The impact of Artificial Intelligence and Machine learning on workforce skills and economic mobility in developing countries: A case study of Ghana and Nigeria

Authors

  • Abdulgaffar Muhammad Ahmadu Bello University https://orcid.org/0000-0003-3701-1160
  • Uwaisu Abubakar Umar Department of Computer Science, Modibbo Adamawa University; Nigeria
  • Fatima Labaran Adam Department of Public Administration, Ahmadu Bello University, Zaria, Nigeria

DOI:

https://doi.org/10.56556/jtie.v2i1.466

Keywords:

Artificial Intelligence, Machine Learning, Workforce Skills, Economic Mobility

Abstract

This study investigates the impact of Artificial Intelligence (AI) and Machine Learning (ML) technologies on workforce skills and economic mobility in Ghana and Nigeria. Using a qualitative research design, the study involves a literature review and data collection through interviews and focus groups with workers, educators, employers, and policymakers in both countries. The study shows that the adoption of AI and ML technologies is creating a growing demand for workers with complementary skills, leading to a skills gap in the workforce as the education systems in these countries struggle to keep up with the demand. The research study highlights the need for policies and strategies to address the skills gap and promote economic mobility. The study's recommendations can inform policymakers, educators, and employers in these countries on necessary steps to prepare the workforce for the changing demands of the future of work. Overall, this study provides a comprehensive analysis of the qualitative aspects of data collection and analysis and the impact of AI and ML on workforce skills and economic mobility in Ghana and Nigeria.

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Published

2023-03-26
CITATION
DOI: 10.56556/jtie.v2i1.466

How to Cite

Muhammad, A., Umar, U. A., & Adam, F. L. (2023). The impact of Artificial Intelligence and Machine learning on workforce skills and economic mobility in developing countries: A case study of Ghana and Nigeria. Journal of Technology Innovations and Energy, 2(1), 55–61. https://doi.org/10.56556/jtie.v2i1.466

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Section

Research Articles

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