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

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