Over the past decade, higher education has undergone successive shifts driven by three major developments: Massive Open Online Courses (MOOCs), Smart Teaching technologies, and AI-enhanced learning. Each paradigm emerged to address specific limitations of traditional education: MOOCs enable ubiquitous access to learning resources; Smart Teaching supports real-time interaction with data-driven insights; and generative AI offers scalable personalization and on-demand content generation. However, these paradigms are often adopted in isolation, limiting their systemic pedagogical potential. This paper proposes a unified instructional framework that integrates these approaches under a coherent teaching-driven logic. The framework distinguishes three complementary dimensions of instructional design: structured exposure (MOOCs), adaptive allocation (Smart Teaching), and efficiency amplification (AI). To operationalize this integration, we formalize the framework as a layered knowledge transformation model and illustrate its behavior through a step-by-step learning example. The results demonstrate how each layer contributes to measurable and functionally distinct gains in knowledge mastery.

翻译：过去十年间，高等教育经历了由三大发展浪潮驱动的连续变革：大规模开放在线课程（MOOCs）、智慧教学技术以及人工智能增强学习。每种范式都针对传统教育的特定局限应运而生：慕课实现了学习资源的泛在获取；智慧教学通过数据驱动的洞察支持实时互动；而生成式人工智能则提供了可扩展的个性化服务与按需内容生成。然而，这些范式往往被孤立采用，限制了其系统性的教学潜能。本文提出一个统一的教学框架，将这些方法整合于连贯的教学驱动逻辑之下。该框架区分了教学设计的三个互补维度：结构化知识呈现（慕课）、适应性资源分配（智慧教学）与效率增强（人工智能）。为实现这种整合，我们将框架形式化为一个分层知识转化模型，并通过逐步学习案例阐释其运行机制。结果表明，每个层级如何对知识掌握产生可测量且功能差异化的增益。