Leveraging AI to Preserve and Advance the Hyumbe System Through Community-Led Participation

Abstract

The Hyumbe system is a traditional cultural practice of the Tiv in central Nigeria that blends social cohesion with agricultural wisdom, serving as both a knowledge-sharing platform and a unifying activity within communities. However, this important system is increasingly at risk due to the pressures of modernisation, rural-urban migration, farmer-herder conflict, and declining interest among younger generations. This study explores the potential of artificial intelligence (AI) as a tool to preserve, revitalise, and reimagine the Hyumbe system for future generations. The study aims to document existing knowledge, amplify community voices, and foster interactive learning experiences that resonate with the youth. Key AI tools will be used to collect oral histories, simulate traditional practices, and support accessible education. The outcomes include a rich digital archive of the Hyumbe system, renewed youth participation, and stronger cultural and agricultural continuity. Ultimately, the study seeks to bridge tradition and innovation, ensuring that the Hyumbe system remains a living, evolving part of community life.

Keywords: Hyumbe system, artificial intelligence, community-led participation, cultural preservation, digital archive

Introduction

Modernisation, globalisation, rural-urban migration, and recurring farmer-herder conflicts have contributed significantly to the erosion and abandonment of various African cultural practices. These dynamics often lead to traditional systems being perceived by younger generations as outdated or irrelevant (Gyekye 1997). The Hyumbe system, a dynamic age-grade farming practice, involves individuals of the same age group working together on each other’s farms, boosting productivity, ensuring food security, and reducing reliance on mechanised farming and harmful agricultural chemicals. Hyumbe also integrates cultural activities such as singing, dancing, and storytelling, which strengthen community bonds and provide psychological resilience, foster social cohesion, agricultural expertise, and communal responsibility in specific African communities.

Like many indigenous practices, the Hyumbe system has seen a steady decline due to socio-economic and political pressures. Among Tiv youths, there is a growing disconnection from communal traditions such as the Hyumbe, which are increasingly viewed as incompatible with modern lifestyles and aspirations. According to Akor and Okwori (2016), this represents a cultural loss but also disrupts local knowledge-sharing mechanisms and communal resilience. This study therefore aims to integrate AI technologies with community-led efforts to preserve and advance the Hyumbe system. The objectives are: to document traditional Hyumbe practices, enhance community engagement through digital platforms, and encourage continuity of the practice.

Literature Review

Existing studies highlight the importance of preserving indigenous agricultural systems for cultural identity and sustainable development. The Hyumbe system, as an age grade based practice, aligns with other African communal farming traditions that emphasise collective responsibility (Mafeje 1991). However, research indicates that modernisation and migration threaten such systems, with younger generations showing reduced interest (Ncube 2018). AI technologies, including NLP, machine learning, and AR/VR, have been successfully applied in cultural preservation, such as digitising oral histories and creating virtual heritage experiences (Smith and Jones 2022). Yet, few studies have explored AI’s potential in preserving African agricultural practices through community-led approaches, presenting a gap this study aims to fill.

Theoretical/Conceptual framework

This study is grounded on a proposed theory: the Artificial Intelligence for Indigenous Knowledge Systems (AIIKS) framework. The AIIKS framework is built on an emerging theory that blends indigenous knowledge systems and artificial intelligence in order to make diverse, indigenous, non-Western and non-White epistemologies AI’s centre (Lewis, Whaanga, and Yolgörmez 2025).  The AIIKS framework is suitable for this research because it provides a structured way to use AI technologies to document, preserve, and revitalise traditional practices like the Hyumbe system. It supports the recording and archiving of oral histories, traditional knowledge, and cultural practices, aligning with the study’s goal of creating a rich digital archive. AIKS also focuses on amplifying indigenous voices by empowering communities to tell their own stories through digital tools, ensuring the Tiv people maintain control over how the Hyumbe tradition is represented and shared. Additionally, AIIKS emphasises that indigenous knowledge systems are living and evolving, promoting innovation that respects tradition while adapting to modern realities, which fits the study’s broader goal of bridging tradition and innovation to keep the Hyumbe system vibrant and relevant.

Methodology

This study adopts a mixed-methods approach to collect qualitative and quantitative data from Hyumbe custodians. Oral narratives, farming techniques, and social structures will be gathered through interviews and participatory workshops with community elders, members, and practitioners. NLP tools will transcribe and translate oral narratives into a digital archive, ensuring accessibility (Johnson, Lee, and Patel 2023). Machine learning models will analyse agricultural patterns, such as crop rotation and soil management, to document best practices. The study will be conducted in collaboration with local Hyumbe communities, prioritising their input and leadership.

Ethical Considerations

Ethical considerations are central to this study, especially given that the study area is one of the regions most affected by the Fulani herdsmen conflict. Many participants are displaced and currently living in IDP camps. Their safety, dignity, and privacy will be prioritised at all times. Participation will be fully voluntary and informed consent will be obtained before any data is collected. Participants will be clearly informed about how their data, audio, visual, or written, will be used and stored, and only what they explicitly permit will be included in the final work. No material will be shared without their approval, especially where individuals are identifiable. The study will also respect cultural sensitivities and ensure restricted knowledge is handled appropriately. Data privacy will be maintained through secure storage and anonymisation where necessary. To avoid extractive practices, the research will be community-led and participatory, giving local stakeholders a voice in shaping the process. Any technological gaps will be addressed through training and access to tools to ensure full and fair participation.

Interdisciplinary implications

This study bridges cultural anthropology, computer science, and agricultural studies, demonstrating the potential of interdisciplinary approaches to cultural preservation. By integrating AI with indigenous knowledge systems, the project contributes to global discourse on digital heritage and sustainable development. It also highlights the role of technology in empowering marginalised communities, offering a model for preserving other endangered cultural practices. The findings may inform policy on integrating technology into rural development and education, fostering cross-sectoral collaboration.

Findings

The anticipated findings include a comprehensive digital archive of the Hyumbe system, encompassing oral histories, farming techniques, and social structures. The use of AI-driven tools is expected to enhance community engagement, with increased participation in digital platforms and workshops. Preliminary data suggests that community-led approaches strengthen local ownership, ensuring the system’s relevance and resilience.

Conclusion

Leveraging AI to preserve and advance the Hyumbe system through community-led participation offers a scalable and innovative solution to cultural preservation. By documenting practices, enhancing engagement, and revitalising interest, this study aims to ensure the Hyumbe system’s resilience and continued contribution to cultural identity and sustainable agriculture. The project underscores the power of combining technology with grassroots efforts, paving the way for future initiatives to safeguard indigenous knowledge systems.

Acknowledgments

This journey was a success for me largely because of the amazing support from an outstanding team. Honestly, this is one of the best programs I’ve ever been part of. The Research Round team was intentional, thoughtful, and deeply committed. The facilitators brought real energy and a strong drive to make an impact. What stood out most was the atmosphere; no one ever tried to show off. It was all about collaboration, learning together, and making an impact.

The mentors were incredibly kind, patient, and supportive. One of the highlights for me was the opportunity to co-author a paper with one of my mentors, Mr Frank Onu. It’s a big win I’m truly proud of. My other mentor, Mr Frank Onu, also shared some great articles that helped shape my research. He introduced me to AI tools that turned out to be very useful in my work. I have mentioned a couple of names, but the truth is that every single person brought something valuable to the table, and I appreciate them all.

References

Adebayo, K., and O. I. Oladele. 2020. “Indigenous Knowledge Systems and Sustainable Agriculture in Africa.” Journal of Agricultural Extension 24 (3): 45–56.

Akor, L., and J. Z. Okwori. 2016. “Culture and Identity among the Tiv People of Nigeria.” Makurdi: Aboki Publishers.

Gyekye, K. 1997. “Tradition and Modernity: Philosophical Reflections on the African Experience.” Oxford: Oxford University Press.

Johnson, R., M. Lee, and S. Patel. 2023. “Natural Language Processing for Cultural Preservation: Opportunities and Challenges.” Digital Humanities Quarterly 17 (2): 112–130.

Mafeje, A. 1991. “The Theory and Ethnography of African Social Formations.” Dakar: CODESRIA.

Lewis, J. E., H. Whaanga, and C. Yolgörmez. 2025. “Abundant Intelligences: Placing AI within Indigenous Knowledge Frameworks.” AI & Society 20: 2141–2157. https://doi.org/10.1007/s00146-024-02099-4

Ncube, M. 2018. “Youth Disengagement from Traditional Agricultural Practices in Sub-Saharan Africa.” African Studies Review 61 (1): 89–105.