The Adoption of Artificial Intelligence in Tertiary Institution in Rivers State: An Empirical Study of Lecturers and Students’ Awareness, Usage, and Challenges

Abstract

This empirical study investigated the Adoption of Artificial Intelligence (AI) in tertiary educational institutions in Rivers State, Nigeria. The study was guided by 2 research questions and 2 null hypotheses. The population consisted of all tertiary institutions in Rivers State. Using a stratified sampling technique, a total sample of 400 lecturers and students was selected. Data were collected through a structured instrument titled Artificial Intelligence Adoption and Challenge Questionnaire (AIACQ), developed by the researcher. The instrument consisted of 12 items rated on a 4-point Likert scale. The reliability of the AIACQ was established through Cronbach’s alpha, yielding a coefficient of 0.87, indicating high internal consistency. Data were analyzed using descriptive statistics, Pearson correlation, and an independent samples t-test, analysis. Findings from Research Question One revealed a mean of 2.38, implying that AI adoption in tertiary educational institutions in Rivers State is to a low extent; actual usage in classroom instruction remained relatively low. A significant positive correlation was found between AI adoption and students’ academic performance (r = 0.29, p < .000). The results in questions two also reveal an average mean of 3.25 which implies that most of the respondents agree that listed items (lack of infrastructure, insufficient training, students' lack access to AI tools, and policies that support AI implementation) are the challenges impeding the integration of AI adoption in tertiary institutions in Rivers State. Additionally, the calculated t-value of -0.653 is less than the t-critical value of 1.96 at a 0.05 level of significance, so null hypothesis 2 is accepted. Hence, there is no significant difference in the challenges impeding the integration of AI in tertiary institutions in Rivers State based on gender. The study recommends targeted professional development programs, equitable infrastructural investment across tertiary institutions, the establishment of a statewide AI policy, and curriculum reforms that promote computational and AI literacy from early education levels.