Research on the Concrete Teaching of Abstract Theories in the “Fundamentals of Mechanical Engineering Control” Course Empowered by Generative Artificial Intelligence

Abstract

ABSTRACT The modules on “System Stability Criteria” and “Frequency Domain Characteristics” within the “Fundamentals of Mechanical Engineering Control” course constitute essential theoretical components. However, their highly abstract mathematical nature and disconnect from physical intuition present considerable teaching challenges. This study examines the potential of Generative Artificial Intelligence (GAI) to facilitate concrete representation of abstract theories in engineering education. Grounded in cognitive load theory and the SAMR model, this research systematically analyzes key pedagogical obstacles—including the separation between mathematical derivations and physical significance, the abstraction of graphical logic, and insufficient engineering case studies—and harnesses GAI's capabilities in text analysis, dynamic visualization, and case generation to develop an innovative pedagogical framework. Using “System Stability Criteria” and “Frequency Domain Characteristics” as primary examples, this paper demonstrates the integration of text generation models (such as ChatGPT), image and dynamic visualization tools (including Midjourney and Stable Diffusion), and code generation models (like GitHub Copilot) to transform abstract theories into intuitive, interactive learning experiences. Through the design and classroom implementation of this GAI‐enhanced pedagogical framework, its feasibility and perceived utility are evaluated. Qualitative feedback from students and instructor observations indicate that the framework aids in reducing cognitive barriers, strengthening connections between theoretical concepts and engineering applications, and fostering more engaging learning experiences. This study offers a proof‐of‐concept, theoretical insights and practical guidance for reforming the teaching of “Fundamentals of Mechanical Engineering Control” while contributing novel perspectives on GAI's role in the digital transformation of engineering education.

Ähnliche Arbeiten