Qiuchen Wu, Bin Bai
As artificial intelligence automates routine technical operations, the irreplaceable human contribution shifts toward technical application innovation-making useful improvements within existing technical frameworks through contextual judgment and adaptive problem-solving. China's vocational undergraduate education system, expanding from 15 pilot institutions in 2019 to 124 by June 2026, is uniquely positioned to cultivate this capability, yet how such innovation is activated through teacher-student interaction remains empirically unexamined. Grounded in symbolic interaction theory, this study employed an exploratory sequential mixed-methods design. A qualitative phase (n = 10) using theory-informed qualitative analysis produced a three-layer account of symbolic transmission, symbolic reception, and interactive mechanisms. A quantitative phase (N = 386) developed a 35-item scale and conducted an initial psychometric evaluation across six institutions. Structural equation modeling showed that interactive mechanisms had the strongest association with technical application innovation capability (β = 0.453), that symbolic reception showed stronger associations with the outcome than symbolic transmission in student self-reports, and that 38-44% of the association between symbolic rules and innovation passed through interactive mechanisms. These patterns are consistent with an innovation-as-emergence-from-interaction framework and provide a starting point for positioning vocational undergraduate education in the AI era.