By: Glenice Deterville
August 25, 2024
Artificial intelligence (AI) and community-based research will increasingly intersect as technology develops, which will be crucial for countries and islands addressing global issues. AI’s capacity to examine massive datasets and spot trends greatly enhances researcher productivity. However, when paired with local expertise and knowledge from community-based research, AI creates new avenues for addressing challenging problems like rural development, education, and climate change. AI and community-based research work together to open doors to more efficient, inclusive, and adaptable solutions to some of society’s issues.
Connecting Global Understanding with Local Knowledge Community-Based Research
Community-based research (CBR) is a collaborative approach that not only engages community members as partners in the research process but also empowers them to ensure that the research is based on the lived experiences and needs of the community. According to Israel et al. (2013), this method, which values and incorporates the unique insights of community members, has proven particularly effective in solving problems that require a deep understanding of local contexts, such as the impacts of climate change, educational disparities, and rural development challenges. Empowering community members through CBR inspires us to know the potential of this approach.
Artificial intelligence, on the other hand, offers a powerful way to analyze complex data sets, exhibit potential outcomes, and optimize solutions. Integrating AI with CBR not only improves the research process by providing advanced analytical capabilities but also promises to optimize solutions to complex problems, thereby increasing the efficiency and effectiveness of research (Cohen & Nelson, 2019).
This connection between AI and CBR creates a dynamic where global technological advances are directly influenced by local knowledge. This makes research more effective and highlights the important role that community knowledge plays in shaping global solutions. It also makes the community/individual feel valued and included in the research process, ensuring that the solutions developed are more likely to be adopted and sustained by the communities they are designed for.
Climate Change: Targeted AI Responses for Global Challenges
Climate change is a global issue that affects communities differently depending on geography, economic status, and environmental conditions. Community-based research plays a critical role in understanding how climate change affects specific communities and what measures are most effective in mitigating these impacts.
AI has the potential to revolutionize climate change research by processing massive amounts of climate data, predicting future trends, and identifying the most effective interventions (Rolnick et al., 2019). For example, AI-driven models can help communities anticipate the impacts of extreme weather events, plan for climate adaptation, and optimize resource use for sustainability (Vinuesa et al., 2020).
Combined with the insights gained from CBR, AI ensures that these models are scientifically robust and practically applicable in the local context, providing security and confidence in the research’s effectiveness. This collaboration enables communities to take proactive steps to address climate change, supported by advanced technology and a deep understanding of their individual needs and circumstances (Berryhill et al., 2019).
AI and Community Engagement for Individualized Learning
AI and community-based research can revolutionize the way we understand and address the pedagogical challenges in education. While AI can analyze patterns in student performance and predict educational outcomes, CBR ensures that these insights are based on the reality of students’ lives and the contexts in which they learn (Holmes et al., 2019).
For example, AI can identify gaps in educational achievement in diverse communities and help educators develop targeted interventions that address specific needs (Luckin et al., 2016). AI-driven tools can also personalize learning experiences and adapt content to the pace and learning style of individual students (Perrotta & Selwyn, 2020).
When these AI capabilities are combined with CBR’s insights, the resulting strategies are more likely to be accepted and implemented by the community. This is because they are based not only on data but also on a sound understanding of the local context, including the social, cultural, and economic factors that influence rural life (Anderson & Galvan, 2021).
Challenges and Ethical Considerations
While incorporating AI and community-based research is promising, it presents significant challenges and ethical considerations. A major challenge is ensuring that AI does not overshadow or marginalize the voices of the communities involved. AI must enhance, not replace, the knowledge and agency of local communities (Cohen & Nelson, 2019).
Data privacy, algorithmic bias, and transparency issues also need to be carefully managed. Communities should be fully informed about how their data is used and have a say in the design and deployment of AI tools (Hilbert, 2020). Ensuring that AI algorithms are transparent and bias-free is also crucial to prevent reinforcing existing inequalities (Vinuesa et al., 2020).
Future Public Policy Collaboration
The intersection of AI and community-based research represents a powerful new approach to tackling some of the world’s most pressing challenges. By combining AI’s advanced analytical capabilities with CBR’s local knowledge and commitment, we can develop solutions that are not only effective but also fair and sustainable.
As we look to the future, collaboration between AI and community-based research will play an increasingly important role in public policy. Whether addressing the impacts of climate change, improving educational outcomes, or promoting sustainable rural development, this integrated approach offers an opportunity to leverage technology and local knowledge.
However, the success of this approach depends on a commitment to ethical research practices, community engagement, and the responsible use of AI. By prioritizing these principles, we can ensure that all the benefits of AI and CBR are shared, leading to a fairer and more sustainable world.
References
Anderson, C., & Galvan, M. (2021). Artificial Intelligence and Rural Development: A Review of the Literature. Rural Sociology, 86(3), 540-563.
Bachmann, N., Tripathi, S., Brunner, M., & Jodlbauer, H. (2022). The Contribution of Data-Driven Technologies in Achieving the Sustainable Development Goals. Sustainability, 14(5), 2497.
Berryhill, J., et al. (2019). Using Artificial Intelligence to Enhance the Capabilities of the Public Sector. OECD Working Papers on Public Governance No. 36.
Cohen, J. E., & Nelson, K. (2019). AI and Community-Engaged Research: Prospects for Public Health and Beyond. Journal of Public Health Policy, 40(2), 165-176.
Hamal, O., Hamal, O., & Lu, J. (2022). Artificial Intelligent in Education. Sustainability, 14(5), 2862.
Hilbert, M. (2020). Digital Divide and Artificial Intelligence: A Threat to Sustainable Development? Technology in Society, 61, 101258.
Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial Intelligence in Education: Promises and Implications for Teaching and Learning. Center for Curriculum Redesign.
Israel, B. A., Eng, E., Schulz, A. J., & Parker, E. A. (Eds.). (2013). Methods for community-based participatory research for health. John Wiley & Sons.
Kshetri, N. (2019). Artificial Intelligence in Rural Development: A Review of AI-Driven Initiatives. Journal of Rural Studies, 67, 165-173
Luckin, R., et al. (2016). Intelligence Unleashed: An argument for AI in Education. Pearson Education.
Perrotta, C., & Selwyn, N. (2020). Deep Learning Goes to School: Toward a Relational Understanding of AI in Education. Learning, Media and Technology, 45(3), 243-256.
Relich, M. (2023). A Data-Driven Approach for Improving Sustainable Product Development. Sustainability, 15(8), 6736.
Rolnick, D., et al. (2019). Tackling Climate Change with Machine Learning. arXiv preprint arXiv:1906.05433.
Unleash the Power of AI: Revolutionize Climate Change Analysis and Ignite a Phenomenal Transformation – Business, Finances, Tech, Markets. https://busilon.com/unleash-the-power-of-ai-revolutionize-climate-change-analysis-and-ignite-a-phenomenal-transformation/
Vinuesa, R., et al. (2020). The role of artificial intelligence in achieving the Sustainable Development Goals. Nature Communications, 11(1), 1-10.
Wrigley, C. (2020). Designing AI to Enhance Learning: The Role of Human-Centered Approaches. AI & Society, 35(2), 261-277.
Zhang, C., et al. (2020). AI in Agriculture: Deep Learning Applications for Sustainable Rural Development. IEEE Access, 8, 147644-147662.