This chapter examines how artificial intelligence (AI), in the form of large language models, can be used to support the process of empirical data analysis. The focus is on three key steps: selecting appropriate methods, evaluating and interpreting the data, and visualizing the results. The chapter demonstrates how AI can provide well-founded suggestions for method selection—both for qualitative and quantitative data—while also taking into account risks such as hallucinations or methodological deviations. In the second part, the chapter shows how AI can assist in the practical implementation and reflection of analytical procedures, without replacing the necessary scientific responsibility. Finally, it outlines ways in which AI can be applied to the creation of scientific visualizations—for example, through code generation for graphical tools. The aim of the chapter is to strengthen students’ methodological competence and to enhance the quality of empirical work through targeted use of AI. Numerous prompts provide practical examples of how this approach can be implemented in concrete terms.