Reading comprehension is essential for both knowledge acquisition and memory reinforcement. Automated modeling of the comprehension process provides insights into the efficacy of specific texts as learning tools. This paper introduces an improved version of the Automated Model of Comprehension, version 3.0 (AMoC v3.0). AMoC v3.0 is based on two theoretical models of the comprehension process, namely the Construction-Integration and the Landscape models. In addition to the lessons learned from the previous versions, AMoC v3.0 uses Transformer-based contextualized embeddings to build and update the concept graph as a simulation of reading. Besides taking into account generative language models and presenting a visual walkthrough of how the model works, AMoC v3.0 surpasses the previous version in terms of the Spearman correlations between our activation scores and the values reported in the original Landscape Model for the presented use case. Moreover, features derived from AMoC significantly differentiate between high-low cohesion texts, thus arguing for the model's capabilities to simulate different reading conditions. [This paper was published in: "AIED 2023: Artificial Intelligence in Education," edited by N. Wang et al., Springer, Switzerland, 2023, pp. 229-241, 2023.]