Manual assignment of courses to university faculty often leads to misalignment with instructors’ expertise and inequitable workloads, problems exacerbated by favoritism or other organizational politics. These shortcomings undermine the transparent and equitable allocation process essential for a supportive academic environment. To address this challenge, this paper proposes a scalable, rule-based system design for fair and efficient course distribution. The framework systematically matches courses to instructors based on their ranked course preferences and weightage for teacher seniority, while enforcing institutional policies such as minimum and maximum teaching loads. At its core, a constraint-based assignment algorithm optimizes the alignment of faculty choices with course needs, applying algorithmic fairness principles so no instructor is overloaded or consistently denied preferred courses. Key components of the design include a faculty preference input module, a weighted allocation engine that balances individual priorities with departmental requirements, and an integration layer for academic ERP systems to ensure seamless data flow. The paper outlines how the system can be evaluated through simulations or pilot deployments, comparing outcomes with manual allocations in terms of workload balance, satisfaction, and transparency. By eliminating adhoc bias in scheduling and adapting to evolving rules, this design offers a novel, holistic approach to academic course scheduling. It not only improves fairness and faculty morale but also aligns teaching assignments with expertise, ultimately contributing to more effective and adaptable university teaching operations.

Course allocation faculty workload fairness optimization academic scheduling ERP integration