Research on the Integration Path and Practice of AI Intelligent Technology and English Distance Education

Abstract

Abstract This paper introduces AI intelligence technology in English distance education and establishes a learning analytics model that includes information collection and acquisition, data storage, cleaning, integration, analysis, visualization, and action. Each component corresponds to a series of specific technologies and processes, and generates information such as learner portrait modeling profiling learning behaviors, using data mining to provide personalized learning solutions for learning. The assessment system is constructed from three perspectives: contextual, cognitive, and technological, which ensures accurate teaching for the personalized needs of English learners. It has been verified that the contextual interaction effect on students’ performance accounts for 78% of the total sample in the English distance education system after applying AI intelligent technology. Students in the experimental class showed positive interest in the distance education system 47.44±7.15, which was significantly higher than that of the control class 29.65±8.21. This integration model provides a new development direction for English distance education, and also provides strong support for personalized teaching and continuous optimization of the educational process.