DECODING THE SACRED: ARTIFICIAL INTELLIGENCE AND THE SYMBOLIC KNOWLEDGE STRUCTURES OF AFRICAN TRADITIONAL RELIGION
Lagos State University of Education Nigeria
Lagos State University of Education Nigeria
OSHAssociation UK, Superior University Pakistan https://orcid.org/0000-0001-6567-5963
Universitas Brawijaya
Beaconhouse Head Office Pakistan
DOI:
https://doi.org/10.56943/jssh.v4i4.870African Traditional Religion (ATR) is based on symbolic systems that reflect cosmology, ethics, spirituality and social order, which are represented through divination, rituals, proverbs, sacred objects as well as texts such as Yoruba Ifa verses. Scholars nowadays focus not only on their multivocal character but also on their epistemic and moral functions: symbols act as organs of knowledge no less than objects of cultural artefacts. AI tools, by symbolically representing that knowledge, through ontologies and explainable learning, provide means to decode, and maintain such sacred systems cultural sensitively. Indicatively, the article Preserving Indigenous Knowledge: Leveraging Digital Technology and Artificial Intelligence describes the way interactive platforms and AI-based applications preserve indigenous knowledge, without violating cultural guidelines and intellectual property rights. Using Classical Machine Learning and Deep Learning, Adinkra Symbol Recognition in Ghana was highly accurate in classifying Adinkra symbols. The results of the studies indicate that AI can improve preservation, access, and learning; however, it is unable to reproduce spiritual intention and group validation. Such ethical concerns as reductionism, digital colonialism, and cultural ownership are not new to the recent literature. This paper thus suggests an alternative approach in which AI would be used to supplement AIR custodianship to make sure that the knowledge is not stale, but dynamic, secure, and useful in the digital era.
Keywords: African Traditional Religion Artificial Intelligence Cultural Preservation Symbolic Knowledge
Abimbọla, ’Wande. (1997). Ifá : an exposition of Ifá literary corpus. Athelia Henrietta Press.
Abubakari, H. (2021). Ontology Annotation for Natural Language Development: A Yorùbá Noun Preliminary Model. Journal of West African Languages, 48(1). https://journalofwestafricanlanguages.org/downloads/download/140-volume-48-number-1/752-ontology-annotation-for-natural-language-development-a-yoru-ba-noun-preliminary-model
Adaba, K. A., & Boio, D. (2024). Across Africa, public trust in key institutions and leaders is weakening. https://www.afrobarometer.org/wp-content/uploads/2024/10/AD891-PAP20-Africans-trust-in-key-institutions-and-leaders-is-weakening-Afrobarometer-31oct24.pdf
Adekola, O. E. (2024). Linguistic Features and Symbolic Communication in Àgídìgbo Music of the Yorùbá of Southwestern Nigeria. Àgídìgbo: ABUAD Journal of the Humanities, 12(2), 351–367. https://doi.org/10.53982/agidigbo.2024.1202.26-j
Adeniyi, E. (2024). Towards the systematisation of African ways of knowing: neocolonial hegemony, theory development and cognitive imperialism in African studies. African Identities, 1–18. https://doi.org/10.1080/14725843.2024.2345325
Adeoye, C. L. (2005). Àṣà àti ìṣe Yorùbá. University Press PLC.
African Union. (2024). Continental Artificial Intelligence Strategy. African Union Commision.
Aina, A. A., & Táíwò, O. (2019). Yorùbá Noun Ontology from Functional Perspectives. Journal of The Linguistic Association of Nigeria, 22(1), 169–211. https://jolan.com.ng/index.php/home/article/view/221
Akbar, G. G., Kania, I., Uhmudin, A., Fadlurohman, M. I., & Nurliawati, N. (2025). STAKEHOLDER PERCEPTIONS OF AI USE IN EDUCATION. Journal of Social Sciences and Humanities, 67–86. https://doi.org/10.56943/jssh.v4i2.742
Akpobi, M. (2025). Yankari: A Monolingual Yoruba Dataset. https://doi.org/10.18653/v1/2025.africanlp-1.1
By, D. L., & Ros, D. P. (2024). THE CORRELATIONAL STUDY OF ARTIFICIAL INTELLIGENCE IN EDUCATION ON STUDENTS’ OUTCOMES. Journal of Social Sciences and Humanities, 1–21. https://doi.org/10.56943/jssh.v3i4.615
Chabata, L. (2024). Artificial intelligence and Afrocentric Biblical Hermeneutics crossroads in Zimbabwe (Col 2:8). HTS Teologiese Studies / Theological Studies, 80(1). https://doi.org/10.4102/hts.v80i1.10106
Creswell, J. W., & Clark, V. L. P. (2017). Designing and Conducting Mixed Methods Research. SAGE Publications Ltd.
Creswell, J. W., & Creswell, J. D. (2023). Research design: Qualitative, quantitative, and mixed methods approaches (Sixth). SAGE Publication, Inc.
Elawa, N. (2024). Indigeneity in African Religions: Oza Worldviews, Cosmologies and Religious Cultures by Afe Adogame (review). Nova Religio, 27(3), 122–124. https://doi.org/10.1353/nvr.2024.a919635
Elijah, I. S., & Taiye, D. M. (2025). THE SIGNIFICANCE OF THE DIGITALIZATION OF IFA IN THE COMMITTEE OF KNOWLEDGE. Cognizance Journal of Multidisciplinary Studies, 5(2), 208–216. https://doi.org/10.47760/cognizance.2025.v05i02.015
Hackett, C., Stonawski, M., Tong, Y., Kramer, S., Shi, A., & Fahmy, D. (2025). Religion in sub-Saharan Africa. Pew Research Center. https://www.pewresearch.org/religion/2025/06/09/religion-in-sub-saharan-africa/
He, Z., Su, J., Chen, L., Wang, T., & Lc, R. (2025). “I Recall the Past”: Exploring How People Collaborate with Generative AI to Create Cultural Heritage Narratives. Proceedings of the ACM on Human-Computer Interaction, 9(2), 1–30. https://doi.org/10.1145/3711006
Howard, B. (2020). Religion in Africa: Tolerance and trust in leaders are high, but many would allow regulation of religious speech. https://www.afrobarometer.org/wp-content/uploads/2022/02/ab_r7_dispatchno339_pap12_religion_in_africa.pdf
James, S. (2023). Art Language through Selected Signs and Symbols of the Yoruba People of Nigeria. European Journal of Philosophy, Culture and Religion, 7(1), 79–87. https://doi.org/10.47672/ejpcr.1572
Levin, I., & Minyar-Beloruchev, K. (2024). Reimagining Culture Through AI: The Cognification Phenomenon. International Journal for Digital Society, 15(1), 1934–1944. https://doi.org/10.20533/ijds.2040.2570.2024.0242
Lewis, J. E., Whaanga, H., & Yolgörmez, C. (2025). Abundant intelligences: placing AI within Indigenous knowledge frameworks. AI & SOCIETY, 40(4), 2141–2157. https://doi.org/10.1007/s00146-024-02099-4
Nasarian, E., Alizadehsani, R., Acharya, U. R., & Tsui, K.-L. (2024). Designing interpretable ML system to enhance trust in healthcare: A systematic review to proposed responsible clinician-AI-collaboration framework. Information Fusion, 108, 102412. https://doi.org/10.1016/j.inffus.2024.102412
Odey, E. A., Asuquo, O. O., Amokaha, G. S., Onah, G. A., & Obo, E. O. (2023). Symbol in African Religion and Philosophy: the Tiv Experience. Pharos Journal of Theology, 104(2). https://doi.org/10.46222/pharosjot.104.211
Ofosu-Asare, Y. (2025). Cognitive imperialism in artificial intelligence: counteracting bias with indigenous epistemologies. AI & SOCIETY, 40(4), 3045–3061. https://doi.org/10.1007/s00146-024-02065-0
Olagunju, A. S., James, A. A., Adeyefa, E. O., & Joseph, F. L. (2023). Algebraic characterization of Ifa main divination codes. Scientific African, 20, e01729. https://doi.org/10.1016/j.sciaf.2023.e01729
Pozzi, G. (2023). Automated opioid risk scores: a case for machine learning-induced epistemic injustice in healthcare. Ethics and Information Technology, 25(1), 3. https://doi.org/10.1007/s10676-023-09676-z
Qiang, Z. (2023). Ontology-Compliant Knowledge Graphs (pp. 298–309). https://doi.org/10.1007/978-3-031-43458-7_48
Turner, V. W. (1973). Symbols in African Ritual. Science, 179(4078), 1100–1105. https://doi.org/10.1126/science.179.4078.1100
United Nations. (2025, August 7). Ensuring Indigenous Peoples’ rights in the age of AI. United Nations; United Nations. https://www.un.org/en/desa/ensuring-indigenous-peoples’-rights-age-ai
UNSECO. (2024, September 26). Recommendation on the Ethics of Artificial Intelligence. UNESCO. https://www.unesco.org/en/articles/recommendation-ethics-artificial-intelligence
van Beek, W. E. A. (2020). African Indigenous Religions. In Oxford Research Encyclopedia of African History. Oxford University Press. https://doi.org/10.1093/acrefore/9780190277734.013.661
World Bank Group. (2024). Digital Transformation Drives Development in Africa . World Bank Group. https://www.worldbank.org/en/results/2024/01/18/digital-transformation-drives-development-in-afe-afw-africa
Zhang, J., Chen, B., Zhang, L., Ke, X., & Ding, H. (2021). Neural, symbolic and neural-symbolic reasoning on knowledge graphs. AI Open, 2, 14–35. https://doi.org/10.1016/j.aiopen.2021.03.001
