融合AI与产学研的QMAD高阶教学框架研究——以《最优化方法》课程为例

Research on a High-Level QMAD Teaching Framework Integrating AI and Industry-Academia Collaboration—A Case Study of the “Optimization Methods” Course

摘要: 为应对人工智能时代对新工科研究生创新能力培养的迫切需求，本文针对《最优化方法》研究生课程长期存在的理论与前沿脱节、产学研协同薄弱、高阶科研与工程能力培养不足等核心问题，依托教育部“基于人工智能的最优化方法教学改革研究”产学研项目，系统构建并实践了深度AI驱动与产学研融合的QMAD (Question-Model-Algorithm-Decision)高阶教学框架。本研究对经典QMAD方法进行了面向研究生教育与新工科内涵的升级适配，将其拓展为“AI前沿与产业双源问题驱动(Q)–高维动态与约束精准建模(M)–智能优化算法创新与白盒实现(A)–科研价值与产业效能双轨决策验证(D)”的闭环教学范式。通过校企联合开发前沿案例库、实施学术与产业双导师协同指导、深度嵌入AI开发与调优工具链、以及推行以研究生为主体的探究式专题授课等多元化路径，该框架有力促进了学生“理论深度、AI技术融合度、产学研协同紧密度、自主科研创新力”的四维综合提升。教学实践表明，改革有效强化了研究生面向复杂AI场景的高阶优化建模能力、算法再造能力及跨学科协同解决能力，为其未来从事前沿科研或高端产业研发奠定了坚实的思维与实践基础，为新工科背景下研究生核心课程的教学改革提供了可借鉴的融合创新模式。

Abstract: To address the urgent need for cultivating innovative capabilities of graduate students in emerging engineering disciplines in the era of artificial intelligence (AI), this paper focuses on the long-standing core issues in the graduate course—Optimization Methods, including the disconnection between theory and cutting-edge advancements, weak industry-academia collaboration, and insufficient training in high-level scientific research and engineering competencies. Supported by the Ministry of Education’s industry-academia collaborative project—Research on Teaching Reform of Optimization Methods Based on Artificial Intelligence, this study systematically constructs and implements a high-level QMAD (Question-Model-Algorithm-Decision) teaching framework deeply driven by AI and integrated with industry-academia collaboration. The classic QMAD approach is upgraded and adapted to align with the demands of graduate education and the essence of emerging engineering disciplines, extending it into a closed-loop teaching paradigm: Dual-source problem-driven by AI frontiers and industry (Q), High-dimensional dynamic and constraint-accurate modeling (M), Innovative intelligent optimization algorithms and white-box implementation (A), Dual-track decision verification for scientific research value and industrial effectiveness (D). Through multiple pathways, such as jointly developing cutting-edge case libraries with enterprises, implementing dual mentorship by academic and industry advisors, deeply embedding AI development and tuning toolchains, and promoting inquiry-based thematic teaching led by graduate students, this framework significantly enhances students’ comprehensive development in four dimensions: theoretical depth, integration of AI technologies, closeness of industry-academia collaboration, and independent research innovation capability. Teaching practice demonstrates that the reform effectively strengthens graduate students’ advanced optimization modeling capabilities for complex AI scenarios, algorithm re-engineering abilities, and interdisciplinary collaborative problem-solving skills. It lays a solid foundation for their future engagement in cutting-edge research or high-end industrial research and development, offering a referential integrated innovation model for teaching reform in core graduate courses under the context of emerging engineering education.