From Framework to Function: Developing an AI-Generated Workload App for School-Based Speech-Language Pathologists

Abstract

School-based speech-language pathologists manage complex workloads that extend beyond direct therapy to include evaluation, documentation, collaboration, consultation, compliance activities, and service coordination. Although established workload frameworks and spreadsheet-based monitoring tools exist to better capture the full scope of these responsibilities, they are often difficult to maintain and inconsistently used in everyday practice. Barriers such as time demands, usability, and accessibility can limit the adoption of these tools in school settings. This project explores the development of an artificial intelligence–generated workload application designed to operationalize existing workload frameworks for school-based speech-language pathologists. Using previously developed workload frameworks and Excel-based workload monitoring systems as a foundation, artificial intelligence tools were used to generate an interactive application that streamlines data entry, automates workload calculations, and visually represents workload distribution across professional responsibilities. The AI-generated app aims to improve accessibility to workload monitoring, reduce administrative burden, and support speech-language pathologists in tracking how their time is allocated across service delivery and indirect responsibilities. By transforming traditional spreadsheet tools into a more user-friendly application, this project demonstrates how artificial intelligence can support more efficient workload management and promote data-informed advocacy for sustainable service delivery and staffing practices in school-based speech-language pathology.

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