Artificial intelligence and digital technologies are changing both the organization of work and the skills expected of university graduates. In China, these changes are increasingly discussed through the concept of New Quality Productive Forces (NQPF), which emphasizes innovation, digital transformation, and higher-quality development. For foreign language programs in applied universities, the challenge is therefore not simply to add AI tools to existing courses. It is to reconsider what students should be able to do with language, disciplinary knowledge, and digital technologies when they enter the workplace. Taking the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) as its regional context, this paper develops a conceptual model for foreign language talent cultivation based on Outcome-Based Education (OBE). The model gives particular attention to three connected issues: curriculum restructuring around workplace outcomes, the development of digital intelligence literacy through human-machine collaboration, and the use of data-informed teaching management to support continuous improvement. Rather than treating technology as a substitute for language learning, the paper argues that AI should be used where it can extend practice, feedback, analysis, and productivity, while human judgment remains central to cultural interpretation, critical evaluation, and professional communication. The proposed framework is intended as a practical reference for applied universities seeking closer alignment between foreign language education, regional industry needs, and the ongoing digital transformation of higher education.

© 2026, the Authors. Published by ISCCAC

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