评估LLMs用于职业指导：在十国非洲国家中比较计算能力推荐的比较分析
Evaluating LLMs for Career Guidance: Comparative Analysis of Computing Competency Recommendations Across Ten African Countries
AI总结：
本研究比较了六个LLM在十国非洲国家中对计算职业能力的推荐，揭示了LLM在上下文意识和伦理考量上的差异，强调了去殖民化方法在AI教育中的重要性。
摘要：
雇主越来越多地期望毕业生在职场中利用大型语言模型（LLMs），但鉴于各国的差异性背景，非洲计算角色所需的能力仍然不明确。本研究考察了六个LLMs，即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。使用《计算课程2020》框架，并借鉴数字殖民主义理论和Ubuntu哲学，对60个LLM对标准化提示的响应进行内容分析，发现一致覆盖技术能力，如云计算和编程，但在非技术能力方面存在显著差异，特别是伦理和负责任的AI使用。模型在识别国家特定因素方面差异很大，包括本地技术生态系统、语言要求和国家政策，平均仅35.4%的上下文意识。开源模型表现出更强的上下文意识，并在技术与专业技能之间取得了更好的平衡，Llama（4.47/5）和DeepSeek（4.25/5）在性能上优于专有替代品ChatGPT-4（3.90/5）和Claude（3.46/5）。然而，Mistral的糟糕上下文表现（0.00/4）尽管是开源的，表明开发哲学本身并不保证上下文响应能力。本研究首次全面比较了LLM对非洲计算学生的职业指导，揭示了根深蒂固的基础设施假设和西方中心偏见，这些偏见导致技术建议与本地现实之间存在差距。研究结果挑战了对资源有限环境中的AI工具质量的假设，并强调了在教育中采用去殖民化方法的必要性，强调上下文相关性和混合的人工智能指导模型。
英文摘要：
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.
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