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, content analysis of 60 LLM responses to standardized prompts reveals consistent coverage of technical competencies such as cloud computing and programming, but notable differences in non-technical competencies, particularly ethics and responsible AI use. Models vary considerably in recognizing country-specific factors, including local technology ecosystems, language requirements, and national policies averaging only 35.4% contextual awareness overall. Open-source models demonstrated stronger contextual awareness and better balance between technical and professional skills, with Llama (4.47/5) and DeepSeek (4.25/5) outperforming proprietary alternatives ChatGPT-4 (3.90/5) and Claude (3.46/5). However, Mistral's poor contextual performance (0.00/4) despite being open-source indicates that development philosophy alone does not guarantee contextual responsiveness. This first comprehensive comparison of LLM career guidance for African computing students uncovers entrenched infrastructure assumptions and Western-centric biases that create gaps between technical recommendations and local realities. The findings challenge assumptions about AI tool quality in resource-constrained settings and underscore the need for decolonial approaches to AI in education, emphasizing contextual relevance and hybrid human-AI guidance models.

翻译：雇主日益期望毕业生能在职场中运用大型语言模型（LLMs），但考虑到各国国情差异，非洲各地计算岗位所需的能力要求仍不明确。本研究基于《计算课程2020》框架，借鉴数字殖民主义理论和乌班图哲学，对六种LLMs（ChatGPT 4、DeepSeek、Gemini、Claude 3.5、Llama 3和Mistral AI）在十个非洲国家初级计算职业期望描述进行内容分析。通过对60条标准化提示的LLM响应分析发现：在云计算、编程等技术能力方面呈现一致覆盖，但在非技术能力（特别是伦理与负责任的人工智能应用）方面存在显著差异。各模型对国别因素的识别能力差异显著——包括本地技术生态、语言要求及国家政策等维度，整体语境感知度平均仅为35.4%。开源模型展现出更强的语境感知能力及技术技能与职业素养的更好平衡，其中Llama（4.47/5）和DeepSeek（4.25/5）表现优于专有模型ChatGPT-4（3.90/5）和Claude（3.46/5）。然而同为开源模型的Mistral在语境理解方面表现欠佳（0.00/4），表明开发理念本身并不能保证语境适应性。这项针对非洲计算专业学生的首次LLM职业指导系统比较研究，揭示了根深蒂固的基础设施假设和西方中心主义偏见，导致技术建议与本土现实之间存在断层。研究结果挑战了关于资源受限环境中AI工具质量的固有认知，强调教育领域需要采用去殖民化的人工智能发展路径，重点关注语境相关性及人机协同的混合指导模式。