FACTORS AFFECTING INTEGRATION OF ARTIFICIAL INTELLIGENCE IN LEARNING AT MANAGEMENT UNIVERSITY OF AFRICA

Abstract

The purpose of this study is to investigate the factors influencing the integration of Artificial Intelligence (AI) in teaching and learning at the Management University of Africa. Specifically, the study aims: (i) to evaluate the effect of faculty training on the integration of AI in teaching and learning, (ii) to assess the effect of student digital literacy on AI integration, (iii) to examine the effect of institutional support on AI integration and (iv) to evaluate the effect of curriculum alignment on AI integration. This study employed a descriptive research design. The study targeted a population of 480 participants. Using a stratified random sampling technique. A sample of 144 respondents. Data was collected using closed-ended questionnaires. A pilot study involving 14 respondents was conducted beforehand to test the validity and reliability of the instrument. Data collection was carried out by administering the questionnaires physically. The collected data was processed, cleaned and analyzed using descriptive statistics, specifically frequencies and percentages, through Microsoft Excel. Presentation of results was done using tables for structured reporting and visual aids such as bar graphs and pie charts to enhance interpretation. Ethical considerations, including informed consent, voluntary participation, confidentiality, privacy and anonymity, were strictly observed. A total of 138 responses were obtained out of 144 targeted, yielding a high response rate. Descriptive findings showed that 49% of respondents agreed faculty were adequately trained, while 38% remained neutral or disagreed, suggesting gaps in faculty preparedness. On student digital literacy, 77% of respondents agreed that students could access digital information, though 24% indicated limitations. Regarding institutional support, only 46% agreed that adequate financial resources were allocated, while 43% disagreed, highlighting resource constraints. On curriculum alignment, 55% agreed AI was integrated into learning outcomes, while 36% disagreed, reflecting uneven adoption. The study concluded that faculty preparedness, student competencies, institutional commitment, and curriculum design were critical determinants of successful AI adoption. It further concluded that inconsistencies in training, financial resources, and curriculum reforms limited the full potential of AI in enhancing teaching and learning. The study recommended that the university should have strengthened faculty training programs and ensured continuous professional development, promoted structured digital literacy initiatives for students, increased financial allocations and improved ICT infrastructure, and aligned curriculum content and assessments with AI competencies relevant to the job market. It also recommended greater leadership commitment and collaboration with industry stakeholders to sustain AI adoption. The study suggested further research on government policy, cost–benefit analysis, and faculty attitudes toward AI in higher education. Overall, the findings underscored that while progress had been made, comprehensive and consistent strategies were necessary to achieve effective and sustainable integration of AI in university teaching and learning.