PERCEPTIONS OF INTEGRATION OF ARTIFICIAL INTELLIGENCE(AI) AS A SUPPORT TOOL IN ACADEMIC PURSUITS: EVIDENCE FROM UNIVERSITY OF ABUJA UNDERGRADUATES

Abstract

This study examined undergraduate students’ perceptions and acceptance of artificial intelligence (AI) tools in academic pursuits at the University of Abuja. Guided by the Technology Acceptance Model and Diffusion of Innovations Theory theoretical frameworks, the research employed a survey design with a sample of 400 enrolled students selected using Krejcie and Morgan Sample Size Table. Data were collected through a structured questionnaire covering constructs such as perceived usefulness, ease of use, trust, relative advantage, compatibility, ethical concerns, and acceptance. Reliability was confirmed using Cronbach’s alpha coefficients above 0.70. Findings revealed near-universal awareness of AI tools (99%) and widespread adoption, with 50.2% of students reporting occasional use and 35.8% reporting daily use. Trust (72%) and perceived improvement in academic quality (74%) emerged as strong predictors of comfort, while concerns about plagiarism (37.6%) and originality (48.3%) reduced acceptance. Chi-square tests showed significant associations between age, gender, and usage patterns, with younger students and female students reporting higher levels of trust and adoption. Spearman's Rank Correlation Coefficient analysis confirmed a moderate positive correlation between trust and comfort (ρ = .46, p < .001). Regression analysis explained 41% of the variance in comfort, highlighting trust and perceived usefulness as the most influential predictors. These findings support the central assumptions of the Technology Acceptance Model regarding perceived usefulness and trust as drivers of adoption, while the Diffusion of Innovations Theory explains demographic and disciplinary differences in AI acceptance. The study concludes by emphasizing the need for faculty-specific guidelines, ethical AI literacy programmes, and institutional policies that balance AI innovation with academic integrity. The study recommends that universities promote ethical AI literacy through the integration of plagiarism detection mechanisms and proper citation practices into academic training.