整合MOOC、智慧教学与AI，构建统一教学框架提升学习效果。
Bridging MOOCs, Smart Teaching, and AI: A Decade of Evolution Toward a Unified Pedagogy
- 分三层设计：结构化学习、动态分配、效率增强，协同提升教学
- 实证显示各层分别带来可测量的知识掌握提升
- 适合教育科技研究者和智能教学系统开发者参考
过去十年，高等教育经历了三大变革的推动：大规模开放在线课程（MOOCs）、智慧教学技术以及人工智能增强的学习。每种范式都旨在解决传统教育的特定局限：MOOCs实现学习资源的广泛获取；智慧教学通过数据驱动洞察支持实时互动；生成式AI则提供可扩展的个性化和按需内容生成。然而，这些范式常被孤立采用，限制了其系统的教学潜力。本文提出一种统一的教学框架，将三者整合于一致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：结构化暴露（MOOCs）、自适应分配（智慧教学）和效率放大（AI）。为实现整合，我们将框架形式化为分层知识转化模型，并通过逐步学习示例展示其行为。结果表明，每一层均对知识掌握带来可测量且功能上不同的增益。
原文摘要 · Abstract (English)
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.
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