Paper Type

Complete

Abstract

Indigenous knowledge comprises ecological and environmental knowledge, agricultural knowledge, medicinal knowledge, historical knowledge and cultural heritage and craftmanship deeply imbedded in the society (Sergon, P. J., Akoth, S. O., & Dzinekou, J. Y. 2022). The knowledge plays a critical role in social, political and economic development. Specialized libraries are libraries attached to entities such as government departments, corporate institutions and research bodies and usually serve the interests of the parent bodies. The libraries are uniquely positioned to capture, manage and share knowledge including Indigenous knowledge (Gosart, U. 2021). However, the absence of a dedicated platform within the libraries to identify, document, preserve, synthesize and disseminate indigenous knowledge jeopardizes its management. (Mdhluli, T. D., Mokgoatšana, S., Kugara, S. L., & Vuma, L. 2021). Furthermore, oral traditions usually tend to fade away over time due to their tacit and experiential characteristics, and their unstructured and inherently fragile nature. Artificial intelligence technologies such as knowledge graphs, automated transcription, natural language processing, machine learning, and expert systems have the potential to capture, manage, preserve and optimize indigenous knowledge for use and reuse (Jha, S. K. 2023). Previous studies show that Kenyan academic libraries have begun exploring the potential use of artificial intelligence technologies in running their activities although awareness and readiness remains a major challenge (Masinde, J. M., Mugambi, F., & Wambiri, D. M. 2024). Cox, A. M., & Mazumdar, S. (2024) in their study that investigated the use of artificial intelligence in libraries assert that lack of awareness, systemic factors, infrastructural limitations and the absence of clear and comprehensive policies continue to pose significant challenges in the adoption of the technology particularly in emerging economies.

Paper Number

1530

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Aug 19th, 12:00 AM

A Systematic Review of Artificial Intelligence and Indigenous Knowledge Management in Special Libraries in Kenya

Indigenous knowledge comprises ecological and environmental knowledge, agricultural knowledge, medicinal knowledge, historical knowledge and cultural heritage and craftmanship deeply imbedded in the society (Sergon, P. J., Akoth, S. O., & Dzinekou, J. Y. 2022). The knowledge plays a critical role in social, political and economic development. Specialized libraries are libraries attached to entities such as government departments, corporate institutions and research bodies and usually serve the interests of the parent bodies. The libraries are uniquely positioned to capture, manage and share knowledge including Indigenous knowledge (Gosart, U. 2021). However, the absence of a dedicated platform within the libraries to identify, document, preserve, synthesize and disseminate indigenous knowledge jeopardizes its management. (Mdhluli, T. D., Mokgoatšana, S., Kugara, S. L., & Vuma, L. 2021). Furthermore, oral traditions usually tend to fade away over time due to their tacit and experiential characteristics, and their unstructured and inherently fragile nature. Artificial intelligence technologies such as knowledge graphs, automated transcription, natural language processing, machine learning, and expert systems have the potential to capture, manage, preserve and optimize indigenous knowledge for use and reuse (Jha, S. K. 2023). Previous studies show that Kenyan academic libraries have begun exploring the potential use of artificial intelligence technologies in running their activities although awareness and readiness remains a major challenge (Masinde, J. M., Mugambi, F., & Wambiri, D. M. 2024). Cox, A. M., & Mazumdar, S. (2024) in their study that investigated the use of artificial intelligence in libraries assert that lack of awareness, systemic factors, infrastructural limitations and the absence of clear and comprehensive policies continue to pose significant challenges in the adoption of the technology particularly in emerging economies.

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