为翻译与专业沟通领域设计AI技术课程，提升从业者算法素养。
A technical curriculum on language-oriented artificial intelligence in translation and specialised communication
- 聚焦向量嵌入、神经网络、分词与Transformer四大核心
- 课程帮助学习者建立计算思维与算法意识
- 适合翻译专业师生及AI应用从业者参考
本文提出一门面向语言与翻译（L&T）行业的语言导向人工智能（AI）技术课程。课程旨在通过易懂的方式，让翻译与专业传播领域的利益相关者掌握现代语言导向AI的理论与技术基础。核心内容包括：1）向量嵌入，2）神经网络技术基础，3）分词，4）Transformer神经网络。课程目标是培养学习者的计算思维、算法意识与算法自主性，从而增强其在AI驱动工作环境中的数字韧性。该教学方案已在科隆应用技术大学翻译与多语种传播研究所的AI专题硕士课程中进行教学验证。结果表明课程具有良好的教学适切性，但参与者反馈需配合更高阶的教学支持（如讲师引导）以实现最佳学习效果。
原文摘要 · Abstract (English)
This paper presents a technical curriculum on language-oriented artificial intelligence (AI) in the language and translation (L&T) industry. The curriculum aims to foster domain-specific technical AI literacy among stakeholders in the fields of translation and specialised communication by exposing them to the conceptual and technical/algorithmic foundations of modern language-oriented AI in an accessible way. The core curriculum focuses on 1) vector embeddings, 2) the technical foundations of neural networks, 3) tokenization and 4) transformer neural networks. It is intended to help users develop computational thinking as well as algorithmic awareness and algorithmic agency, ultimately contributing to their digital resilience in AI-driven work environments. The didactic suitability of the curriculum was tested in an AI-focused MA course at the Institute of Translation and Multilingual Communication at TH Koeln. Results suggest the didactic effectiveness of the curriculum, but participant feedback indicates that it should be embedded into higher-level didactic scaffolding - e.g., in the form of lecturer support - in order to enable optimal learning conditions.
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