META AI ADOPTION AND EDUCATIONAL USE AMONG UNDERGRADUATE STUDENTS IN NORTH-CENTRAL NIGERIA: THE LIMITED ROLE OF DEMOGRAPHIC FACTORS
Keywords:
Educational Technology, Generative Artificial Intelligence, Higher Education, Meta AI, Technology Adoption, Undergraduate StudentsAbstract
The increasing adoption of generative artificial intelligence (AI) in higher education has transformed teaching and learning by providing intelligent tools that support academic activities. However, empirical evidence on whether undergraduate students' demographic characteristics influence Meta AI adoption and educational use remains limited, particularly in Nigeria. This study examined the level of Meta AI adoption among undergraduate students in selected universities in North-Central Nigeria and investigated whether selected demographic characteristics significantly influenced Meta AI adoption and educational use. A cross-sectional survey research design was adopted. A sample of 382 undergraduate students was selected using a multistage sampling procedure from a population of 83,106 students. Data were collected using the researcher-developed Meta AI Adoption and Use for Learning Questionnaire (MAAULQ), which yielded a Cronbach's alpha reliability coefficient of 0.89. Descriptive statistics and multiple linear regression were employed for data analysis at the 0.05 significance level. The findings revealed a high level of Meta AI adoption (Grand Mean = 3.91, SD = 0.98). However, gender, age, level of study, device ownership, and internet access did not significantly predict either Meta AI adoption, F(5, 376) = 0.74, p = .599, R² = .010, or Meta AI educational use, F(5, 376) = 0.98, p = .430, R² = .013. The study concludes that undergraduate students' demographic characteristics explain little variation in Meta AI adoption and educational use, suggesting that AI integration strategies should prioritise inclusive access and responsible educational use rather than demographic targeting.
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