AI for optimizing School Timetabling and Human Resource Deployment

Joyce Uche Egwu; Iwezu Ngozi Caroline

doi:10.61227/jtlc.v1i2.216

Authors

Joyce Uche Egwu Chukwuemeka Odumegwu Ojukwu University
Iwezu Ngozi Caroline Federal College of Education (Tech). Asaba, Delta State, Nigeria

DOI:

https://doi.org/10.61227/jtlc.v1i2.216

Keywords:

Artificial intelligence, School timetabling, Human resource deployment, educational administration, Gender perceptions

Abstract

Artificial intelligence (AI) is increasingly transforming administrative and academic processes in educational institutions, offering tools that can streamline operations, reduce human error, and improve overall efficiency. The study used a descriptive survey to examine 65 lecturers’ perceptions of AI-driven timetabling and administrative tools in selected tertiary institutions in Anambra State, Nigeria. Data were collected online via Google Forms using a validated, reliable Likert-scale questionnaire. Analysis employed robust M-estimators for central tendencies and tests of normality, revealing mostly non-normal data. Consequently, Mann-Whitney U and Wilcoxon W tests were used to compare male and female perceptions, showing significant gender differences across scheduling conflict reduction, staff allocation, instructional time management, and administrative decision-making. The study’s robust estimates using Huber, Tukey, Hampel, and Andrews M-estimators indicate stable central tendencies across all AI-related items, with values ranging from (15.32–15.57) for scheduling conflict reduction to (17.04–17.51) for instructional time management, reflecting minimal influence of outliers. Normality tests revealed that most variables, except administrative decision-making (p > .05), were non-normal, justifying the use of Mann-Whitney U and Wilcoxon W for comparing male and female perceptions. Gender differences were significant across all hypotheses: males consistently rated AI-driven timetabling (U = 227.50, p = .006), staff allocation (U = 264.50, p = .029), instructional time management (U = 203.00, p = .002), and administrative decision-making (U = 272.50, p = .041) more positively, leading to rejection of all null hypotheses and highlighting gendered perception patterns. The observed gender differences highlight the importance of targeted training and awareness programs to ensure equitable adoption and utilization among all staff members. Integrating AI into school management processes is therefore critical for modernizing administrative practices, improving academic delivery, and supporting data-driven decision-making across institutions.

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