DIGITAL COMPETENCE, A PREDICTOR OF APPLICABILITY OF ARTIFICIAL INTELLIGENCE IN MANAGING HIGHER LEARNING INSTITUTIONS IN NIGERIA ON ECONOMIC-EDUCATIONAL OUTCOME

Abstract

The integration of Artificial Intelligence (AI) in higher learning institutions has transformed educational landscapes globally. Nigeria, with its vision to become a knowledge-based economy, has recognized the importance of AI in driving innovation and competitiveness. However, the success adoption of AI in Nigerian higher education institutions faces significant challenges, primarily due to the digital divide and inadequate digital competence among staff. Nigeria is faced with limited digital infrastructure, inadequate staff training, low digital literacy rate and resistance to technological change. These challenges hinder the effective application of AI, comprising the quality of education and Nigeria’s competitiveness in the global knowledge economy. Digital competence, defined as the ability to effectively use digital technologies to achieve specific goals, is a critical precursor to AI adoption. The staff’s digital competence affects their ability to harness AI’s potential, integrate AI-based tools, and facilitate student learning outcomes. Digital competence is an important factor in predicting the applicability of Artificial Intelligence otherwise known as generative AI. The integration of Artificial intelligence (AI) in higher learning institutions has transformed educational management. This empirical research study investigated digital competence as a predictor of applicability of Artificial intelligence (AI) in managing Nigerian higher learning institutions on economic-educational outcome. While studies have explored AI adoption in education, few have investigated the relationship between digital competence and AI applicability specifically among staff in Nigeria higher institutions of studies. This study aims to bridge the gap by examining the level of digital competence among staff and examine how it predicts the successful application of AI technologies in private and public universities in Anambra state taking into account gender, years of experience and discipline. The study likewise examines the significant association between digital literacy (competence), technical skills, information literacy and research competence applicability of AI technologies by staff in Nigerian higher learning institution on economic-educational outcome. By addressing these objectives, this study will contribute to the development of a digitally competent workforce, facilitating effective AI integration and improving educational outcome sin Nigeria higher learning institution. The study made use of a mixed-methods approach, combining surveys, interviews and econometrics analysis.  A sample size of 4,701 was used drawn from a population that consists of 15,768 lecturers with at least 3 years working experience. Digital competence model was used for data collection while mean and percentages were used to analyzed demographic varaibles. Multi-regression analysis and the chi-square distribution at P < 0.001 level of significance to determine the validity or otherwise of the hypothesis was used for data analysis. The results are reported in tables and represented graphically. The result of the study shows that digital competence significantly predicts applicability of AI in managing higher learning institutions in Nigeria. In addition, there is significant difference between digital competence of lecturers and applicability of AI in public and private universities in the state, among others. The implications of the study will inform policymakers, educators and administrators on strategies to enhance digital competence and AI adoption, ultimately improving economic-educational outcomes in Nigeria higher education. It will also improve educational outcomes through effective AI integration and enhance Adoption strategies in Nigeria higher learning institutions