Construction of an Intelligent Assistant System for Intercultural Communication Among International Students Based on AI

With the deepening of globalization, intercultural communication ability has become an important skill for international students. However, cultural differences, language barriers, and communication habits have brought many challenges regarding cross-cultural communication. To meet these challenges, this study designs and implements an intelligent assistant system to help international students better adapt to foreign cultures and improve their intercultural communication ability by providing real-time language translation, cultural interpretation, and communication suggestions. The system adopts micro-service architecture to ensure loose coupling and scalability among functional modules. Functional modules include language translation, cultural interpretation, communication suggestions, and user support modules. The language translation module uses the deep learning neural machine translation (NMT) model to realize multi-language real-time translation and supports text and voice input. The cultural interpretation module provides cultural common-sense query, real-time dialogue content analysis, and a case base for users to learn and reference. The communication suggestion module provides personalized communication strategies and suggestions according to the dialogue content and helps users improve their communication skills through simulated dialogue exercises. The user support module provides online help documents, user guides, and feedback collection systems to enhance the user experience. The key technologies of the system include multilingual translation technology, speech recognition and synthesis technology, intelligent recommendation algorithm, and natural language processing (NLP) technology. The system can efficiently recognize and synthesize speech through the deep learning model and provide accurate cultural interpretation and communication suggestions based on users' historical behavior and contextual information. In terms of implementation, Python and Django framework are used in the backend, react framework is used in the frontend, PostgreSQL is used as the database, and it is deployed on an AWS cloud platform to ensure the stability and scalability of the service. After comprehensive functional and performance tests, the results show that the system can accurately perform language translation, efficiently recognize pronunciation, intelligently recommend cultural knowledge and communication suggestions, and maintain stable operation under high concurrency. This study not only provides a convenient and efficient cross-cultural communication tool for international students, but also provides new ideas and methods for applying AI in education, which has important practical significance and research value.