Analysis of a Scheduling System as a Proposal for Automatic Generation Using Machine Learning

The incorporation of machine learning techniques into school management systems has provided numerous opportunities to enhance efficiency and decision-making in the educational realm. Successful integration necessitates the establishment of a robust data infrastructure, relevant data collection, and attention to ethical and privacy considerations. With the increasing complexity of the Systems Engineering curriculum, it becomes imperative to implement a method for the reliable and automatic prediction of semester schedules. A thorough analysis of the current scheduling process has revealed significant shortcomings, prompting the search for solutions. This study involved gathering data on existing methodologies and identifying the problems inherent to the manual approach. The ultimate goal is to refine academic administration by optimizing course allocation and enhancing educational quality to benefit student performance. By adopting an innovative and personalized strategy, this project aims to set a new standard in educational practices, facilitated by the advancement of artificial intelligence in school management.