Optimization-Based Faculty Course Allocation Using Artificial Intelligence Techniques

In the academic world, faculty course assignments are a terrifying issue that frequently appears easier than they actually are. The difficulty of assigning teachers to appropriate classrooms and courses becomes a delicate act of fairness, aptitude, and workload balance as colleges expand and their course offerings become more varied. Artificial intelligence (AI) has become a viable solution to these resource allocation problems in recent years. This study contrasts a combination model that combines machine learning and optimisation with a number of traditional and AI-based optimisation techniques, including Greedy Heuristic, Rule-Based Assignment, Constraint Satisfaction, and Genetic Algorithm. Before training the model, we meticulously cleaned and processed the institutional data, emphasising key characteristics. We considered teacher satisfaction and the fairness of the workloads in addition to the accuracy and speed of the assignments. We discovered that while simple approaches are useful, the combined AI-optimization solution performs exceptionally well, with an 87% preference fulfilment rate and more evenly distributed tasks. Our results suggest that AI-based frameworks could play a key role in increasing the scalability and equity of faculty course assignments.