This study focuses on a generative AI approach to facilitate qualitative analysis in Writing Studies research. We gathered 13,336 one-sentence to one-paragraph responses written by 3,334 incoming students in a directed self-placement program administered at a large R1 U.S. university. In these responses, students describe their high school writing experience and college writing expectations. In stage one of the project, we pilot the use of Retrieval-Augmented Generation to expedite the selection of relevant responses for a topic—in this case, students’ positive self-assessments as writers. The selected responses were then compared to a random sample and rated by three faculty with writing expertise. In stage two, these faculty generated codes and themes from a subset of the responses, incorporating ChatGPT-4 through the stages of thematic analysis. Results show that the use of AI expedites and enhances qualitative analysis, but human participation in the process is still essential. We suggest a machine-in-the-loop framework with which Writing Studies researchers can more readily integrate generative AI to study large corpora of student writing.