To address the challenges of teacher marginalization and diminished instructional control arising from the integration of generative artificial intelligence (AI) into higher vocational classrooms, this study constructs a teacher-led “dual-track parallel” human-machine collaborative teaching model. This model delineates the teacher’s leadership authority across the four phases of “context-principle-intervention-reconstruction” while harnessing AI’s enabling capabilities characterized by “personalization” and “immediacy”. It pioneers a five-dimensional management mechanism encompassing “input-process-output-ethics-evaluation”, achieving a balance of unified pedagogical depth and scalable differentiated instruction. Statistical results demonstrate promising outcomes: interactions involving higher-order thinking accounted for 65% of student engagements; the experimental group exhibited an average 44% improvement in core skill mastery, significantly outperforming the control group; and over 90% of students reported enhanced learning directionality and autonomy. This model effectively enables teachers to concentrate on diagnostic assessment and the stimulation of higher-order cognition, while simultaneously significantly enhancing student classroom participation, autonomous learning capabilities, and problem-solving skills. It offers a replicable solution for vocational education classroom reform in the era of artificial intelligence.
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Share and Cite
Hua, B., Liu, L., Yang, X., Zhou, J., Tan, Y. (2025) Research on the Construction and Management Mechanism of a “Dual-track Parallel” Model for Human-machine Collaborative Teaching in Vocational Colleges. Global Education Bulletin, 2(6), 41-51. https://doi.org/10.71052/grb2025/IMPX2269