ARTIFICIAL INTELLIGENCE AS DETERMINANT FOR BASIC SCHOOLS LOCATIONAL PLANNING IN THE NORTH-EAST, NIGERIA (AUN INTERNATIONAL CONFERENCE, 2025)
This study examined artificial intelligence as determinant for basic school locational planning in the North-East, Nigeria. Specifically, three specific sub-variables, three research questions and three hypotheses guided the study. Correlational research design was adopted for this study. The population of this study is 36,802. This population consists of 2,423 principals, 34,379 teachers in 2423 Basic Schools in North-East, Nigeria. The sample of the study is 1,173 respondents (i.e., 484 principals and 688 teachers) in Basic Schools in North-East, Nigeria. Multi-stage sampling technique was adopted for this study. Two self-structured instruments developed by the researchers were used for data collection. The questionnaires were tagged “Artificial Intelligence Questionnaire (AIQ)” and “School Locational Planning Questionnaire (SLPQ)”. The instruments were scored on a five-point Likert type Scale. A reliability coefficient of 0.85 and 0.82 were obtained for “AIQ and SLPQ using Cronbach Alpha Method of internal consistency. Data collected for the study were analysed using descriptive statistics of Mean and Standard Deviation in answering the three research questions raised. While, Simple linear regression analysis was used in testing null hypothesis 1 and 2. Multiple regression analysis was used to test hypothesis 3. The results revealed that AI-driven human resource management system is a significant determinant for basic school locational planning in the North-East, Nigeria at (F(1, 1172) = 8.416, p < .05). The findings revealed that Personalized learning system is a significant determinant for basic school locational planning in North-East, Nigeria (F(1, 1172) = 9.327, p < .05). The regression model indicates that Artificial intelligence variables of AI-driven human resource management systems and personalized learning systems are significant determinants for basic school locational planning in North-East, Nigeria F(2, 1172) = 8.945, p < .05. The results led to the conclusion that AI-powered human resource management systems provided the most significant unique input in understanding the locational planning for basic schools in North-East Nigeria, particularly when accounting for the variance attributed to other factors in the model, in contrast to personalized learning systems, which offered a relatively lesser unique contribution. Based on the findings of the study, the following recommendations among others were made; Educational planners need to guarantee the complete application of AI algorithms, particularly in enhancing budget distribution through the examination of past financial data to forecast upcoming expenses. AI-powered tools should be employed to handle tasks like hiring teachers and conducting performance assessments. Educational planners should create an interactive learning environment that fosters deeper student engagement through AI-powered intelligent tutoring systems. This is due to the fact that intelligent tutoring systems powered by AI can suggest personalized study plans and offer immediate feedback for fundamental school location planning.