Construction of Teaching Resource Optimization Model from the Perspective of Human-Machine Collaboration

This study aims at the current problems such as the disconnection and insufficient adaptability between digital teaching resources and the needs of teachers and students, and constructs a teaching resource optimization model from the perspective of human-machine collaboration, in order to improve resource allocation, enhance the accuracy of resources and personalized teaching. By analyzing the demands of teachers and students, a resource optimization model centered on the trinity collaboration of "teacher - GenAI - student" is proposed. Teaching resources are scientifically divided, and a four-step dynamic process of "data collection - demand transformation - resource generation - application iteration" is designed. Taking the "Obstacle Avoidance Design of Intelligent Logistics Robots" project of the Python course in higher vocational education as a case for practical verification, the results show that students' mastery rate of sensor principles, code debugging efficiency, and ability of cross-scenario knowledge transfer have all improved. This model has achieved the transformation of teaching resources from supply-driven to demand-led by precisely matching the needs of teachers and students and dynamically optimizing the supply of resources, providing theoretical support and practical paths for the construction of the human-machine collaborative education ecosystem.