Many researchers are already experimenting with AI tools for drafting code, synthesizing literature, outlining proposals, or summarizing datasets, yet few feel they have a clear framework for evaluating what these systems are actually doing. Are they sophisticated statistical instruments, overconfident pattern generators, or something that genuinely changes how symbolic information is processed? Without a structural mental model, it is difficult to decide where AI belongs in rigorous scientific work. This two part series is designed for environmental data scientists who want conceptual clarity before adoption.