With the advancement of the deep integration of artificial intelligence and education, the traditional ideological and political teaching of business English courses has significant shortcomings in terms of single context construction, lagging process feedback and lack of refinement in value guidance. To this end, this paper introduced artificial intelligence and multimodal interaction technology to construct a course ideological and political teaching model that integrates language intelligent analysis, virtual context simulation and process tracking feedback, so as to improve the embedding depth of value guidance and the adaptability of language training. The research focuses on the three functional modules of "virtual communication scene generation + intelligent semantic recognition + behavioral data analysis" to support teachers to dynamically adjust the ideological and political content embedding strategy in the teaching process and achieve detailed control of students' ideological and political cognition, language output and interactive behavior. The experimental results show that the average score of the experimental group students in the business English course ideological and political teaching mode enabled by artificial intelligence was 430 points in the pre-test, and the average score in the post-test rose to 474.6 points, an average increase of 44.6 points. In the traditional teaching mode, the control group had an average score of 428.6 in the pre-test and 439 in the post-test, with an average improvement of only 10.4 points. The post-test scores of the experimental group were significantly better than those of the control group, which verified the positive role of artificial intelligence technology in promoting students' language ability in the integration of ideological and political education (IPE) and English skills teaching.

Wei Guo. Application of Artificial Intelligence Empowering Ideological and Political Education in Business English Courses. International Journal of New Developments in Education (2025), Vol. 7, Issue 9: 62-69. https://doi.org/10.25236/IJNDE.2025.070909.

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