: SC offers scalable and cost-effective infrastructure for AI models, while AI enhances and automates serverless systems. This paper reviews how AI improves SC architectures and how SC supports AI deployment. The findings highlight mutual benefits in terms of efficiency, cost, and scalability.

This article analyzes the drivers, strategies, and outcomes of artificial intelligence (AI) integration in China’s education system, from K-12 to higher education, emphasizing policy initiatives, technological adoption, and the growth of AI-focused EdTech companies. It further assesses the implications of China’s AI education expansion for workforce development, global competitiveness, and the future of personalized learning.