Structured arguments are a valuable resource for analyzing and understanding complex topics. However, manual annotation is time-consuming and often not feasible for large datasets, and automated approaches are less accurate. To address this issue, we propose an interactive argument mining system that takes advantage of generative language models to support humans in the creation of argument graphs. We present the open source ArgueMapper Assistant featuring two prompting strategies and evaluate it on a real-world news dataset. The resulting corpus containing 88 argument graphs is publicly available as well. With generative models, the annotation time is reduced by about 20% while the number of errors is slightly increased (mostly due to missing argumentative units and wrong relation types). A survey provides insights into the usefulness and reliability of the assistant features and shows that participants prefer to use the assistant in the future.