Medical laboratory scientists routinely evaluate blood smears in the clinical laboratory. A high level of skill in white blood cell morphology is required to do the task quickly and accurately. Educational approaches to teaching white blood cell morphology consist of showing examples of cells to students and allowing them to practice using trial and error to hone their skills. Student progress with this traditional approach is often quite slow. In order to expedite the learning process, we created a series of questions that can guide morphological identification; these questions focus on the shape and description of the nucleus, cytoplasm, granules and cell overall. We hypothesized that white blood cells can be accurately and uniformly identified using the criteria we’ve outlined. We confirmed the accuracy of this approach to white blood cell identification using a random forest classifier algorithm on a data set of 3600 cells that we had previously categorized. Our approach has the potential to standardize white blood cell morphology training in undergraduate medical laboratory scientist programs, and could help improve the consistency of manual differential results between hospital laboratories. This work is funded by a Teaching Development Grant from the Brigham Young University College of Life Sciences This is the full abstract presented at the American Physiology Summit 2023 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.