University level computing ethics courses extract varied discussion from students, especially at large scale universities. The expansion of undergraduate CS programs brings different forms of student identity, sociotechnical perspectives, and intersectionality into the classroom. All these variables affect students’ understanding of the world, as well as their work in computing ethic classes. As such, instructors must facilitate topics that are particularly interesting for students from a range of backgrounds—especially in discussion- based seminar courses that survey many topics. In this work, we introduce a low-overhead natural language processing tool that can assist instructors in extracting student talking points from over 600 discussion forum posts in a large-scale undergraduate computing ethics course. While simple in implementation, this n- gram-based scripting tool selects popular quotes and summarizes common discussion topics more effectively than large language model approaches and can be easily adapted by instructors for classroom discussion preparation and practical examples.