2023). There are various language tools that educators at the UJ can be familiar with to ensure there is no language barrier. As an example, the InkubaLM natural language processing model is being designed to underpin and enhance low-resourced African languages such as Swahili, Yoruba, isiZulu, and isiXhosa with close to 364 million speakers (Mzekandaba, 2024). According to Mzekandaba (2024), Lelapa AI and InkubaLM (Dung Beetle Language Model) are “robust, compact models created to serve African communities without requiring extensive resources” (2024, p.2). It consists of two datasets: Inkuba-Mono and InkubaInstruct (Mzekandaba, 2024). As such Inkuba-Mono “is a monolingual dataset collected from open-source repositories in five African languages, along with English and French data, to pre-train InkubaLM models” (Mzekandaba, 2024, p. 2). They are focused on machine translation, sentiment analysis, speech tagging, and question-answering. For each endeavour, they covered five African languages: Hausa, Swahili, isiZulu, Yoruba, and isiXhosa (Mzekandaba, 2024). While this solution might still be in progress, it is pertinent to recognise its significance in personalising learning in Africa and, significantly, the UJ, as some of its educators are already doing, to include itself in such initiatives and make them mandatory. In my teaching, I have found it crucial to incorporate and educate students about AI language models. However, it is important to highlight the limitations of these tools, particularly in their representation of African languages. Despite these shortcomings, AI language models are invaluable for helping students understand course material in their native languages. I strongly believe this approach has and will continue to enhance learning, cognitive abilities, and self-esteem in academic spaces. 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