| Explains the historical, theoretical, and technological development of machine translation. |
| Compares the fundamental principles of rule-based, example-based, statistical, and neural machine translation approaches and large language model-based translation systems. |
| Evaluates the outputs of different machine translation and AI systems in terms of accuracy, fluency, terminology, consistency, context, and cultural appropriateness. |
| Identifies error types in machine translation output and applies appropriate post-editing and quality control methods. |
| Explains and evaluates the integration of machine translation with translation memories, terminology databases, and computer-assisted translation tools. |
| Designs translation-oriented prompts for large language models and generative AI systems and guides and verifies outputs by providing contextual, terminological, and stylistic information. |
| Evaluates ethical and legal issues related to AI and machine translation, including confidentiality, data security, copyright, algorithmic bias, transparency, and professional responsibility. |
| Evaluates the changing role of the translator in AI-assisted translation environments and emerging workflows based on human–AI collaboration. |