Acid-base chemistry, and in particular the Lewis acid-base model, is foundational to understanding mechanistic ideas. This is due to the similarity in language chemists use to describe Lewis acid-base reactions and nucleophile-electrophile interactions. The development of artificial intelligence and machine learning technologies has led to the creation of predictive text analysis models that evaluate a large number of open-ended, written formative assessment items. One of these machine learning-based tools developed by the authors evaluates correct Lewis acid-base model use. Bridging the gap between educational research, technological innovation, and instructional practice, we report the development of a web-based, interactive app using R Shiny application technologies that automates scoring of written assessments about acid-base chemistry. Results given by this Shiny app, in the form of on-screen output or a downloadable file, provide instructors with immediate feedback to evaluate acid-base instruction in their organic chemistry courses.