Multimodal Large Language Models in Language Education: Personalization, Scale, and Future Potential
Xianping Wang, Hao Dong Qiu, Jiayue Shen, Weiru Chen, Anthony Choi, Wenbing Zhao · 2025
The advent of multimodal large language models (MLLMs) has transformed numerous fields, and language learning stands as one of the most promising domains for their application. This paper examines how MLLMs can design individualized learning plans and generate tailored content to enhance the four fundamental language skills—reading, writing, speaking, and listening—across common topics such as travel, business, culture, and daily life. By leveraging natural language processing, adaptive learning algorithms, and real-time feedback mechanisms, MLLMs offer an innovative, scalable, and accessible approach to language acquisition. This paper outlines the potential of MLLMs in creating dynamic curricula, discusses their strengths and limitations, and envisions their role in shaping the future of language education.