Research on Teaching Reform and Practice of Empowering Mechanical Drawing Course in Vocational Colleges with Generative Artificial Intelligence
Year: 2025
Type: article
Abstract: Against the backdrop of the "Made in China 2025" strategy, the Mechanical Drawing course in higher vocational education faces challenges where traditional teaching models are disconnected from the needs of industrial digitization. To address this, this study explores the deep integration of Generative Artificial Intelligence (Generative AI) into the Mechanical Drawing curriculum and proposes a new teaching model tailored for the modern era. The research first establishes the theoretical foundation by drawing upon Cognitive Load Theory and Constructivism Learning Theory, articulating Generative AI's role as a "Cognitive Scaffold." This function effectively reduces students' Extraneous Cognitive Load associated with tedious drawing tasks, allowing them to focus on Higher-Order Design Thinking. Based on this, we designed the "Human-Machine Collaboration and Virtual-Physical Integration" three-stage teaching model. This model reconstructs teaching objectives, shifting the focus from "mastering drawing skills" to "cultivating design thinking and human-machine collaboration capabilities." Implemented through three phases—basic guidance, AI-assisted innovation (concept generation and topology optimization), and deepening of engineering practice (standard correction and physical validation)—the model achieves a fundamental shift in teacher and student roles. The study utilizes an Action Research methodology, combined with quasi-experimental design and mixed methods, for practical validation. This research aims to provide a systematic theoretical framework, implementation pathway, and empirical reference for the intelligent transformation of engineering courses in vocational colleges.
DOI: 10.54097/x5ea4j44
Author: Yunfei Liao
Institution: Chongqing Industry Polytechnic University
Language: English
Subfield: Education
Field: Social Sciences
Domain: Social Sciences
SDG: Quality education
Open Access status: diamond
OpenAlex ID: W4416807368