Educational Policies for Foreign Language Majors in the AI Era: A Comparative Study of Undergraduate Talent Training Programs at Four Chinese Universities

Against the backdrop of the deep integration of artificial intelligence into higher foreign language education, this study selects undergraduate talent training programs for foreign language majors from four Chinese universities as research samples: Beijing Foreign Studies University, Shanghai International Studies University, East China Normal University, and Shanghai Jiao Tong University. And it conducts a comparative analysis of their 2024-2025 talent training programs. The findings reveal significant disparities among the four universities in the number of AI-related courses offered and their credit proportions, giving rise to three distinct technology integration models, namely Embedded, Parallel, and Integrated, which correspond to three training paradigms: the feature-oriented paradigm, the education-integrated paradigm, and the interdisciplinary cross-integration paradigm. Meanwhile, common issues are observed across all four institutions, including insufficient depth of AI-integrated courses, a shortage of interdisciplinary teaching staff, and inadequate evaluation systems. Based on these findings, this paper proposes targeted recommendations to inform the digital transformation of foreign language programs in higher education.