<?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">RTED</journal-id><journal-title-group><journal-title>Research on Teacher Education and Development</journal-title></journal-title-group><issn>3066-8999</issn><eissn>3066-9006</eissn><publisher><publisher-name>Art and Technology</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.61369/RTED.2026170040</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title>生成式人工智能驱动高校计算机项目制教学重构：机制、路径与边界</title><url>https://artdesignp.com/journal/RTED/2/17/10.61369/RTED.2026170040</url><author>陈静</author><pub-date pub-type="publication-year"><year>2026</year></pub-date><volume>2</volume><issue>17</issue><history><date date-type="pub"><published-time>2026-04-24</published-time></date></history><abstract>生成式人工智能已进入高校计算机课程的需求分析、程序设计等环节，在提高任务完成效率的同时，也削弱了最终成果与学生实际能力之间的对应关系。本文从项目制教学出发，分析生成式人工智能的影响，指出当前教学中存在任务外包、生成结果同质化、学生素养差异以及评价规则滞后等问题。在此基础上，提出设计分层项目任务，建立&amp;ldquo;独立&amp;mdash;协同&amp;mdash;验证&amp;mdash;迁移&amp;rdquo;教学流程，构建多源互证的过程证据链，并通过多元评价和分级治理明确工具使用边界。</abstract><keywords>生成式人工智能,计算机教育,项目制教学,人机协同,过程评价</keywords></article-meta></front><body/><back><ref-list><ref id="B1" content-type="article"><label>1</label><element-citation publication-type="journal"><p>[1] 李艳 , 许洁 , 贾程媛 , 等 . 大学生生成式人工智能应用现状与思考&amp;mdash;&amp;mdash;基于浙江大学的调查 [J]. 开放教育研究 , 2024, 30(1): 89-98.
[2] 秦渝超 , 刘革平 , 许颖 . 生成式人工智能如何重塑教学活动&amp;mdash;&amp;mdash;基于活动理论的模型构建与应用 [J]. 中国远程教育 , 2023, 43(12): 34-45.
[3] 祝智庭 , 戴岭 , 胡姣 . 高意识生成式学习：AIGC 技术赋能的学习范式创新 [J]. 电化教育研究 , 2023, 44(6): 5-14.
[4]FINNIE-ANSLEY J, DENNY P, BECKER B A, et al. The Robots Are Coming: Exploring the Implications of OpenAI Codex on Introductory Programming[C]//Proceedings of the Australasian Computing Education Conference. New York: ACM, 2022: 10-19.
[5]BECKER B A, DENNY P, FINNIE-ANSLEY J, et al. Programming Is Hard&amp;mdash;Or at Least It Used to Be: Educational Opportunities and Challenges of AI Code Generation[C]// Proceedings of the 54th ACM Technical Symposium on Computer Science Education V. 1. New York: ACM, 2023: 500-506.
[6]DENNY P, KUMAR V, GIACAMAN N. Conversing with Copilot: Exploring Prompt Engineering for Solving CS1 Problems Using Natural Language[C]//Proceedings of the 54th ACM Technical Symposium on Computer Science Education V. 1. New York: ACM, 2023: 1136-1142.
[7]QADIR J. Engineering Education in the Era of ChatGPT: Promise and Pitfalls of Generative AI for Education[C]//2023 IEEE Global Engineering Education Conference (EDUCON). Piscataway: IEEE, 2023: 1-9.
[8]MIAO F, HOLMES W. Guidance for Generative AI in Education and Research[R]. Paris: UNESCO, 2023.
[9]KASNECI E, SESSLER K, KUCHEMANN S, et al. ChatGPT for Good? On Opportunities and Challenges of Large Language Models for Education[J]. Learning and Individual Differences, 2023, 103: 102274.
[10] 余南平 , 张翌然 . ChatGPT/ 生成式人工智能对教育的影响：大国博弈新边疆 [J]. 华东师范大学学报 ( 教育科学版 ), 2023, 41(7): 15-25.
[11] 祝智庭 , 戴岭 . 融合创新：数智技术赋能高等教育的新质发展 [J]. 开放教育研究 , 2024, 30(3): 4-14.
[12] 严奕峰 , 丁杰 , 高赢 , 等 . 生成式人工智能赋能数字时代育人转型 [J]. 开放教育研究 , 2024, 30(2): 42-48.</p><pub-id pub-id-type="doi"/></element-citation></ref></ref-list></back></article>
