Amid accelerating industrial transformation driven by artificial intelligence and digital manufacturing, vocational education faces an acute shortage of du-al-qualified teachers who can bridge academic instruction and enterprise prac-tice. This study investigates the construction and optimisation of faculty teams in Shandong’s vocational colleges within the paradigm of industry–education integration. Drawing upon Human Capital Theory, the TVET-oriented TPACK framework, and the AI-enabled professional learning model, a mixed-methods design combined questionnaire data (132 valid responses) with semi-structured interviews and a case study at Shandong X College. Findings reveal structural and competence imbalances: only 49.4 per cent of teachers hold dual qualifica-tions, fewer than 30 per cent engage in enterprise practice exceeding three months annually, and merely 22.7 per cent employ AI-assisted or digital-twin pedagogy. Three interlocking barriers were identified—rigid institutional mechanisms, resource scarcity, and cognitive constraints regarding digital and industrial teaching. To address these challenges, the study proposes a Tri-ple-Entity Collaboration and Dual-Drive framework linking full-time teachers, enterprise engineers, and part-time practitioners, supported by policy incen-tives and AI-based capability development. Implementation at Shandong X Col-lege increased dual-qualified faculty to 58.2 per cent, raised digital-teaching coverage to 65.3 per cent, and improved enterprise participation by 40 per cent. Theoretically, this research extends Human Capital Theory by integrating AI-mediated capacity growth and enriches the TVET-TPACK model through quantitative validation. Practically, it offers a replicable approach for aligning vocational faculty development with industrial upgrading in China’s digital economy.
Wu et al. (Sun,) studied this question.
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