In teaching design, these intelligent agents leverage technologies such as natural language processing (NLP), retrieval-augmented generation (RAG), dynamic knowledge graphs, and sentiment analysis to support case generation and precise teaching scenario design. This significantly improves the efficiency and effectiveness of translation education. The agents can assess translation quality dynamically and provide personalized feedback, helping students master specialized terminology, understanding cultural metaphors, and enhancing cross-cultural communication skills. In technical communication, these agents combine multimodal generation technologies (e.g., 3D visualization, voice-based retrieval, and sentiment analysis) to create dynamic, scenario-specific technical manuals, making knowledge dissemination more precise, engaging, and efficient.
The presentation highlights real-world applications, showcasing how digital human intelligent agents leverage scenario recognition to simulate real usage environments, enable voice-triggered dynamic knowledge retrieval, and generate comprehensive solutions. It also explores the future of intelligent technical manuals through multiple intelligent agents collaboration and information fusion. Looking ahead, through multimodal integration and intelligent collaboration, digital human intelligent agents are expected to drive a fully integrated smart ecosystem in technical translation and communication, offering forward-thinking guidance for industry development.
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