Undergraduate technical education is expected to develop skilled professionals, yet the guidance through which students learn technical standards remains underexamined. This interview-based qualitative case study examines how six teachers and six high-achieving students in one Chinese undergraduate technical institution describe and interpret technical mentorship. Symbolic interactionism informed the analysis, but Mead's "I" and "me" were used only as sensitizing concepts, not as labels for teachers and students. Semi-structured interviews were analyzed through constructivist coding procedures with inductive, abductive, and theory-informed steps. The study reports patterns in participants' narratives; it does not claim to document interaction sequences. Teachers described making written standards concrete, adjusting guidance to learners' difficulties, and using practice to test understanding. Students described interpreting abstract requirements, assessing what counted as acceptable work, planning their development, and revising their understanding when practice did not match expectations. Three interpretive processes recurred in the accounts: constructing a personal understanding, negotiating standards with autonomy, and connecting external requirements to personal goals. These processes were not uniform. Anxiety about misunderstanding, a preference for direct instruction, and mismatches between classroom preparation and equipment use complicated the more orderly accounts. The analysis offers a context-specific interpretation of how technical mentorship was narrated in one institution and identifies theoretical propositions for future research. It does not establish causal mechanisms, long-term career effects, or transferability to other settings.

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.