Investigating How Inclusive Intercultural Strategies Influence Immigrant Childrens Language Acquisition Efficiency

As the global immigrant population continues to grow, immigrant children face the challenges of language gap, acculturation, and communicative anxiety in the process of language acquisition. To cope with this problem, this study used a quasi-experimental design to assist the experimental group with a 12-week English language teaching intervention by introducing an AI multilingual story generation, semantic translation engine, and voice conversation simulation system. Data were collected using a mixed-methods approach with multi-layer validation. The results show that the experimental group significantly outperforms the control group in terms of language proficiency, cultural adaptation and classroom participation, and the feedback and behavioral data provided by the AI system provide critical support for teaching personalization, cultural empathy and output quality optimization. The study validates the potential of the AI-enabled inclusive education model in multilingual environments and provides empirical evidence for language teaching and education policy making.