Barriers and enablers for adopting artificial intelligence for learning and teaching: evidence from Nigeria

Artificial intelligence (AI) has emerged as a vital tool for humanity due to the numerous advantages its usage offers to many sectors, including education. This study adopts a quantitative approach to identify the key barriers and enablers influencing the adoption of AI for teaching and learning in educational institutions, and develops a structural equation model (SEM) to examine their underlying relationships by testing hypotheses through statistical methods. A structured questionnaire was administered to 210 undergraduate students studying built environment programs (architecture, building, estate management, quantity surveying, surveying and geoinformatics, and urban and regional planning) within Nigeria. The reliability and validity of the results obtained were assessed using Cronbach’s alpha; afterwards, the data retrieved from the survey were analyzed using descriptive statistics, Kruskal–Wallis H-test, exploratory factor analysis (EFA), and SEM. Findings from the study revealed that the significant enablers of AI adoption can be grouped into two underlying constructs: AI adoption framework and AI-driven education. The EFA results show that the critical barriers to AI adoption can be categorized into three underlying constructs: capacity barriers, cost factors, and skill gaps. Also, the partial least squares (PLS)-SEM results show that the AI adoption framework positively and substantially influences cost factors in AI adoption for teaching and learning in an educational environment. The current study suggests ways to overcome the barriers militating against the adoption of AI for learning and teaching and further suggests ways for the implementation of the enablers as well.