Massive Open Online Courses (MOOCs), Smart Teaching (ST), and AI-assisted learning have reshaped higher education at different stages of its digital transformation. Existing educational technology research often examines these approaches in isolation or relies on aggregate outcomes, making their contributions within a continuous learning process difficult to distinguish. This study develops a unified quantitative model comprising MOOC-based pre-class learning, ST-based in-class adaptation, and AI-assisted post-class personalization. Student knowledge is represented as a bounded multidimensional state, and all stages follow a common exponential learning-response function through which instructional resources reduce remaining knowledge gaps. The stages differ in their resource-allocation rules: predefined course-content emphasis for MOOCs, class-level adaptation based on aggregate feedback for ST, and individualized allocation based on mastery gaps for AI-assisted learning. Simulations demonstrate cumulative learning gains, adaptive resource redistribution across knowledge components, and diminishing returns as the learning budget increases. Experiments across 100 independently generated classes further show stable stage-wise progression under learner heterogeneity. By making state transitions and allocation mechanisms explicit, the framework enables stage-wise decomposition of learning gains and controlled analysis of resource distribution and budget sensitivity. It thus provides a quantitative foundation for analyzing and coordinating successive instructional stages within an integrated learning process.

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