Adaptive learning systems use information about learners’ activities to tailor their functionalities such as recommending learning materials, providing feedback, and visualizing progress. I describe the research conducted so far during my PhD—at the beginning of my second year—where we developed a computational framework that systematically characterizes adaptive learning services by defining their key components, inputs, and outputs. This framework is validated through pilot projects at NOLAI, the Dutch National Education Lab AI. I describe the mAIchart project—a pilot project that integrates data from diverse digital learning environments into a unified dashboard for data-driven instructional decisions. mAIchart aims to help teachers make faster, better-informed instructional decisions. The paper outlines plans for the future work aimed at combining different learner models.