When graduates of computing degree programs enter the software industry, they will most likely join teams working on legacy code bases developed by people other than themselves. In these so-called brownfield software development settings, generative artificial intelligence (GenAI) coding assistants like GitHub Copilot are rapidly transforming software development practices, yet the impact of GenAI on student programmers performing brownfield development tasks remains underexplored. This paper investigates how GitHub Copilot influences undergraduate students' programming performance, behaviors, and understanding when completing brownfield programming tasks in which they add new code to an unfamiliar code base. We conducted a controlled experiment in which 10 undergraduate computer science students completed highly similar brownfield development tasks with and without Copilot in a legacy web application. Using a mixed-methods approach combining performance analysis, behavioral analysis, and exit interviews, we found that students completed tasks 35% faster (p < 0.05) and made 50% more solution progress p (< 0.05) when using Copilot. Moreover, our analysis revealed that, when using Copilot, students spent 11% less time manually writing code (p < 0.05), and 12% less time conducting web searches (p < 0.05), providing evidence of a fundamental shift in how they engaged in programming. In exit interviews, students reported concerns about not understanding how or why Copilot suggestions work. This research suggests the need for computing educators to develop new pedagogical approaches that leverage GenAI assistants' benefits while fostering reflection on how and why GenAI suggestions address brownfield programming tasks. Complete study results and analysis are presented at https://ghcopilot-icer.github.io/.