This study presents a systematic literature review (SLR) that analyzes 23 articles and policy documents published between 2020 and 2025, with a focus on AI Governance in Education (AIGE). The main goal of this review was to identify: (1) existing frameworks and policies, (2) the core components of these governance systems, and (3) the similarities and differences in their implementation across various countries. By applying the PRISMA protocol, the analysis integrates global, regional, national, and institutional perspectives to develop an Integrated AIGE Model ultimately. The key findings suggest that the approach to governing AI in education has undergone a significant shift. It has moved away from a purely regulatory control model toward a more multidimensional and participatory ecosystem. This new system is fundamentally built upon ethical, institutional, and collaborative principles. Globally, Frameworks like the UNESCO Recommendation on the Ethics of AI and the OECD AI Principles have established shared, crucial values such as transparency, accountability, fairness, and human-centeredness. Regionally, initiatives such as the ASEAN Guide on AI Governance and Ethics and the African Union's AI Strategy underscore the critical importance of inclusiveness and capacity building. Nationally: High-capacity countries (e.g., Singapore, Korea, Australia) tend to adopt more compliance-oriented models (based on audits and formal regulations). Emerging economies (e.g., India, Indonesia, Nigeria) primarily focus on digital readiness, AI literacy, and ethical awareness as their main priorities. Despite the contextual diversity, there is a strong consensus on the moral foundations (convergence), but a significant divergence in the actual governance structures and enforcement mechanisms. Global North frameworks are typically formalized and audit-based, whereas Global South models are more adaptive, community-oriented, and capacity-driven. Synthesizing these insights, the research proposes the Integrated AIGE Model, which consists of four interconnected dimensions: structural architecture, normative foundations, functional mechanisms, and actor networks. The model emphasizes that sustainable AI governance in education requires hybrid systems that successfully integrate global ethical standards with local contextualization. The overarching aim is to foster innovation that remains equitable, inclusive, and human-centered.