Evaluating the Accessibility of Digital Resources for Nǀuu Language Revitalisation: The Study of Internet and Social Media
The rapid growth of the internet and social media platforms has led to the acceptance of Artificial Intelligence (AI) and the Fourth Industrial Revolution (4IR) in exploring ICT-based solutions for revitalising indigenous languages, such as the Nǀuu language. This study sought to evaluate the accessibility of material on the internet and social media to revitalise the Nŀuu language. Linguists, members of the ≠Khomani San community, and scholars expressed enthusiasm to actively preserve the N|uu language. Using a qualitative methodology and a case study design, the study recruited sixteen participants with a National Qualification Framework (NQF) level 8 and higher in African Languages, Linguistics, and Language Practice. The results showed that social media and internet platforms do not readily provide the resources required to revitalise the Nǀuu language. While most participants could not find any N|uu language language learning resources on social media sites, those who could access preferred videos with pronunciation coaching. These results suggest that more language learning resources should be broadly available and visible across various social media platforms and the internet to ensure that people are exposed to the N|uu language and support their study of it. This study contributes to scholarship by emphasising accessibility problems for Nǀuu language materials online and arguing for more structured, community-driven digital tools to facilitate language revitalisation. Keywords: Nluu, ≠Khomani San, Language Revitalisation, Language Learning Material, Internet
Authors
- Walter Tshamano (ORCID: https://orcid.org/0000-0002-5739-7665)
- Lefty Mabela (ORCID: https://orcid.org/0000-0002-5461-5807)
- Simphiwe Mlaba (ORCID: https://orcid.org/0000-0001-7750-8484)
Institutions
- Tshwane University of Technology (ZA)
Publication Details
- Journal
- E-Journal of Humanities Arts and Social Sciences
- Published
- 2025-04-25
- DOI
- https://doi.org/10.38159/ehass.20256520
- Primary Topic
- Natural Language Processing Techniques
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Tshwane University of Technology