AI赋能研究生《随机过程》课程建设探索

Exploration of AI-Empowered Course Construction for the Graduate Stochastic Processes Course

摘要: 人工智能技术的快速发展对研究生教育提出了新的挑战与要求。针对传统《随机过程》课程存在的教学内容滞后于AI发展、与民航行业需求脱节、学生实践创新能力不足等突出问题，本文立足民航强国建设需求，依托本校民航特色办学优势，面向交通运输工程、控制科学与工程等专业人才培养目标，提出并实施了一套系统性的课程改革方案。该方案以“知识筑基–能力赋能–素养提升”三位一体为核心理念，从课程体系重构、教学理念革新、创新能力培养、思政劳动教育融入、考核评价体系优化五个维度展开具体建设。在课程体系方面，重构了“数理基础 + AI建模 + 民航场景”的教学框架，引入随机分析与深度学习、马尔可夫链与强化学习等AI拓展内容，并以航班到达流、航空设备退化等民航真实问题贯穿教学全过程。在教学模式方面，采用AI驱动的混合式教学与翻转课堂，引入智能答疑、知识图谱等AI辅助工具。在能力培养方面，通过民航特色案例库、数智化项目实践、科研反哺教学等举措，强化学生的建模与编程实践能力。同时，将思政教育与劳动实践深度融入教学，构建了“知识 + 能力 + 素养”三维考核评价体系。实践表明，该改革有效提升了课程的先进性、实践性与育人成效，为培养具备民航特色的数理建模与AI应用复合型人才提供了可行路径。

Abstract: The rapid development of artificial intelligence technology has brought new challenges and requirements to graduate education. In response to the prominent problems existing in the traditional Stochastic Processes course, such as the lag of teaching content behind AI development, the disconnection from the needs of the civil aviation industry, and the insufficiency of students’ practical and innovative abilities, this paper, based on the national strategy of building a strong civil aviation country, leverages the university’s distinctive advantages in civil aviation education, and targets the talent cultivation goals of majors such as Transportation Engineering and Control Science and Engineering, proposes and implements a systematic curriculum reform scheme. The scheme is centered on the trinity concept of “Knowledge Foundation-Competence Empowerment-Character Cultivation”, and is implemented through five specific dimensions: curriculum system reconstruction, teaching concept innovation, innovation ability cultivation, integration of ideological and political education with labor practice, and optimization of the assessment and evaluation system. In terms of the curriculum system, the teaching framework of “Mathematical Foundation + AI Modeling + Civil Aviation Scenarios” is reconstructed, with the introduction of AI extension topics such as stochastic analysis and deep learning, Markov chains and reinforcement learning, and the integration of real-world civil aviation problems such as flight arrival flows and aviation equipment degradation throughout the entire teaching process. In terms of the teaching model, AI-driven blended learning and flipped classrooms are adopted, and AI-assisted tools such as intelligent Q&A systems and knowledge graphs are introduced. In terms of competence cultivation, measures such as a civil aviation-specific case library, digital and intelligent project practice, and research feeding back into teaching are implemented to strengthen students’ modeling and programming practical abilities. Meanwhile, ideological and political education and labor practice are deeply integrated into the teaching process, and a three-dimensional assessment and evaluation system of “Knowledge + Competence + Character” is constructed. Practice has shown that this reform has effectively enhanced the advanced nature, practicality, and educational effectiveness of the course, providing a feasible path for cultivating interdisciplinary talents with civil aviation characteristics who are proficient in mathematical modeling and AI applications.