人工智能伦理教育与技术实践的深度融合已成为新工科建设的重要议题。针对传统机器学习实验教学“重技能轻价值”的困境,研究构建“知识–能力–价值”三维目标体系,创新提出“技术解构–社会关联–批判实践”的递进路径。通过双轨案例设计,将数据偏见修正、算法可解释性等思政要素转化为可编程的模型约束条件,结合仿真情境、跨学科协作及开源伦理实践,形成“技术规范与价值判断”协同的教学范式。研究揭示了理工科课程思政的核心机制,即依托技术实践的价值负载特征,在模型调试中实现“伦理认知–价值内化”的转化。为实现教育创新制度化,建议构建校企联动的动态案例库、开发嵌入式伦理评估工具包,并建立涵盖技术性能与伦理敏感度的双重评价体系,推动思政教育从课程改革向系统建构转型,培养兼具技术理性与价值判断力的新型AI人才。The deep integration of AI ethics education with technical practices has become a critical topic in the development of emerging engineering education. Addressing the challenge that traditional machine learning experimental teaching tends to emphasize skills over values, this study constructs a three-dimensional objective system encompassing knowledge, capability, and values. It innovatively proposes a progressive approach through “technical deconstruction-social association-critical practice”. By designing dual-track cases, it transforms ideological and political elements, such as bias correction in data and algorithm explainability, into programmable model constraints. Combining simulated scenarios, interdisciplinary collaboration, and open-source ethical practices, it forms a teaching paradigm where “technical norms and value judgments” are synergized. The research elucidates the core mechanism of ideological and political work in science and technology courses, highlighting the value-laden characteristics of technical practices to achieve the transformation from “ethical awareness to value internalization” during model tuning. To institutionalize educational innovation, it recommends establishing dynamic case libraries involving school-enterprise interaction, developing embedded ethical assessment toolkits, and creating a dual evaluation system covering both technical performance and ethical sensitivity, thereby fostering the transition from course reform to systematic construction in ideological and political education, and nurturing new types of AI talents who possess both technological rationality and value judgment abili
马克思主义理论研究和建设工程(“马工程”)重点教材是新时代马克思主义理论学科建设和课程建设的重要成果,是强化高等院校思想引领与文化阵地建设的重要抓手。地方商科院校“马工程”课程实验教学存在实验教学内容体系不完善、实验教学师资队伍不充足、实验教学平台资源不丰富、实验教学评价体系不科学等问题。基于数据驱动,从实验教学内容、师资队伍、实验平台、评价体系四个方面探索“马工程”课程实验教学路径,旨在提升“马工程”课程实验教学的实效性。The key textbooks of the Project to Study and Develop Marxist Theory (“Marxist Project”) are significant achievements in the development of Marxist theory disciplines and curriculum construction for the new era, serving as a crucial tool to reinforce ideological leadership and cultural front construction in higher education institutions. There are some problems in the experimental teaching of “Marxist Project” course in local business colleges, such as the imperfect experimental teaching content system, the insufficient experimental teaching staff, the insufficient experimental teaching platform resources, and the unscientific experimental teaching evaluation system. Based on data-driven, this paper explores the experimental teaching path of “Marxist Project” course from four aspects of experimental teaching content, teaching staff, experimental platform and evaluation system, aiming to improve the effectiveness of experimental teaching of “Marxist Project” courses.
文章针对地方商科院校非计算机类专业学生的AI素养培养议题,开展了深入研究。剖析了新工科视角下地方商科院校非计算机类专业学生AI素养培养的理论支撑与层次划分。同时,通过剖析当前地方商科院校非计算机类专业学生AI素养教育环境的现状与行业需求,探讨了涵盖课程体系优化、实践教学模式革新、评价体系完善的培养方案的构建。研究为地方商科院校在学生AI素养培养方面提供了可行理论支撑,对于提升商科学生的AI素养及创新能力有着积极的影响,并可为相关教育实践提供参考与启示。This paper conducts an in-depth study on the cultivation of AI literacy among non-computer science students at local business colleges. It analyzes the theoretical foundations and hierarchical classifications of AI literacy development from the perspective of the New Engineering Education framework. Furthermore, by examining the current state of AI literacy education environments and industry demands for non-computer science students at local business colleges, this research explores the construction of a comprehensive training program that encompasses curriculum optimization, innovation in practical teaching models, and improvements in evaluation systems. The findings of this study provide a feasible theoretical foundation for local business colleges aiming to enhance students’ AI literacy and innovative capabilities, offering valuable insights and references for relevant educational practices.
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