Interactive Dashboards for Modeling-Based Education: Methods and Best Practices with Python and Plotly Dash

The integration of interactive digital tools into science education has become increasingly important for helping students grasp abstract and multiparameter concepts that are often difficult to convey with static figures. This chapter presents a reproducible method for building interactive dashboards with Python and the Plotly Dash framework, illustrated through the case of ligand binding equilibria. The approach combines classical binding models (single site, multiple independent sites, heterogeneous sites, and cooperative Hill binding) with a browser-based interface that allows users to manipulate parameters and immediately visualize the corresponding curves. From a pedagogical perspective, dashboards offer a powerful way to bridge symbolic formalism and intuitive understanding. By experimenting directly with constants, numbers of sites, and cooperativity factors, learners can explore how these parameters affect the shape of saturation curves, compare models side by side, and develop a deeper conceptual grasp of molecular recognition and cooperativity. The method also highlights the value of dashboards as teaching aids that promote active learning, fostering curiosity and engagement while reducing the cognitive gap between mathematical abstraction and experimental interpretation. Although the case study focuses on ligand binding, the structure is general and can be adapted to other quantitative models in chemistry, biology, or physics. The implementation is lightweight, modular, and fully based on open-source software. A brief introduction to Flask-Babel is included to illustrate how the same dashboard can be internationalized for multilingual use. Beyond educational contexts, the dashboards described here can also be employed as flexible tools for data analysis, enabling instructors, researchers, and students to explore real datasets interactively with the same interface.