Teacher's Perspective on Generative Artificial Intelligence-Driven Innovation of Vocational Undergraduate Teaching Models in Road and Bridge Engineering—An Empirical Study Based on Structural Equation Modeling
Bibliographic record
Abstract
This study examines the role mechanism of generative artificial intelligence (GAI) in empowering the innovation of vocational undergraduate teaching modes in road and bridge engineering from the teachers' perspective. Integrating the Technology Acceptance Model (TAM), Constructivist Learning Theory (CLT), and Engineering Education Theory (EET), a multidimensional Structural Equation Model (SEM) is constructed, covering Teaching AI Literacy (TAL), Generative AI Use Behavior (GAU), Teaching Perception of Adaptation (TPA), Learning Engagement (LE), Learning Outcomes (LO), Engineering Problem Solving Skills (EPS), Teaching Satisfaction (TS), Ethical Risk Perception (ERP), and College-Company Collaboration Intensity (CCI) as a contextual moderating variable. Taking all 54 teachers of the road and bridge engineering program at Qinghai Vocational and Technical University as the empirical sample, the study showed that teachers' AI literacy significantly contributed to AI usage behavior and students' learning engagement; AI usage behavior further positively affected learning engagement by enhancing teachers' teaching perception of adaptation; and learning engagement significantly and positively impacted the students' learning outcomes, which in turn improved their engineering problem-solving ability and teaching satisfaction. The increase in learning engagement also considerably enhanced students' ability to perceive the ethical risks of AI. The intensity of school-enterprise collaboration (CCI) as a contextual variable was significantly and positively correlated with both teacher AI literacy TAL and generative AI usage behavior (GAU). This study has significant practical implications for the generative AI-driven reform of vocational undergraduate teaching modes and school-enterprise synergy in road and bridge engineering.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".