This review synthesizes current research on Large Language Model (LLM)-enhanced curriculum design, examining frame-works, methodologies, and practical implementations across diverse educational contexts. We analyze prominent LLM platforms including GPT-4, Claude, and Gemini, their applications in content generation, assessment development, and instructional design automation. Key findings indicate that available evidence suggests Human-in-the-Loop (HITL) approaches offer advantages over fully automated systems in maintaining pedagogical quality and domain accuracy. Critical success factors include structured prompt engineering, multi-stage validation protocols, and platform-agnostic design principles. Technical challenges encompass token capacity limitations, mathematical content formatting, and platform-specific knowledge gaps. Evidence suggests that optimal outcomes emerge from iterative human-AI collaboration within HITL frameworks, rather than complete or minimally supervised automation, with educator supervision remaining essential for quality assurance, while identified re-search gaps point to promising future directions for AI-assisted educational content development.