Artificial Intelligence-Driven Learning Systems and Training Efficiency: Evidence from the Nigerian Banking Sector

Abstract

Training and development (T&D) functions within financial institutions in emerging economies continue to grapple with the inefficiencies of uniform instructional models that fail to account for individual skill trajectories. This study examines the effect of AI-driven training and development on T&D process efficiency within the Nigerian banking sector, guided by the hypothesis that AI-driven training and development significantly affects training and development process efficiency (H4). Anchored in the Technology Acceptance Model (TAM) and the Ability-Motivation-Opportunity (AMO) framework, a quantitative cross-sectional survey design was employed. Structured five-point Likert-scale questionnaires were administered to banking professionals across selected deposit money banks in Gwagwalada, Abuja, yielding 124 usable responses from a population of 149. Data were analyzed using multiple linear regression via SPSS. Results indicate a very strong predictive relationship (R = .878, R² = .770, F = 100.672, p < .001), with AI-driven training explaining 77% of the systematic variance in process efficiency. Competency Growth (β = .807, p < .001) and Learning Personalization (β = .789, p < .001) emerged as the dominant predictors, while Training Cost reduction was also statistically significant (β = .132, p = .018). The null hypothesis is decisively rejected. Findings underscore AI's role as a strategic catalyst for human capital development in digital-era banking, with implications for HRD practitioners, institutional strategists, and policymakers in Sub-Saharan Africa

DOI: https://doi.org/10.5281/zenodo.22953489