In this talk, I will introduce a novel approach for longitudinal assessment data that involve item responses from individuals at two or more time points. A key limitation of existing longitudinal item response models is their inability to capture item-by-person interactions that can change over time. To address this, I propose an interaction map approach that can capture and visualize time-varying person-by-item interactions, offering valuable insights into individuals’ progress over time. Furthermore, I will present a more structured version of the interaction map approach, which focuses on tracking individuals’ progress toward a measurement target directly within the map. Real-world examples will be shared to illustrate the practical applications of the proposed methodologies.