INTEGRATING AI IN RURAL LIVELIHOOD AND EMPLOYMENT GENERATION PROGRAMS
Keywords:
Artificial Intelligence, Rural Livelihoods, Precision Agriculture, Digital Upskilling, TelemedicineAbstract
Integrating Artificial Intelligence (AI) into rural livelihood and employment-generation initiatives significantly enhances socioeconomic development by increasing agricultural productivity, facilitating digital skill-building, and improving access to essential services. AI-driven precision agriculture has increased crop yields by 15–54% and reduced resource wastage. Upskilling initiatives, such as Microsoft’s ADVANTA(I)GE and India’s Karya cooperative, offer digital literacy and gig-based employment opportunities, empowering millions of rural workers. Additionally, AI-enabled telemedicine platforms have expanded healthcare access by up to 300%, while governance applications have increased transparency and efficiency in public services. However, the widespread adoption of AI in rural contexts poses challenges including job displacement, digital inequities, and ethical concerns such as algorithmic bias. Addressing these requires inclusive policy frameworks, infrastructure investments, and ethical guidelines. Ultimately, responsible AI deployment holds transformative potential for inclusive rural prosperity.
References
I. “Applications of AI in precision agriculture.” (2025). Springer.
II. “Artificial Intelligence in Agriculture: Advancing Crop Productivity and Sustainability.” (2025). ResearchGate.
III. “Artificial Intelligence in sustainable agriculture.” (2025). Computers & Electronics in Agriculture.
IV. “Enhancing precision agriculture: A comprehensive review.” (2024). ScienceDirect.
V. “Integration of IoT–AI powered local weather forecasting.” (2024). arXiv.
VI. “Precision agriculture.” (2025). Wikipedia.
VII. Agro Deep Learning Framework achieving 85% accuracy in crop stress detection (2024). BMC Bioinformatics.
VIII. Ashe, G., & Mengistu, D. (2025). Future of Farming: A review of AI applications in agriculture. Asian Science Bulletin, 3(1), 82–91.
IX. Ayim, C., Kassahun, A., Tekinerdogan, B., & Addison, C. (2020). Adoption of ICT innovations in the agriculture sector in Africa: A systematic literature review. arXiv.
X. Casaburi, L., Glennerster, R., & Suri, T. (2014). The effect of information on agricultural productivity: Evidence from mobile phone–based advisory services in India. Agricultural Systems, 126, 131–139.
XI. Digital Agriculture. (2025). In Wikipedia. Retrieved July 2025.
XII. IFPRI. (2017). Rural youth migration in Ethiopia. International Food Policy Research Institute.
XIII. Naidoo, G. M. (2024). The potential of artificial intelligence in South African rural development. OIDA International Journal of Sustainable Development, 17(11), 207–218.
XIV. News: Kenyan farmers using AI tripled coffee yields. The Guardian, Sep 2024.
XV. News: Malawi’s Ulangizi AI chatbot. Time, 2024.
XVI. ResearchGate. (2025). Leveraging AI for rural development. ResearchGate.
XVII. ScienceDirect. (2025). Artificial intelligence development and rural labor employment quality. International Review of Economics & Finance.
XVIII. Wikipedia. (2025a). National Rural Livelihood Mission. Retrieved July 2025.
XIX. Wikipedia. (2025b). Mahatma Gandhi National Rural Employment Guarantee Act, 2005. Retrieved July 2025.
XX. Zenodo. (2025). Challenges of AI integration in rural development in India.
Additional Files
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 International Educational Journal of Science and Engineering

This work is licensed under a Creative Commons Attribution 4.0 International License.