Most AI research on scientific model construction aims to automate this process using discovery techniques. In contrast, we describe an interactive environment for model construction that lets the user construct, edit, and visualize scientific models, use them to make predictions, and call on discovery methods to revise them in ways that better fit the available data. The environment relies on a new formalism that embeds mathematical equations, which are familiar to many scientists, within distinct processes, which can encode background knowledge used to constrain model revision. We report initial studies on ecosystem modeling that suggest this environment is more effective than earlier approaches and more transparent to users. In closing, we discuss related work on modeling environments and model revision, then suggest directions for future research.