Employers increasingly expect graduates to utilize large language models (LLMs) in the workplace, yet the competencies needed for computing roles across Africa remain unclear given varying national contexts. This study examined how six LLMs, namely ChatGPT 4, DeepSeek, Gemini, Claude 3.5, Llama 3, and Mistral AI, describe entry-level computing career expectations across ten African countries. Using the Computing Curricula 2020 framework and drawing on Digital Colonialism Theory and Ubuntu Philosophy, we analyzed 60 LLM responses to standardized prompts. Technical skills such as cloud computing and programming appeared consistently, but notable differences emerged in how models addressed non-technical competencies, particularly ethics and responsible AI use. Models varied considerably in recognizing country-specific factors, including local technology ecosystems, language requirements, and national policies. Open-source models demonstrated stronger contextual awareness and a better balance between technical and professional skills, earning top scores in nine of ten countries. Still, all models struggled with cultural sensitivity and infrastructure considerations, averaging only 35.4% contextual awareness. This first broad comparison of LLM career guidance for African computing students uncovers entrenched infrastructure assumptions and Western-centric biases, creating gaps between technical recommendations and local needs. The strong performance of cost-effective open-source models (Llama: 4.47/5; DeepSeek: 4.25/5) compared to proprietary alternatives (ChatGPT 4: 3.90/5; Claude: 3.46/5) challenges assumptions about AI tool quality in resource-constrained settings. Our findings highlight how computing competency requirements vary widely across Africa and underscore the need for decolonial approaches to AI in education that emphasize contextual relevance

翻译：雇主日益期望毕业生能在职场中运用大语言模型（LLMs），但考虑到各国国情差异，非洲各地计算岗位所需的核心能力仍不明确。本研究考察了六种大语言模型——ChatGPT 4、DeepSeek、Gemini、Claude 3.5、Llama 3和Mistral AI——如何描述十个非洲国家的初级计算职业期望。基于《计算课程2020》框架，并借鉴数字殖民主义理论和乌班图哲学，我们分析了60条针对标准化提示的LLM回复。技术技能（如云计算和编程）在回复中普遍出现，但各模型在处理非技术能力（特别是伦理与负责任的人工智能使用）方面存在显著差异。模型在识别国家特定因素（包括本地技术生态系统、语言要求和国家政策）方面表现出较大差异。开源模型展现出更强的语境感知能力及技术与职业技能间更优的平衡，在十个国家中的九个获得最高评分。然而，所有模型在文化敏感性和基础设施考量方面均存在不足，语境感知平均得分仅为35.4%。这项针对非洲计算专业学生的首次大规模LLM职业指导比较研究，揭示了根深蒂固的基础设施假设和西方中心主义偏见，导致技术建议与本地需求之间存在脱节。相较于专有模型（ChatGPT 4：3.90/5；Claude：3.46/5），高性价比开源模型（Llama：4.47/5；DeepSeek：4.25/5）的优异表现，挑战了关于资源受限环境中AI工具质量的固有认知。我们的研究结果凸显了非洲各国计算能力要求的广泛差异性，并强调在教育领域需要采用去殖民化的人工智能方法，以强化语境相关性。