Data Collection Methods for Artificial Intelligence and Machine Learning Research
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-05
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About the Article
Data collection is the cornerstone of artificial intelligence (AI) and machine learning (ML), directly shaping the accuracy and fairness of resulting models. This article reviews quantitative, qualitative, and mixed-methods approaches, along with participatory and digital tools such as mobile apps and online survey platforms. Special emphasis is placed on adapting data collection to low-resource settings, where infrastructure and capacity constraints require flexible and ethical solutions. Case studies from healthcare, agriculture, education, and humanitarian aid illustrate the integration of methodologies in practice. Ethical principles—including informed consent, data privacy, and cultural sensitivity—are foregrounded as essential to trustworthy research. The article provides scholars and practitioners with guidance for designing inclusive, responsible, and effective data collection strategies that underpin robust AI and ML systems.
Key Topics
- Quantitative and qualitative data collection
- Mixed-methods research design
- Digital tools for surveys and mobile data gathering
- Ethical frameworks for responsible AI research
- Strategies for low-resource settings
- Case studies across healthcare, agriculture, education, and aid
Suggested Citation
Kazanskaia, A. N. (2025). Data Collection Methods for Artificial Intelligence and Machine Learning Research. NEYA Global Journal of Non-Profit Studies. Neya Global Publishing. https://doi.org/10.64357/neya-gjnps-ai-mch-lr-05
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