Teaching Paper: AI and Machine Learning Tools and Resources for Non-Profit Innovation
Author: Dr. Anna Neya Kazanskaia
Publisher: NEYA Global Publishing
Article | NEYA Global Journal of Non-Profit Studies
Year: 2025
ORCID: https://orcid.org/0009-0009-5669-1676
DOI: https://doi.org/10.64357/neya-gjnps-ai-mch-lr-tp-01
This material forms part of the NEYA Global knowledge architecture. Institutional, organizational, professional, training, consulting, curriculum, program design, or implementation use requires an active license from NEYA Global
Explore Architecture →
Module Overview →
Request Institutional Access →
About the Article
Artificial Intelligence (AI) and Machine Learning (ML) are increasingly relevant to the non-profit sector, where they provide opportunities to improve efficiency, broaden reach, and strengthen evidence-based decision-making. This Teaching Paper introduces key open-source and commercial AI/ML tools—including TensorFlow, PyTorch, scikit-learn, Hugging Face Transformers, IBM Watson, and Microsoft Azure AI—and contextualizes their use in resource-constrained environments. It highlights practical applications for fundraising analytics, community needs assessments, and program monitoring, while addressing challenges of data readiness, technical expertise, and ethical safeguards. A case study illustrates how non-profits can implement AI solutions with minimal resources. By bridging academic perspectives with practical guidance, the paper equips scholars and practitioners with strategies for responsible and sustainable AI integration.
Key Topics
- Open-source AI/ML tools for non-profits
- Commercial AI platforms and their applications
- Data readiness and ethical safeguards
- Practical strategies for adoption in low-resource environments
- Case study on AI implementation in East Africa
- Academic debates on digital inclusion and innovation
Suggested Citation
Kazanskaia, A. N. (2025). Teaching Paper: AI and Machine Learning Tools and Resources for Non-Profit Innovation. NEYA Global Publishing. https://doi.org/10.64357/neya-gjnps-ai-mch-lr-tp-01
References
| Bryson, J. M. (2018). Strategic planning for public and nonprofit organizations. John Wiley & Sons. | ||||
| Drucker, P. F. (1990). Managing the non-profit organization: Principles and practices. HarperCollins. | ||||
| Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255-260. https://doi.org/10.1126/science.aaa8415 | ||||
| Kazanskaia, A. N. (2025). AI and Machine Learning. NEYA Global Publishing. https://doi.org/10.64357/ai-and-machine-learning-2025 | ||||
| Kazanskaia, A. N. (2025). Technology Tools for Efficiency & Impact. NEYA Global Publishing. https://doi.org/10.64357/technology-tools-2025 | ||||
| Kazanskaia, A. N. (2025). Guide to Digital Transformation. NEYA Global Publishing. https://doi.org/10.64357/guide-to-digital-transformation-2025 | ||||
| Kazanskaia, A. N. (2025). Digital Collaboration Tools: Enhancing Team Productivity. NEYA Global Publishing. https://doi.org/10.64357/digital-collaboration-tools-2025 | ||||
| Kazanskaia, A. N. (2025). Artificial Intelligence and Machine Learning in the Non-Profit Sector: Opportunities, Challenges, and Applications. NEYA Global Journal of Non-Profit Studies. https://doi.org/10.64357/neya-gjnps-ai-mch-lr-08 | ||||
| Kazanskaia, A. N. (2025). Artificial Intelligence Fundamentals: Classifications, Core Technologies, and Computational Techniques. NEYA Global Journal of Non-Profit Studies. https://doi.org/10.64357/neya-gjnps-ai-mch-lr-03 | ||||
| Patton, M. Q. (2008). Utilization-focused evaluation (4th ed.). SAGE. | ||||
