This project leverages Python programming and an open-source large language model (LLM) to calculate semantic similarity scores between pairs of text strings. These scores are systematically recorded in an Excel workbook, enabling the organization of string pairings into ranked mappings. Dr. Macintosh employed this methodology to propose mappings between AACN graduate sub-competencies and course learning outcomes. The Python code utilized is versatile and can be applied to map any two sets of text strings. During the presentation, attendees will observe a live demonstration of the report generation process using Google Colab and will have access to the Python code used in the analysis.