Artificial Intelligence Support Systems Use and Academic Staff Job Performance: Theoretical Review

Abstract

This study critically examined the relationship between Artificial Intelligence Support Systems
Use (AISSU) and Academic Staff Job Performance (ASJP) within the context of education. It
combined perspectives from Nigeria, Africa and around the world, emphasizing the opportunities
and challenges of the growing integration of AI into research, teaching and administrative
procedures. The paper established a conceptual basis for understanding how academic staff interact
with AI-driven tools by drawing on four theoretical frameworks: the Socio-Technical Systems
Theory (STST), the Technology Acceptance Model (TAM), Diffusion of Innovation (DOI) and
the Unified Theory of Acceptance and Use of Technology (UTAUT). The review shows that
AISSU can improve efficiency, productivity and innovation when properly implemented and
institutionally supported, thereby lowering workload, increasing teaching efficacy and developing
research capabilities. However, adoption is still significantly shaped by obstacles like limited AI
literacy, infrastructural gaps and innovation reluctance. The theoretical synthesis emphasizes that
integrating AI tools into dynamic knowledge networks that support academic practice, maintaining
institutional readiness and striking a balance between human and technical subsystems are all
necessary for successful use of Artificial Intelligence Support Systems (AISS). This conceptual
overview offers a multidimensional framework for researchers and policymakers looking to use
AI to enhance academic staff performance within the university system.