This paper focuses on how to use AI big model to improve the teaching quality and learning effect of Python programming course. Starting from the foundation of AI big model and the current situation of Python programming courses, it provides new ideas for the teaching reform of programming courses in colleges and universities by exploring the innovative practice of AI big model in all aspects of course teaching (syntactic knowledge, application scenarios and practical aspects), including the auxiliary knowledge lectures, the project-driven as the core, and the real-time feedback evaluation system, in order to realize the optimization of programming practice and stimulate the creativity of students. and practice reference. At the same time, the challenges brought by the big model to the auxiliary teaching are viewed with dialectical thinking, and reasonable scientific suggestions are given to cultivate compound innovative talents with both profound technical skills and diversified knowledge reserves.
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。一方面，AI 大模型可为课程教学赋能，提供个性化学习支持、智能辅导答疑、丰富的案例资源，革新教学模式，激发学生学习兴趣，提升教学质量；另一方面，Python 课程作为编程教育的重要载体，为 AI 大模型的实践应用提供平台，助力学生深入理解 AI 技术原理，培养其运用 AI 解决实际问题的能力，为社会输送兼具 Python 编程技能与 AI 素养的复合型人才。本文聚焦此融合，旨在探索创新教学路径，为教育现代化提供新思路。
2．AI大模型与Python程序设计课程概述
2.1．AI大模型基础
AI 大模型，作为人工智能领域的前沿成果，是指运用海量数据与超强算力，经复杂架构训练而成的具备巨量参数的模型
为契合时代发展需求，将 AI 大模型相关知识系统融入 Python 程序设计课程至关重要。在理论教学板块，专门开辟章节阐释 AI 大模型基础原理，涵盖神经网络架构（如 Transformer）、训练机制（反向传播、梯度下降优化）、参数规模效应等核心要点，结合可视化图表、动画演示，助力学生洞悉模型内部运作逻辑。以 GPT 系列为例，剖析其从海量文本数据学习语言模式，进而实现智能文本生成的过程，让学生理解大模型强大语言处理能力根源
。同时聚焦大模型与 Python 协同应用，像运用 Python 调用大模型 API 实现智能客服功能，从构建请求、处理响应到优化交互流程，全方位展示实操细节，使学生明晰二者结合的巨大潜力。
实践环节，设计专项实践项目，如“基于大模型的文本情感分析系统开发”，学生运用 Python 的数据处理、模型调用库（如 TensorFlow、Hugging Face Transformers 结合 Python），完成数据预处理、模型接入、结果可视化，亲身体验大模型赋能下 Python 解决复杂问题的魅力，提升实践技能与创新思维。
3.2．教学模式创新
3.2.1．智能辅助教学
AI 大模型在 Python 课程教学中可全方位、多阶段赋能，革新教学模式。备课环节，教师借助大模型强大信息整合能力，输入课程主题如“Python 数据分析实战”，如图1所示。大语言模型“豆包”不光提供了实例的解决思路和相关代码，还对问题进行了拓展和延伸，如图2所示。模型瞬间汇聚海量优质资源，包括前沿案例、经典算法解析、行业应用数据，依教学目标筛选重组，自动生成结构清晰、内容丰富的课件，融入新颖实例，如电商平台实时销售数据分析，使备课高效精准，知识储备与时俱进。
AI 大模型融入 Python 课程，对教师与学生的教育观念转变提出挑战。教师长期习惯传统教学主导模式，部分教师对新技术心存顾虑，担忧被大模型取代，缺乏主动学习运用的动力；且在教学实践中，如何有效结合大模型设计教学环节、引导学生合理使用、把控教学节奏，均需教师重构教学理念与方法，这一过程漫长且艰难，若教师培训与实践指导跟不上，易导致新技术形式大于内容，无法落地生根。
教育理念转变与师资建设是 AI 大模型赋能 Python 课程的关键支撑。一方面，学校应积极组织教师参与 AI 大模型应用培训，涵盖技术原理、教学融合案例、实操演练等模块，邀请专家讲学、企业工程师实操指导，提升教师技术驾驭能力；开展教学研讨活动，以“AI 大模型下 Python 课程设计与引导”为主题，交流经验、碰撞思维，探索最佳实践路径，促使教师从传统知识传授者向智能学习引导者蜕变。
同时，着力引导学生树立正确学习观念。通过入学教育、课程导论等环节，阐释 AI 大模型作为学习工具的本质，培养学生批判性思维，使其明辨模型输出优劣；教师在课堂教学中，设计对比练习，如让学生自主分析大模型生成代码与手动编写代码差异，强化自主探索意识，规避过度依赖，让学生成为学习主人，在 AI 助力下实现 Python 编程素养进阶。
6．结论与展望
6.1．研究总结
本研究深入探究 AI 大模型赋能 Python 程序设计课程，剖析其理论根基、实践路径、成效与挑战，得出诸多关键结论。理论上，建构主义与个性化学习理论为融合奠基，AI 大模型助力学生在真实情境自主构建知识，依个体差异定制学习路径，激发学习主动性与潜能
Wu, X., Wu, Z., Li, Z. (2025). Innovative Practice and Transformation of Python Programming Course Empowered by AI Large Models. Science Innovation, 13(2), 11-15. https://doi.org/10.11648/j.si.20251302.11
Wu, X.; Wu, Z.; Li, Z. Innovative Practice and Transformation of Python Programming Course Empowered by AI Large Models. Sci. Innov.2025, 13(2), 11-15. doi: 10.11648/j.si.20251302.11
Wu X, Wu Z, Li Z. Innovative Practice and Transformation of Python Programming Course Empowered by AI Large Models. Sci Innov. 2025;13(2):11-15. doi: 10.11648/j.si.20251302.11
@article{10.11648/j.si.20251302.11,
author = {Xiaoxuan Wu and Zhize Wu and Zhengmao Li},
title = {Innovative Practice and Transformation of Python Programming Course Empowered by AI Large Models
},
journal = {Science Innovation},
volume = {13},
number = {2},
pages = {11-15},
doi = {10.11648/j.si.20251302.11},
url = {https://doi.org/10.11648/j.si.20251302.11},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.si.20251302.11},
abstract = {This paper focuses on how to use AI big model to improve the teaching quality and learning effect of Python programming course. Starting from the foundation of AI big model and the current situation of Python programming courses, it provides new ideas for the teaching reform of programming courses in colleges and universities by exploring the innovative practice of AI big model in all aspects of course teaching (syntactic knowledge, application scenarios and practical aspects), including the auxiliary knowledge lectures, the project-driven as the core, and the real-time feedback evaluation system, in order to realize the optimization of programming practice and stimulate the creativity of students. and practice reference. At the same time, the challenges brought by the big model to the auxiliary teaching are viewed with dialectical thinking, and reasonable scientific suggestions are given to cultivate compound innovative talents with both profound technical skills and diversified knowledge reserves.
},
year = {2025}
}
TY - JOUR
T1 - Innovative Practice and Transformation of Python Programming Course Empowered by AI Large Models
AU - Xiaoxuan Wu
AU - Zhize Wu
AU - Zhengmao Li
Y1 - 2025/04/14
PY - 2025
N1 - https://doi.org/10.11648/j.si.20251302.11
DO - 10.11648/j.si.20251302.11
T2 - Science Innovation
JF - Science Innovation
JO - Science Innovation
SP - 11
EP - 15
PB - Science Publishing Group
SN - 2328-787X
UR - https://doi.org/10.11648/j.si.20251302.11
AB - This paper focuses on how to use AI big model to improve the teaching quality and learning effect of Python programming course. Starting from the foundation of AI big model and the current situation of Python programming courses, it provides new ideas for the teaching reform of programming courses in colleges and universities by exploring the innovative practice of AI big model in all aspects of course teaching (syntactic knowledge, application scenarios and practical aspects), including the auxiliary knowledge lectures, the project-driven as the core, and the real-time feedback evaluation system, in order to realize the optimization of programming practice and stimulate the creativity of students. and practice reference. At the same time, the challenges brought by the big model to the auxiliary teaching are viewed with dialectical thinking, and reasonable scientific suggestions are given to cultivate compound innovative talents with both profound technical skills and diversified knowledge reserves.
VL - 13
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ER -
Wu, X., Wu, Z., Li, Z. (2025). Innovative Practice and Transformation of Python Programming Course Empowered by AI Large Models. Science Innovation, 13(2), 11-15. https://doi.org/10.11648/j.si.20251302.11
Wu, X.; Wu, Z.; Li, Z. Innovative Practice and Transformation of Python Programming Course Empowered by AI Large Models. Sci. Innov.2025, 13(2), 11-15. doi: 10.11648/j.si.20251302.11
Wu X, Wu Z, Li Z. Innovative Practice and Transformation of Python Programming Course Empowered by AI Large Models. Sci Innov. 2025;13(2):11-15. doi: 10.11648/j.si.20251302.11
@article{10.11648/j.si.20251302.11,
author = {Xiaoxuan Wu and Zhize Wu and Zhengmao Li},
title = {Innovative Practice and Transformation of Python Programming Course Empowered by AI Large Models
},
journal = {Science Innovation},
volume = {13},
number = {2},
pages = {11-15},
doi = {10.11648/j.si.20251302.11},
url = {https://doi.org/10.11648/j.si.20251302.11},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.si.20251302.11},
abstract = {This paper focuses on how to use AI big model to improve the teaching quality and learning effect of Python programming course. Starting from the foundation of AI big model and the current situation of Python programming courses, it provides new ideas for the teaching reform of programming courses in colleges and universities by exploring the innovative practice of AI big model in all aspects of course teaching (syntactic knowledge, application scenarios and practical aspects), including the auxiliary knowledge lectures, the project-driven as the core, and the real-time feedback evaluation system, in order to realize the optimization of programming practice and stimulate the creativity of students. and practice reference. At the same time, the challenges brought by the big model to the auxiliary teaching are viewed with dialectical thinking, and reasonable scientific suggestions are given to cultivate compound innovative talents with both profound technical skills and diversified knowledge reserves.
},
year = {2025}
}
TY - JOUR
T1 - Innovative Practice and Transformation of Python Programming Course Empowered by AI Large Models
AU - Xiaoxuan Wu
AU - Zhize Wu
AU - Zhengmao Li
Y1 - 2025/04/14
PY - 2025
N1 - https://doi.org/10.11648/j.si.20251302.11
DO - 10.11648/j.si.20251302.11
T2 - Science Innovation
JF - Science Innovation
JO - Science Innovation
SP - 11
EP - 15
PB - Science Publishing Group
SN - 2328-787X
UR - https://doi.org/10.11648/j.si.20251302.11
AB - This paper focuses on how to use AI big model to improve the teaching quality and learning effect of Python programming course. Starting from the foundation of AI big model and the current situation of Python programming courses, it provides new ideas for the teaching reform of programming courses in colleges and universities by exploring the innovative practice of AI big model in all aspects of course teaching (syntactic knowledge, application scenarios and practical aspects), including the auxiliary knowledge lectures, the project-driven as the core, and the real-time feedback evaluation system, in order to realize the optimization of programming practice and stimulate the creativity of students. and practice reference. At the same time, the challenges brought by the big model to the auxiliary teaching are viewed with dialectical thinking, and reasonable scientific suggestions are given to cultivate compound innovative talents with both profound technical skills and diversified knowledge reserves.
VL - 13
IS - 2
ER -
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