Distributed neural systems engage in coordinated information processing that develops over time to support complex learning. Here, we extract the information represented in these neural systems in order to observe changes in conceptual knowledge related to STEM learning. Two groups of learners with different levels of prior knowledge and experience completed an fMRI-based task involving abstract mechanical engineering concepts. First, using data collected during learning, we identified emergent networks of neural activity that displayed similar response patterns. Next, we extracted the representational structure of these informational networks using multivariate representational similarity analysis. Finally, by comparing these representations to an expert model of concept knowledge, we identified within each informational network the presence (or absence) of expert-level knowledge of the target concepts. Results demonstrate that between groups and over the course of learning, different neural systems represent expert concept knowledge, indicating distinctions between dorsal and ventral stream processes and along an anterior-posterior gradient within the ventral stream. Our approach can be applied to investigate conceptual change across STEM domains, and provides a novel means of assessing the neural basis of concept learning over time and between individual learners.