This paper reports on the development of an analysis engine for the Research Writing Tutor (RWT), an AWE program designed to provide genre and discipline-specific feedback on the functional units of research article discourse. Unlike traditional NLP-based applications that categorize complete documents, the analyzer categorizes every sentence in Introduction section texts as both a communicative move and a rhetorical step. We describe the construction of a cascade of two support vector machine classifiers trained on a multi-disciplinary corpus of annotated texts. This work not only demonstrates the usefulness of NLP for automated genre analysis, but also paves the road for future AWE endeavors and forms of automated feedback that could facilitate effective expression of functional meaning in writing.