African Air and Space Law (Law/Engineering crossover)

Advancing Scholarship Across the Continent

Vol. 2007 No. 1 (2007)

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AI-Aided Language Translation Apps for Education Equity in South Africa: A Methodological Exploration

Tshepo Selemani, University of KwaZulu-Natal Nolwandle Qobozi, Department of Cybersecurity, Council for Geoscience Zola Khumalo, Department of Data Science, University of KwaZulu-Natal Seth Mabudani, University of KwaZulu-Natal
DOI: 10.5281/zenodo.18859803
Published: June 27, 2007

Abstract

Language barriers in South Africa present a significant educational challenge, particularly for non-English speaking students who require translation apps to access educational materials and resources effectively. The methodology involves a systematic review of current language translation technologies, an assessment of user needs through surveys, and the design and testing of prototype apps. Statistical analysis will be employed to evaluate app performance based on predefined metrics. A preliminary study revealed that over 70% of respondents preferred apps with higher accuracy rates in translating between Zulu and English compared to existing options. The development of AI-assisted language translation apps for education in South Africa represents a novel approach towards enhancing educational equity by reducing language barriers. Further research should focus on refining app functionality, addressing user privacy concerns, and evaluating long-term impact on student engagement and learning outcomes. AI translation, education access, language barriers, South Africa, educational technology Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.

How to Cite

Tshepo Selemani, Nolwandle Qobozi, Zola Khumalo, Seth Mabudani (2007). AI-Aided Language Translation Apps for Education Equity in South Africa: A Methodological Exploration. African Air and Space Law (Law/Engineering crossover), Vol. 2007 No. 1 (2007). https://doi.org/10.5281/zenodo.18859803

Keywords

GeographicSub-SaharanNatural Language ProcessingCorpus LinguisticsMachine LearningEthnographyQuantitative Analysis

References