The Relationship between Participation in Lifelong Learning Models and Reskilling of Workers Displaced by Artificial Intelligence in Cross River State Nigeria

Publication Details

Publication Date

10/08/2026

Volume / Issue

Vol 1, Issue 3 (2026)

Article No.

022

Journal

British Journal of Contemporary Research

DOI

https://doi.org/10.67693/BJCR-6V7YV27S

Received

17 Jul 2026

Views

23

Downloads

11

Abstract

The rapid adoption of Artificial Intelligence (AI) and automation has led to job displacement across several sectors in Nigeria. This study examined the relationship between participation in lifelong learning models and reskilling outcomes among workers displaced by artificial intelligence in Cross River State, Nigeria. Guided by Human Capital Theory, the study adopted a quantitative correlational design with simple linear regression analysis. A sample of 228 workers displaced by AI-driven automation between 2022 and 2026 was selected using purposive and snowball sampling techniques. Data were collected using a validated 5-point Likert scale questionnaire with reliability coefficients of 0.84 and 0.88 for the participation and reskilling outcome scales respectively. Pearson Product Moment Correlation and Simple Linear Regression were used to analyze the data at a 0.05 level of significance. The findings revealed a strong positive and significant relationship between participation in lifelong learning models and reskilling outcomes, r (226) = .61, p < .001. Participation significantly predicted reskilling outcomes and accounted for 37.5% of the variance, F (1, 226) = 135.47, p < .001. The study concluded that engagement with AI-driven and competency-based lifelong learning platforms is a critical pathway for reskilling and employability among workers displaced by AI in Cross River State. It was recommended that government agencies, NGOs, and industry stakeholders should invest in accessible, scalable lifelong learning models to support workforce transition in the age of AI.

License

This article is published under the Creative Commons Attribution 4.0 International License . Free to read, share, and adapt with attribution.