<?xml version="1.1" encoding="utf-8"?>
<article xsi:noNamespaceSchemaLocation="http://jats.nlm.nih.gov/publishing/1.1/xsd/JATS-journalpublishing1-mathml3.xsd" dtd-version="1.1" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"><front><journal-meta><journal-id journal-id-type="publisher-id">SDME</journal-id><journal-title-group><journal-title>Scientific Development of Modern Education</journal-title></journal-title-group><issn>2998-9027</issn><eissn>2998-9043</eissn><publisher><publisher-name>Art and Technology</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.61369/SDME.2025270034</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title>人工智能在 MATLAB 程序开发中的应用探索及其教学启示</title><url>https://artdesignp.com/journal/SDME/2/27/10.61369/SDME.2025270034</url><author>刘剑,桂勇,徐少锋</author><pub-date pub-type="publication-year"><year>2025</year></pub-date><volume>2</volume><issue>27</issue><history><date date-type="pub"><published-time>2025-11-14</published-time></date></history><abstract>基于MATLAB 的矿床地质分析软件开发实践，探索人工智能（AI）在工程软件开发中的应用路径。通过实例梳理AI 参与需求分析与程序框架设计、核心函数与算法实现、调试重构与可视化以及文档与注释生成等典型环节，总结其在提升开发效率、优化程序结构、降低复杂功能开发门槛方面的优势，同时指出在工程语境理解、边界条件处理和复杂逻辑维护等方面的局限，提出&amp;ldquo;AI 写，人审，人实测&amp;rdquo;的基本使用原则。在此基础上，从教学目标、项目化任务设计与评价方式三个方面提出将AI 合理融入MATLAB 程序开发教学的设想，为后续课程改革和师生理性使用AI 工具有一定参考。</abstract><keywords>人工智能,MATLAB,程序开发,教学启示</keywords></article-meta></front><body/><back><ref-list><ref id="B1" content-type="article"><label>1</label><element-citation publication-type="journal"><p>[1] 李治艳. MATLAB 基础及应用课程教学改革探究[J]. 安徽工业大学学报( 社会科学版), 2024, 41(2):52&amp;ndash;55.[2] 裴惠琴, 赖强. 面向创新能力培养的 Matlab 与控制系统仿真课程教学改革探究[J]. 教育教学论坛, 2018(39):105&amp;ndash;106.[3] 贺跃帮, 王天雷, 李兴春, 等. 基于案例的 MATLAB 教学探讨与实践[J]. 科技创新导报, 2017, 14(13):226&amp;ndash;228.[4] 杨炼, 陈芳, 谭理. MATLAB 多项式数据拟合的案例式教学设计[J]. 教育现代化, 2019(9):97&amp;ndash;99.[5] Tan X, Fu F, Xing W, et al. Artificial Intelligence in Teaching and Teacher Professional Development: A Systematic Review[J]. Contemporary Educational Technology, 2024.[6] Jacobs M, Stein N, Guo P, et al. Evaluating the Application of Large Language Models to Introductory Programming Education[C]//Proceedings of the 55th ACM Technical Symposium on Computer Science Education (SIGCSE '24). New York: ACM, 2024.[7] MathWorks. Engaging Students in Project-Based Learning with MATLAB Mobile and ThingSpeak[EB/OL]. Natick, MA: MathWorks, 2022.</p><pub-id pub-id-type="doi"/></element-citation></ref></ref-list></back></article>
