Construction and Application of a "Teacher-Machine-Student" Interactive Symbiotic Teaching Model in Vocational Education
Year: 2026
Type: article
Study design: Clinical Trial
Abstract: Vocational education is under pressure from two directions at once: industrial upgrading keeps raising the bar for skilled talent, while the conventional classroom, built around one teacher and a fixed textbook, struggles to give each student timely guidance. Drawing on symbiosis theory, constructivist learning theory and recent work on human-AI collaboration, this paper constructs a "Teacher-Machine-Student" (TMS) interactive symbiotic teaching model. In the model the teacher acts as instructional designer and value guide, the artificial intelligence agent works as adaptive tutor, analyst and resource generator, and the student remains the constructor of knowledge; the three parties exchange tasks, data and feedback across pre-class, in-class and post-class stages, forming a closed loop of co-construction, co-adaptation and co-evolution. A 16-week quasi-experiment was carried out in the course "Industrial Robot Programming and Operation" with two intact classes (experimental group n=48, control group n=46). Results show that the experimental group outperformed the control group on the post-test (82.5 vs 74.2 points, t=6.09, p<0.001, Cohen's d=1.26), obtained 1.5 to 1.8 times the score gains of the control group on every competency dimensions, and maintained a steadily rising trajectory of human-machine interaction. Interaction frequency, feedback adoption and learning engagement correlated moderately with achievement (r=0.49-0.64), and 85.4% of the experimental students reported being satisfied or strongly satisfied with the model. The findings suggest that a structured symbiosis among teacher, machine and student can improve both the efficiency and the quality of vocational classroom teaching, and the paper closes with boundary conditions and governance suggestions for wider application.
Author: <p>Kai Wei, Wenjuan Guo</p>
Language: English
Subfield: Education
Field: Social Sciences
Domain: Social Sciences
SDG: Quality education
Open Access status: diamond
OpenAlex ID: W7212004586