大语言模型赋能智慧交通数学建模课程教学改革实践

Exploration and Practice of Teaching Reform on Mathematical Modeling Course for Intelligent Transportation Major Empowered by Large Language Models

摘要: 本研究旨在破解智慧交通专业数学建模课程案例固化、理论与实践脱节等核心教学痛点，响应交通强国建设战略与高等教育数字化转型要求，探索大语言模型赋能的工科课程教学改革路径，培养兼具专业建模能力与AI应用素养的智慧交通复合型人才。研究构建课程体系重构、教学模式创新、作业体系优化三位一体教学改革框架，以内蒙古农业大学2024级交通工程专业64名本科生为实践对象，采用李克特五级量表与结构方程模型开展系统实证检验。结果表明，课程学生总体满意度达98.44%，各核心评价维度好评率均超93%，测评量表信效度优良，改革显著提升了学生的交通建模核心能力与AI应用综合素养。本研究可为智慧交通专业数字化教学改革提供可落地的实践参考，为新工科复合型工程人才培养提供科学实证依据。

Abstract: This research aims to address the core teaching problems such as the rigidity of case studies and the disconnection between theory and practice in the professional mathematics modeling course of intelligent transportation, in response to the requirements of the transportation-strong country construction strategy and the digital transformation of higher education. It explores the teaching reform path of engineering courses empowered by large language models, and aims to cultivate intelligent transportation professionals with both professional modeling capabilities and AI application literacy. The research constructs a three-in-one teaching reform framework of curriculum system reconstruction, teaching mode innovation, and homework system optimization. With 64 undergraduate students from the transportation engineering major of Inner Mongolia Agricultural University in the 2024 grade as the practical subjects, the research adopts the Likert five-point scale and structural equation model for systematic empirical verification. The results show that the overall satisfaction of the course students reaches 98.44%, and the favorable rates of each core evaluation dimension exceed 93%. The reliability and validity of the assessment scale are excellent, and the reform has significantly improved the students’ core transportation modeling ability and comprehensive AI application literacy. This research can provide practical references for the digital teaching reform of intelligent transportation and provide scientific empirical evidence for the cultivation of new engineering composite talents.