Authors : Ede Ifesinachi Chizzy; Abubakar Bello Bada; Sirajo Abdullahi Bakura; Ibrahim Musa Mungadi; Bashar Tukur Shehu; Abdullahi Usman Gulumbe; Abdulsalam Ibrahim Magawata; Mubashir Haruna

Volume/Issue : Volume 10 - 2025, Issue 12 - December

Google Scholar : https://tinyurl.com/mjju52xt

Scribd : https://tinyurl.com/yyey6yv9

DOI : https://doi.org/10.38124/ijisrt/25dec450

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Note : Google Scholar may take 30 to 40 days to display the article.

Abstract : Artificial Intelligence (AI) prompting has emerged as a pivotal skill in the era of generative AI, transforming the way humans interact with intelligent systems. This paper explores the evolution of prompt engineering as both a technical discipline and an emerging form of digital labor, emphasizing its strategic relevance to Nigeria’s technological and socio- economic future. Anchored in the dual theoretical frameworks of Digital Labor Theory and Socio-Technical Systems Theory, the study examines how prompt engineering shapes digital competencies, creative work, and national productivity. The analysis reveals significant opportunities in education, entrepreneurship, and innovation ecosystems, while identifying critical challenges including infrastructural deficits (power and connectivity), digital literacy gaps, high costs of premium AI tools, and algorithmic bias against local cultural contexts. The paper proposes strategic recommendations including curriculum integration, government-private partnerships, infrastructure development, and indigenous AI model development (such as N-ATLAS) to position Nigeria as an active participant in the global AI economy. The study concludes that prompt engineering represents a transformative pathway for Nigerian youth to access global digital earnings, but requires coordinated national action to dismantle structural barriers and ensure inclusive, sustainable development.

Keywords : Prompt Engineering; Artificial Intelligence; Digital Labor; Socio-Technical Systems; Nigeria; Technological Development.

References :

1. Adeola, O., & Olayinka, F. (2024). Prompt literacy as a key enabler of AI adaptation in emerging economies. *African Journal of Technology*, 12(3), 45-62.
2. Baxter, G., & Sommerville, I. (2011). Socio-technical systems: From design methods to systems engineering. *Interacting with Computers*, 23(1), 4-17.
3. Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? *Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency*.
4. Bommasani, R., Hudson, D. A., Adeli, E., et al. (2021). On the Opportunities and Risks of Foundation Models. *arXiv preprint arXiv:2108.07258*.
5. Brown, T., Mann, B., Ryder, N., et al. (2020). Language Models are Few-Shot Learners. *Advances in Neural Information Processing Systems*, 33, 1877–1901.
6. Federiakin, D. (2024). Prompt engineering as a new 21st century skill. *Frontiers in Education*. https://doi.org/10.3389/feduc.2024.1366434
7. Ji, Z., Lee, N., Frieske, R., et al. (2023). Survey of Hallucination in Natural Language Generation. *ACM Computing Surveys*, 55(12), 1-38.
8. Laurito, W., et al. (2025). AI–AI bias: Large language models favor communications produced by LLMs. *PNAS*. https://doi.org/10.1073/pnas.2415697122
9. Liu, P., Yuan, W., Fu, J., et al. (2023). Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing. *ACM Computing Surveys*, 55(9), 1-35. https://doi.org/10.1145/3560810
10. National Digital Economy Policy. (2022). Nigeria’s strategic framework for digital transformation. Federal Ministry of Communications and Digital Economy.
11. National Information Technology Development Agency (NITDA). (2024). Strategic Roadmap and Action Plan (SRAP 2.0). https://nitda.gov.ng/wp-content/uploads/2024/02/SRAP-2.O.pdf
12. OpenAI. (2023). GPT-4 Technical Report. *arXiv preprint arXiv:2303.08774*.
13. Reynolds, L., & McDonell, K. (2021). Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigm. *arXiv preprint arXiv:2102.07350*.
14. Smith, J. (2024). The Future of Prompt Engineering. *Journal of Artificial Intelligence Research*, 78, 45-62.
15. Trist, E. L., & Emery, F. E. (1951). Socio-technical systems. In *Management Sciences Models and Techniques* (Vol. 2). Pergamon.
16. Vu, A., et al. (2025). Analyzing skill requirements in the AI job market. *arXiv:2506.00058*. https://arxiv.org/abs/2506.00058
17. Wei, J., Wang, X., Schuurmans, D., et al. (2022). Chain-of-Thought Prompting Elicits Reasoning in Large Language Models. *Advances in Neural Information Processing Systems*, 35, 24824–24837.
18. White, M. (2023). The Rise of Prompt Engineering as a Professional Skill. *AI & Society*, 38(3), 455–470.
19. Yao, S., et al. (2024). Cognitive implications of prompt engineering. *Journal of Cognitive AI*, 15(2), 112-128.
20. Ye, Q. (2023). Prompt Engineering a Prompt Engineer. *arXiv:2311.05661*. https://arxiv.org/abs/2311.05661
21. Zhao, W., et al. (2024). Advances in human-AI interaction through prompt engineering. *Computational Linguistics Review*, 18(4), 234-251.
22. Zhou, C., Wu, C., Zhao, S., et al. (2022). Large Language Models are Human-Level Prompt Engineers. *arXiv preprint arXiv:2209.14610*.