TALÖVSIS: A cross-provider, self-learning substitute staffing system for inclusive school support (school aides), incorporating public transport constraints

When school aides are absent at short notice, qualified replacements are often needed by the very first lesson—otherwise, pupils who require support may not be able to participate smoothly in class. In practice, substitute coordination is still largely handled manually and, given the number of cases, tight time windows, and many constraints, it is difficult to optimize effectively.

Against this backdrop, the project develops an AI-based demonstrator system for cross-organizational substitute management in school aide services. In real time, it provides decision support for assigning substitutes and takes into account prioritizable criteria such as qualifications/suitability, time availability, mobility/public-transport travel times, distance, and prior deployment frequency. Based on historical data, the system optimizes workforce scheduling to ensure high substitute coverage and quality even as case numbers grow. Optionally, it can access cross-provider relief staff pools in an anonymized manner to utilize capacities more efficiently—particularly benefiting smaller service providers.