Abstract

Testing-related courses are core foundational components in modern engineering education, playing a critical role in developing students' practical and analytical skills. In current learning practice, however, these courses commonly present several major challenges. These include highly abstract and difficult theoretical content, limited laboratory conditions for hands-on practice, and insufficient pedagogical support for personalized learning pathways. With the rapid development and integration of artificial intelligence (AI) technologies, its core strengths—namely intelligence, personalization, and operational efficiency—have opened up unprecedented new possibilities for transforming how these complex engineering courses are taught and learned. In response to the persistent problems inherent in traditional learning models, this paper discusses the feasibility of applying advanced AI methodologies to the learning of testing-related courses. Furthermore, it provides a comprehensive and systematic analysis of the key points at which AI tools and traditional curricula can be deeply integrated. By examining intelligent tutoring systems, automated assessment frameworks, and virtual laboratory simulations, this research highlights how AI can bridge the gap between theoretical knowledge and practical application. Ultimately, the study aims to offer practical, actionable guidance for engineering students and educators seeking to use AI to support and enhance course learning, thereby fostering a more adaptive, engaging, and effective educational environment.

Keywords: 

artificial intelligence, testing technology, engineering education, parameter measurement, intelligent testing

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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

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