Artificial Intelligence and Tshivenḓa Proverbs:
Exploring technological approaches to documentation, interpretation, and language teaching

Avhatakali Jonathan Makhado ORCID iD 

Issue: Vol. 7 No. 7 2026 Article 6 pp. 775 – 785
DOI : https://doi.org/10.38159/jelt.2026776 Published online 21st September 2026.

© 2026 The Author(s). This is an open access article under the CCBY license (http://creativecommons.org/licenses/by/4.0/).

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Tshivenḓa proverbs are a central element of Venḓa oral tradition, encapsulating moral values, social norms, and indigenous knowledge. Traditionally transmitted orally, these proverbs face challenges in accessibility, continuity, and intergenerational transmission, particularly among younger speakers and Tshivenḓa-speaking diaspora communities. Recent developments in Artificial Intelligence (AI) present new opportunities to digitally document, interpret, and teach Tshivenḓa proverbs, thereby bridging oral traditions and contemporary educational contexts. This study explores the potential of AI to support the preservation, semantic interpretation, and pedagogical use of Tshivenḓa proverbs, while also examining associated cultural and ethical concerns. Based on digital humanities, computational linguistics, and Indigenous Knowledge Systems (IKS), the study adopted a qualitative exploratory research design. Data were generated through the development of a pilot AI prototype for analysing selected Tshivenḓa proverb corpora, semi-structured interviews with language educators, and participant feedback on AI-generated interpretations. The findings indicate that AI can improve semantic parsing, contextual explanation, and interactive learning, making the Tshivenḓa proverbs more accessible and engaging for contemporary learners. However, concerns emerged about the loss of performative and situational meaning, the risk of cultural misinterpretation, and ethical issues related to the ownership and representation of indigenous knowledge. The study concludes that AI can meaningfully support the preservation and teaching of Tshivenḓa proverbs if guided by culturally grounded, linguistically sensitive, and ethically responsible design principles. The study contributes to scholarship by expanding interdisciplinary knowledge on the intersection of artificial intelligence, indigenous languages, and oral literature.

Keywords: Tshivenḓa Proverbs, Artificial Intelligence, Indigenous Knowledge Systems, Language Preservation, Digital Humanities.

Ajuzieogu, Uchechukwu. “Multimodal Generative AI for African Language Preservation: A Framework for Language Documentation and Revitalization,” 2022. https://doi.org/10.13140/RG.2.2.23144.99842.

Anik, Mahfuz Ahmed, Abdur Rahman, Azmine Toushik Wasi, and Md Manjurul Ahsan. “Preserving Cultural Identity with Context-Aware Translation Through Multi-Agent AI Systems.” In Proceedings of the 1st Workshop on Language Models for Underserved Communities (LM4UC 2025), 51–60. Stroudsburg, PA, USA: Association for Computational Linguistics, 2025. https://doi.org/10.18653/v1/2025.lm4uc-1.7.

Batibo, Herman. Language Decline and Death in Africa: Causes, Consequences, and Challenges. Vol. 132. Multilingual Matters, 2005.

Battiste, Marie. “Indigenous Knowledge and the Politics of Education.” Canadian Scholars’ Press, 2002.

Bird, Steven. “Must NLP Be Extractive?” In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 14915–29, 2024.

Crystal, David. “Language Death.” Cambridge, 2000.

Deegan, M., and W. McCarty. Collaborative Research in the Digital Humanities. Ashgate, 2012.

Finnegan, Ruth. Oral Literature in Africa. Vol. 1. Open Book Publishers, 2012.

Gasparotto, Melissa. “Fostering Linguistic Justice in Digital Spaces: Using Indigenous Methods in the Study of NLP.” Emerging Media 3, no. 1 (2025): 13–22.

Joshi, Bishnu Maya, Shambhu Prasad Khatiwada, and Rajendra Kumar Pokhrel. “Influence of Socioeconomic Factors on Access to Digital Resources for Education.” Rupantaran: A Multidisciplinary Journal 8, no. 01 (2024): 17–33.

Koc, Vincent. “Generative AI and Large Language Models in Language Preservation: Opportunities and Challenges.” ArXiv Preprint ArXiv:2501.11496, 2025.

Mathivha, M. E. R. Zwivhuya Zwa Vhavenda. M. E. R. Mathivha Publishers, 2001.

Mieder, Wolfgang. Proverbs: A Handbook. Vol. 488. Greenwood Press, Westport, CT, 2004.

Pinhanez, Claudio, Paulo Cavalin, Luciana Storto, Thomas Finbow, Alexander Cobbinah, Julio Nogima, Marisa Vasconcelos, Pedro Domingues, Priscila de Souza Mizukami, and Nicole Grell. “Harnessing the Power of Artificial Intelligence to Vitalize Endangered Indigenous Languages: Technologies and Experiences.” ArXiv Preprint ArXiv:2407.12620, 2024.

Pradhan, Uma, and Joyeeta Dey. “Language, Artificial Education, and Future-Making in Indigenous Language Education.” Learning, Media and Technology 50, no. 2 (2025): 235–48.

Risam, Roopika. New Digital Worlds: Postcolonial Digital Humanities in Theory, Praxis, and Pedagogy. Northwestern University Press, 2018.

Senekal, Burgert, and Susan Brokensha. “The Potential of Artificial Intelligence (AI) for Decolonising Education in South Africa through the Development of Indigenous Languages.” South African Journal of African Languages 43, no. 3 (2023): 208–15.

Smith, Linda Tuhiwai. Decolonizing Methodologies: Research and Indigenous Peoples. Zed Books Limited, University of Otago Press, 1999.

———. “Decolonizing Methodologies: Research and Indigenous Peoples,” 2021.

Terras, M. “Digital Humanities and the Computational Turn.” In Collaborative Research in the Digital Humanities, edited by M. Deegan and W. McCarty, 1–18. Ashgate, 2011.

Yankah, Kwesi. Speaking for the Chief: Okyeame and the Politics of Akan Royal Oratory. Indiana University Press, 1995.

Zerkowski, Luis Vitor, and Nina S. T. Hirata. “Uncovering Latent Connections in Indigenous Heritage: Semantic Pipelines for Cultural Preservation in Brazil.” ArXiv Preprint ArXiv:2508.10911, 2025.

Avhatakali Jonathan Makhado is a lecturer within the Department of African Languages, under the Faculty of Humanities, Social Sciences, and Education at the University of Venda. He holds a Doctor of Philosophy Degree in Tshivenda. His main interests in the language are found in Phonetics, Phonology, Syntax, Semantics and Traditional Literature. He teaches Tshivenda in both undergraduate and postgraduate programs. He is the author of several articles and has presented at some conferences. Furthermore, his academic experience includes supervising Master’s and Doctoral (PhD) students.

Makhado, Avhatakali Jonathan. “Artificial Intelligence and Tshivenḓa Proverbs: Exploring technological approaches to documentation, interpretation, and language teaching.” Journal of Education and Learning Technology 7, no.7 (2026): 775 – 785. https://doi.org/10.38159/jelt.2026776

© 2026 The Author(s). Published and Maintained by Noyam Journals. This is an open access article under the CCBY license (http://creativecommons.org/licenses/by/4.0/).

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