Introduction: In China, demand is growing for professionals who combine linguistic expertise with computational skills. Yet traditional language programs at science and technology universities often lack such integration. This study explores the feasibility of establishing a Language Intelligence major with a structured curriculum integrating linguistics, AI engineering, and domain applications.

Methods: This empirical study is based on first-hand analysis of a program offered by the authors’ own department. Three methods were applied. 1. Directed content analysis of six Chinese national policy documents (2018-2025) using pre-defined themes: AI-education integration and interdisciplinary talent development. 2. To assess labor market demand, we reviewed 2024 industry reports, which show NLP positions ranking first in AI role growth, with severe shortages of hybrid-skill candidates. 3. A single case study of a real-world program: the English AI Pilot Program at Harbin Engineering University. Data sources include program curriculum documents, course lists, expert evaluations, and student feedback collected directly from the program.

Results: Three findings emerged. First, policy supports “AI+X” education, but traditional language curricula lack mathematics, programming, computational linguistics, and corpus training-specific gaps in technical terminology processing and multilingual annotation. Second, market data confirm rapid NLP growth and a hybrid talent shortage. Third, the case program demonstrates feasibility: it compresses traditional language courses, adds math/programming, and offers specialized courses. Experts call it “precisely positioned at the interdisciplinary frontier”; students report that data mining and language intelligence training build core competencies.

Conclusions: A Language Intelligence major offers a promising response to labor market needs. The model—a tripartite curriculum (linguistic theory, AI engineering, domain knowledge)—provides a preliminary reference for other universities facing similar humanities–technology disconnects. However, the program has operated for only one year, and findings are exploratory. Longitudinal research is needed to validate long-term effectiveness.