The presentation describes efficient and effective computational methods to evaluate an interprofessional education program. Methods from natural language processing were used to assess common themes that emerged from the text of PowerPoint presentations that teams of interprofessional students created as a part of an advocacy project.   Over 700 students from nine different health professions programs across the university are grouped into teams of five to six students, and work with a community volunteer, a Health Mentor for three semesters. Each team is comprised of at least three…