AI-DRIVEN TOOLS IN OPTIMIZING SCHOOL TIMETABLING AS A CORRELATE TO TEACHER ASSIGNMENT WHILE ADDRESSING POTENTIAL BIASES IN FEDERAL UNIVERSITIES IN THE SOUTHEAST NIGERIA

Abstract

Artificial Intelligence (AI)-driven tools are increasingly being adopted to optimize school timetabling, offering solutions to complex scheduling challenges in educational institutions. In federal universities located in Southeast Nigeria, these tools present a promising avenue for enhancing the efficiency of teacher assignment and course allocation. AI timetabling systems utilize algorithms to balance multiple constraints—such as course requirements, faculty availability, and room capacity—while minimizing scheduling conflicts. However, the implementation of such systems in the Southeast region must contend with contextual challenges, including infrastructural limitations, human resource deficits, and cultural resistance to automation. This study explores the correlation between AI-optimized timetabling and equitable teacher assignment, emphasizing the need to address potential biases embedded in algorithmic decision-making. Biases may arise from historical data patterns, unequal resource distribution, or opaque system logic, potentially leading to unfair workload distribution or marginalization of certain departments. By incorporating fairness constraints and stakeholder input, AI systems can be tailored to reflect local values and institutional goals. The study advocates for localized implementation strategies that include pilot testing, capacity building, and participatory design. It also highlights the importance of transparency and explainability in AI systems to foster trust among faculty and administrators. Ultimately, the integration of AI in timetabling within Southeast Nigerian federal universities must be context-sensitive, ethically grounded, and inclusive to ensure both operational efficiency and fairness in teacher assignment.