IMPROVING EDUCATIONAL LEARDERSHIP THROUGH RESPONSIBLE ARTIFICIAL INTELLIGENCE APPLICATIONS IN TERTIARY INSTITUTIONS IN ANAMBRA STATE, NIGERIA

Abstract

This study investigated the influence of responsible Artificial Intelligence (AI) on educational leadership in tertiary institutions in Anambra State, Nigeria. Four research questions and four hypotheses guided the study. The study adopted a descriptive survey research design. The population of the study comprised academic and administrative staff in selected tertiary institutions in Anambra State, with a sample size of 325 respondents selected through purposive and simple random sampling techniques. A structured questionnaire titled Responsible Artificial Intelligence and Educational Leadership Questionnaire (RAIELQ) was used for data collection. The instrument was validated by experts in Educational Management, Measurement and Evaluation, and Educational Technology, while a Cronbach Alpha reliability coefficient of 0.84 was obtained. Data collected were analyzed using mean and standard deviation to answer the research questions, while Pearson Product Moment Correlation, regression analysis and independent t-test were used to test the hypotheses at 0.05 level of significance using SPSS version 26. The findings revealed that educational leaders had moderate awareness of responsible AI applications. The study also found that responsible AI significantly improves decision-making effectiveness and administrative efficiency in tertiary institutions. However, inadequate ICT infrastructure, poor funding, lack of technical expertise and weak policy frameworks were identified as major challenges affecting AI adoption. The hypotheses tested showed significant relationships between AI awareness, adoption, decision-making effectiveness, and administrative efficiency. The study concluded that responsible AI has strong potential to improve educational leadership and institutional management in tertiary institutions when properly implemented. The study recommended regular AI training, improved ICT infrastructure, adoption of AI-driven decision-support systems and development of institutional AI policy frameworks.