Digital learning platforms now mediate a large share of instruction in healthcare and science, technology, engineering, and mathematics education, yet most platforms collect far more learner data than they convert into defensible instructional decisions. This paper develops a conceptual framework that treats data, inference, pedagogical adaptation, and evidence governance as four interacting layers rather than as a single analytics pipeline. The framework is grounded in social cognitive theory, self-determination theory, cognitive load theory, and established models of technology adoption, and it is positioned against existing approaches such as learning analytics reference models, intelligent tutoring architectures, educational data mining, and competency-based education designs. Three arguments organize the framework. First, the value of platform data is created not at the point of capture but at the point where inference is translated into a pedagogical action that a learner experience. Second, healthcare and STEM environments impose evidentiary and equity constraints that general consumer education technology does not, so the same analytic capability carries different obligations in these settings. Third, without an explicit governance layer that defines what counts as evidence and that monitors validity and fairness, adaptive platforms tend to reproduce and amplify the inequities present in their training data. The framework specifies measurable indicators for each layer, feedback loops connecting them, and boundary conditions that mark where it applies and where it does not. The paper synthesizes meta-analytic evidence on technology effects, intelligent tutoring, simulation, and online learning to ground each component, and it maps practical, policy, theoretical, and methodological implications, identifying the experimental and longitudinal designs most likely to test the framework's central claims. The contribution is a structured way to reason about when data-informed instruction improves learning, when it merely instruments it, and when it risks harm.