Hypothetical Modelling of Artificial Intelligence Usage Intention Among Business Education Lecturers in Nigerian Universities

Abstract

 This study proposes a model that explains and predicts artificial intelligence usage intention
among business education lecturers in Nigerian universities based on the theoretical conventions
of Davis's Technology Acceptance Model (TAM) (1989) and Venkatesh, Morris, Davis, and
Davis's Unified Theory of Acceptance and Use of Technology (UTAUT) (2003). The study
indicates that although the business education program in Nigeria has progressed since its
inception, it encounters considerable challenges in modernizing teaching methods through
integrating emerging technologies such as AI. These challenges include inadequate
infrastructure, resistance to change, and a persistent preference for outdated tools. The study
model will test how lecturers' AI usage expectations and perceptions can predict their intentions
through self-reported opinions. The model will offer empirical data on AI adoption in education,
focusing on business education lecturers in universities in the southeastern region of Nigeria. The
study will also provide lecturers with insights into using AI tools in the classroom and statistical
data on AI usage behavior among lecturers. The study will benefit AI developers by giving them
information on how to improve their tools' features, and it will also contribute to bridging the gap
in AI research between Nigeria and the rest of the world. The study recommends that the Nigerian
government conduct more workshops and certifications to enhance lecturers' AI skills. The study
also recommends that university authorities educate lecturers on the benefits of AI for
performance improvement. It also recommends that the Nigerian government improve internet
connectivity, support training and infrastructure, and provide modern devices to university
lecturers, as these are essential for successful AI integration. Although significant, the study is
constrained by its preliminary nature and its lean focus on integrating existing literature.