Predicting Guide Dog Career Success using Machine Learning and Large Language Models

High attrition rates in guide dog training programs cause substantial financial costs and resource strains when unsuitable dogs are identified late in development. To address this challenge, this study developed a machine learning framework combining structured behavioral assessments with qualitative trainer notes processed by large language models. By converting unstructured trainer comments from nearly one thousand dogs into numerical sentiment indicators, the pipeline captured subtle behavioral nuances alongside standard checklist metrics. Incorporating these textual observations significantly improved predictive accuracy over behavioral scores alone while generating clear, trainer-validated explanations for predictions. Ultimately, this hybrid approach demonstrates how natural language processing can optimize candidate screening and improve working dog selection workflows